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extacy
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Can Lifestyle Changes Save Lives in Colon Cancer?
Can Lifestyle Changes Save Lives in Colon Cancer?
Can exercise “therapy” and diet improve survival in patients with colon cancer? It appears so, according to two pivotal studies presented at American Society of Clinical Oncology (ASCO) 2025 annual meeting.
In the CHALLENGE trial, a structured exercise program after surgery and adjuvant chemotherapy cut the risk for colon cancer recurrence in patients with stage III and high-risk stage II disease by more than one quarter and the risk for death by more than one third.
“The magnitude of benefit with exercise is substantial. In fact, it is comparable, and in some cases exceeds the magnitude of benefit of many of our very good standard medical therapies in oncology,” study presenter Christopher Booth, MD, with Queen’s University, Kingston, Ontario, Canada, told attendees.
Results of the study were published online in The New England Journal of Medicine to coincide with the presentation at the meeting.
The findings are “nothing short of a major milestone,” said study discussant Peter Campbell, PhD, with Montefiore Einstein Comprehensive Cancer Center, Bronx, New York.
The other study showed that eating a less inflammatory diet may reduce the risk for death in patients with colon cancer, with the greatest benefits seen in those who embraced anti-inflammatory foods and exercised regularly.
“Putting these two abstracts into perspective, we as physicians need to be essentially prescribing healthy diet and exercise. The combination of the two are synergistic,” Julie Gralow, MD, ASCO chief medical officer and executive vice president, told attendees.
Despite the benefits of these lifestyle changes, exercise and diet are meant to supplement, not replace, established colon cancer treatments.
It would be a false binary to frame this as lifestyle vs cancer treatment, explained Mark Lewis, MD, director of Gastrointestinal Oncology at Intermountain Healthcare in Salt Lake City, Utah. With exercise, for instance, “the key is giving enough chemo to protect against recurrence and eliminate micrometastases but not so much that we cause neuropathy and reduce function and ability to follow the CHALLENGE structured program,” Lewis said.
Exercise and Survival
Colon cancer remains the second-leading cause of cancer death worldwide. Even with surgery and chemotherapy, roughly 30% of patients with stage III and high-risk stage II colon cancer will experience disease recurrence.
“As oncologists, one of the most common questions we get asked by patients is — what else can I do to improve my outcome?” Booth said.
Observational studies published nearly two decades ago hinted that physically active cancer survivors fare better, but no randomized trial has definitively tested whether exercise could alter disease course. That knowledge gap prompted the Canadian Cancer Trials Group to launch the CHALLENGE trial.
Between 2009 and 2023, the phase 3 study enrolled 889 adults (median age, 61 years; 51% women) who had completed surgery and adjuvant chemotherapy for stage III (90%) or high-risk stage II (10%) colon cancer. Most patients were from Canada and Australia and were enrolled 2-6 months after completing chemotherapy.
Half of study participants were randomly allocated to a structured exercise program (n = 445) and half to receive standard health education materials promoting physical activity and healthy eating (control individuals, n = 444).
As part of the structured exercise intervention, patients met with a physical activity consultant twice a month for the first 6 months. These sessions included exercise coaching and supervised exercise. Patients could choose their preferred aerobic exercise and most picked brisk walking.
The consultants gave each patient an “exercise prescription” to hit a specific amount of exercise. The target was an additional 10 metabolic equivalent (MET)–hours of aerobic activity per week — about three to four brisk walks each lasting 45-60 minutes. After 6 months, patients met with their consultants once a month, with additional sessions available for extra support if needed.
Structured exercise led to “substantial and sustained” increases in the amount of exercise participants did, as well as physiologic measures of their fitness, with “highly relevant” improvements in VO2 max, 6-minute walk test, and patient-reported physical function, underscoring that participants were not only exercising more but also getting fitter, Booth said.
Exercise was associated with a clinically meaningful and statistically significant 28% reduction in the risk for recurrent or new cancer (hazard ratio [HR], 0.72; P = .017), with a 5-year disease free survival rate of 80% in the exercise group and 74% in the control group.
In other words, “for every 16 patients that went on the exercise program, exercise prevented 1 person from recurrent or new cancer” at 5 years, Booth reported.
Overall survival results were “even more impressive,” he said.
At 8 years, 90% of patients in the exercise program were alive vs 83% of those in the control group, which translated to a 37% lower risk for death (HR, 0.63; P = .022).
“For every 14 patients who went on the exercise program, exercise prevented 1 person from dying” at the 8-year mark, Booth noted.
“Notably, this difference in survival was not driven by difference in cardiovascular deaths but by a reduction in the risk of death from colon cancer,” he said.
Besides a slight uptick in musculoskeletal aches, no major safety signals emerged in the exercise group.
It’s important to note that the survival benefit associated with exercise came after patients had received surgery followed by chemotherapy — in other words, exercise did not replace established cancer treatments. It’s also unclear whether initiating an exercise intervention earlier in the treatment trajectory — before surgery or during chemotherapy, instead of after chemotherapy — could further improve cancer outcomes, the authors noted.
Still, “exercise as an intervention is a no brainer and should be implemented broadly,” said ASCO expert Pamela Kunz, MD, with Yale School of Medicine, New Haven, Connecticut.
Marco Gerlinger, MD, with Barts Cancer Institute, London, England, agreed.
“Oncologists can now make a very clear evidence-based recommendation for patients who just completed their chemotherapy for bowel cancer and are fit enough for such an exercise program,” Gerlinger said in a statement from the nonprofit UK Science Media Centre.
Booth noted that knowledge alone will not be sufficient to allow most patients to change their lifestyle and realize the health benefits.
“The policy implementation piece of this is really key, and we need health systems, hospitals, and payers to invest in these behavior support programs so that patients have access to a physical activity consultant and can realize the health benefits,” he said.
“This intervention is empowering and achievable for patients and with much, much lower cost than many of our therapies. It is also sustainable for health systems,” he concluded.
Diet and Survival
Diet can also affect outcomes in patients with colon cancer.
In the same session describing the CHALLENGE results, Sara Char, MD, with Dana-Farber Cancer Institute in Boston, reported findings showing that consuming a diet high in proinflammatory foods was associated with worse overall survival in patients with stage III colon cancer. A proinflammatory diet includes red and processed meats, sugary drinks, and refined grains, while an anti-inflammatory diet focuses on fruits, vegetables, whole grains, fish, and olive oil.
Chronic systemic inflammation has been implicated in both colon cancer development and in its progression, and elevated levels of inflammatory markers in the blood have previously been associated with worse survival outcomes in patients with stage III colon cancer.
Char and colleagues analyzed dietary patterns of a subset of 1625 patients (mean age, 61 years) with resected stage III colon cancer enrolled in the phase 3 CALGB/SWOG 80702 (Alliance) clinical trial, which compared 3 months of adjuvant chemotherapy with 6 months of adjuvant chemotherapy, with or without the anti-inflammatory medication celecoxib.
As part of the trial, participants reported their diet and exercise habits at various timepoints. Their diets were scored using the validated empirical dietary inflammatory pattern (EDIP) tool, which is a weighted sum of 18 food groups — nine proinflammatory and nine anti-inflammatory. A high EDIP score marks a proinflammatory diet, and a low EDIP score indicates a less inflammatory diet.
During median follow-up of nearly 4 years, researchers noted a trend toward worse disease-free survival in patients with high proinflammatory diets (HR, 1.46), but this association was not significant in the multivariable adjusted model (HR, 1.36; P = .22), Char reported.
However, higher intake of proinflammatory foods was associated with significantly worse overall survival.
Patients who consumed the most proinflammatory foods (top 20%) had an 87% higher risk for death compared with those who consumed the least (bottom 20%; HR, 1.87). The median overall survival in the highest quintile was 7.7 years and was not reached in the lowest quintile.
Combine Exercise and Diet for Best Results
To examine the joint effect of physical activity and diet on overall survival, patients were divided into higher and lower levels of physical activity using a cut-off of 9 MET hours per week, which roughly correlates to 30 minutes of vigorous walking five days a week with a little bit of light yoga, Char explained.
In this analysis, patients with less proinflammatory diets and higher physical activity levels had the best overall survival outcomes, with a 63% lower risk for death compared with peers who consumed more pro-inflammatory diets and exercised less (HR, 0.37; P < .0001).
Daily celecoxib use and low-dose aspirin use (< 100 mg/d) did not affect the association between inflammatory diet and survival.
Char cautioned, that while the EDIP tool is useful to measure the inflammatory potential of a diet, “this is not a dietary recommendation, and we need further studies to be able to tailor our findings into dietary recommendations that can be provided to patients at the bedside.”
Gralow said this “early but promising observational study suggests a powerful synergy: Patients with stage III colon cancer who embraced anti-inflammatory foods and exercised regularly showed the best overall survival compared to those with inflammatory diets and limited exercise.”
The CHALLENGE trial was funded by the Canadian Cancer Society, the National Health and Medical Research Council, Cancer Research UK, and the University of Sydney Cancer Research Fund. Booth had no disclosures. The diet study was funded by the National Institutes of Health, Pfizer, and the Project P Fund. Char disclosed an advisory/consultant role with Goodpath. Kunz, Gralow and Campbell had no relevant disclosures.
A version of this article first appeared on Medscape.com.
Can exercise “therapy” and diet improve survival in patients with colon cancer? It appears so, according to two pivotal studies presented at American Society of Clinical Oncology (ASCO) 2025 annual meeting.
In the CHALLENGE trial, a structured exercise program after surgery and adjuvant chemotherapy cut the risk for colon cancer recurrence in patients with stage III and high-risk stage II disease by more than one quarter and the risk for death by more than one third.
“The magnitude of benefit with exercise is substantial. In fact, it is comparable, and in some cases exceeds the magnitude of benefit of many of our very good standard medical therapies in oncology,” study presenter Christopher Booth, MD, with Queen’s University, Kingston, Ontario, Canada, told attendees.
Results of the study were published online in The New England Journal of Medicine to coincide with the presentation at the meeting.
The findings are “nothing short of a major milestone,” said study discussant Peter Campbell, PhD, with Montefiore Einstein Comprehensive Cancer Center, Bronx, New York.
The other study showed that eating a less inflammatory diet may reduce the risk for death in patients with colon cancer, with the greatest benefits seen in those who embraced anti-inflammatory foods and exercised regularly.
“Putting these two abstracts into perspective, we as physicians need to be essentially prescribing healthy diet and exercise. The combination of the two are synergistic,” Julie Gralow, MD, ASCO chief medical officer and executive vice president, told attendees.
Despite the benefits of these lifestyle changes, exercise and diet are meant to supplement, not replace, established colon cancer treatments.
It would be a false binary to frame this as lifestyle vs cancer treatment, explained Mark Lewis, MD, director of Gastrointestinal Oncology at Intermountain Healthcare in Salt Lake City, Utah. With exercise, for instance, “the key is giving enough chemo to protect against recurrence and eliminate micrometastases but not so much that we cause neuropathy and reduce function and ability to follow the CHALLENGE structured program,” Lewis said.
Exercise and Survival
Colon cancer remains the second-leading cause of cancer death worldwide. Even with surgery and chemotherapy, roughly 30% of patients with stage III and high-risk stage II colon cancer will experience disease recurrence.
“As oncologists, one of the most common questions we get asked by patients is — what else can I do to improve my outcome?” Booth said.
Observational studies published nearly two decades ago hinted that physically active cancer survivors fare better, but no randomized trial has definitively tested whether exercise could alter disease course. That knowledge gap prompted the Canadian Cancer Trials Group to launch the CHALLENGE trial.
Between 2009 and 2023, the phase 3 study enrolled 889 adults (median age, 61 years; 51% women) who had completed surgery and adjuvant chemotherapy for stage III (90%) or high-risk stage II (10%) colon cancer. Most patients were from Canada and Australia and were enrolled 2-6 months after completing chemotherapy.
Half of study participants were randomly allocated to a structured exercise program (n = 445) and half to receive standard health education materials promoting physical activity and healthy eating (control individuals, n = 444).
As part of the structured exercise intervention, patients met with a physical activity consultant twice a month for the first 6 months. These sessions included exercise coaching and supervised exercise. Patients could choose their preferred aerobic exercise and most picked brisk walking.
The consultants gave each patient an “exercise prescription” to hit a specific amount of exercise. The target was an additional 10 metabolic equivalent (MET)–hours of aerobic activity per week — about three to four brisk walks each lasting 45-60 minutes. After 6 months, patients met with their consultants once a month, with additional sessions available for extra support if needed.
Structured exercise led to “substantial and sustained” increases in the amount of exercise participants did, as well as physiologic measures of their fitness, with “highly relevant” improvements in VO2 max, 6-minute walk test, and patient-reported physical function, underscoring that participants were not only exercising more but also getting fitter, Booth said.
Exercise was associated with a clinically meaningful and statistically significant 28% reduction in the risk for recurrent or new cancer (hazard ratio [HR], 0.72; P = .017), with a 5-year disease free survival rate of 80% in the exercise group and 74% in the control group.
In other words, “for every 16 patients that went on the exercise program, exercise prevented 1 person from recurrent or new cancer” at 5 years, Booth reported.
Overall survival results were “even more impressive,” he said.
At 8 years, 90% of patients in the exercise program were alive vs 83% of those in the control group, which translated to a 37% lower risk for death (HR, 0.63; P = .022).
“For every 14 patients who went on the exercise program, exercise prevented 1 person from dying” at the 8-year mark, Booth noted.
“Notably, this difference in survival was not driven by difference in cardiovascular deaths but by a reduction in the risk of death from colon cancer,” he said.
Besides a slight uptick in musculoskeletal aches, no major safety signals emerged in the exercise group.
It’s important to note that the survival benefit associated with exercise came after patients had received surgery followed by chemotherapy — in other words, exercise did not replace established cancer treatments. It’s also unclear whether initiating an exercise intervention earlier in the treatment trajectory — before surgery or during chemotherapy, instead of after chemotherapy — could further improve cancer outcomes, the authors noted.
Still, “exercise as an intervention is a no brainer and should be implemented broadly,” said ASCO expert Pamela Kunz, MD, with Yale School of Medicine, New Haven, Connecticut.
Marco Gerlinger, MD, with Barts Cancer Institute, London, England, agreed.
“Oncologists can now make a very clear evidence-based recommendation for patients who just completed their chemotherapy for bowel cancer and are fit enough for such an exercise program,” Gerlinger said in a statement from the nonprofit UK Science Media Centre.
Booth noted that knowledge alone will not be sufficient to allow most patients to change their lifestyle and realize the health benefits.
“The policy implementation piece of this is really key, and we need health systems, hospitals, and payers to invest in these behavior support programs so that patients have access to a physical activity consultant and can realize the health benefits,” he said.
“This intervention is empowering and achievable for patients and with much, much lower cost than many of our therapies. It is also sustainable for health systems,” he concluded.
Diet and Survival
Diet can also affect outcomes in patients with colon cancer.
In the same session describing the CHALLENGE results, Sara Char, MD, with Dana-Farber Cancer Institute in Boston, reported findings showing that consuming a diet high in proinflammatory foods was associated with worse overall survival in patients with stage III colon cancer. A proinflammatory diet includes red and processed meats, sugary drinks, and refined grains, while an anti-inflammatory diet focuses on fruits, vegetables, whole grains, fish, and olive oil.
Chronic systemic inflammation has been implicated in both colon cancer development and in its progression, and elevated levels of inflammatory markers in the blood have previously been associated with worse survival outcomes in patients with stage III colon cancer.
Char and colleagues analyzed dietary patterns of a subset of 1625 patients (mean age, 61 years) with resected stage III colon cancer enrolled in the phase 3 CALGB/SWOG 80702 (Alliance) clinical trial, which compared 3 months of adjuvant chemotherapy with 6 months of adjuvant chemotherapy, with or without the anti-inflammatory medication celecoxib.
As part of the trial, participants reported their diet and exercise habits at various timepoints. Their diets were scored using the validated empirical dietary inflammatory pattern (EDIP) tool, which is a weighted sum of 18 food groups — nine proinflammatory and nine anti-inflammatory. A high EDIP score marks a proinflammatory diet, and a low EDIP score indicates a less inflammatory diet.
During median follow-up of nearly 4 years, researchers noted a trend toward worse disease-free survival in patients with high proinflammatory diets (HR, 1.46), but this association was not significant in the multivariable adjusted model (HR, 1.36; P = .22), Char reported.
However, higher intake of proinflammatory foods was associated with significantly worse overall survival.
Patients who consumed the most proinflammatory foods (top 20%) had an 87% higher risk for death compared with those who consumed the least (bottom 20%; HR, 1.87). The median overall survival in the highest quintile was 7.7 years and was not reached in the lowest quintile.
Combine Exercise and Diet for Best Results
To examine the joint effect of physical activity and diet on overall survival, patients were divided into higher and lower levels of physical activity using a cut-off of 9 MET hours per week, which roughly correlates to 30 minutes of vigorous walking five days a week with a little bit of light yoga, Char explained.
In this analysis, patients with less proinflammatory diets and higher physical activity levels had the best overall survival outcomes, with a 63% lower risk for death compared with peers who consumed more pro-inflammatory diets and exercised less (HR, 0.37; P < .0001).
Daily celecoxib use and low-dose aspirin use (< 100 mg/d) did not affect the association between inflammatory diet and survival.
Char cautioned, that while the EDIP tool is useful to measure the inflammatory potential of a diet, “this is not a dietary recommendation, and we need further studies to be able to tailor our findings into dietary recommendations that can be provided to patients at the bedside.”
Gralow said this “early but promising observational study suggests a powerful synergy: Patients with stage III colon cancer who embraced anti-inflammatory foods and exercised regularly showed the best overall survival compared to those with inflammatory diets and limited exercise.”
The CHALLENGE trial was funded by the Canadian Cancer Society, the National Health and Medical Research Council, Cancer Research UK, and the University of Sydney Cancer Research Fund. Booth had no disclosures. The diet study was funded by the National Institutes of Health, Pfizer, and the Project P Fund. Char disclosed an advisory/consultant role with Goodpath. Kunz, Gralow and Campbell had no relevant disclosures.
A version of this article first appeared on Medscape.com.
Can exercise “therapy” and diet improve survival in patients with colon cancer? It appears so, according to two pivotal studies presented at American Society of Clinical Oncology (ASCO) 2025 annual meeting.
In the CHALLENGE trial, a structured exercise program after surgery and adjuvant chemotherapy cut the risk for colon cancer recurrence in patients with stage III and high-risk stage II disease by more than one quarter and the risk for death by more than one third.
“The magnitude of benefit with exercise is substantial. In fact, it is comparable, and in some cases exceeds the magnitude of benefit of many of our very good standard medical therapies in oncology,” study presenter Christopher Booth, MD, with Queen’s University, Kingston, Ontario, Canada, told attendees.
Results of the study were published online in The New England Journal of Medicine to coincide with the presentation at the meeting.
The findings are “nothing short of a major milestone,” said study discussant Peter Campbell, PhD, with Montefiore Einstein Comprehensive Cancer Center, Bronx, New York.
The other study showed that eating a less inflammatory diet may reduce the risk for death in patients with colon cancer, with the greatest benefits seen in those who embraced anti-inflammatory foods and exercised regularly.
“Putting these two abstracts into perspective, we as physicians need to be essentially prescribing healthy diet and exercise. The combination of the two are synergistic,” Julie Gralow, MD, ASCO chief medical officer and executive vice president, told attendees.
Despite the benefits of these lifestyle changes, exercise and diet are meant to supplement, not replace, established colon cancer treatments.
It would be a false binary to frame this as lifestyle vs cancer treatment, explained Mark Lewis, MD, director of Gastrointestinal Oncology at Intermountain Healthcare in Salt Lake City, Utah. With exercise, for instance, “the key is giving enough chemo to protect against recurrence and eliminate micrometastases but not so much that we cause neuropathy and reduce function and ability to follow the CHALLENGE structured program,” Lewis said.
Exercise and Survival
Colon cancer remains the second-leading cause of cancer death worldwide. Even with surgery and chemotherapy, roughly 30% of patients with stage III and high-risk stage II colon cancer will experience disease recurrence.
“As oncologists, one of the most common questions we get asked by patients is — what else can I do to improve my outcome?” Booth said.
Observational studies published nearly two decades ago hinted that physically active cancer survivors fare better, but no randomized trial has definitively tested whether exercise could alter disease course. That knowledge gap prompted the Canadian Cancer Trials Group to launch the CHALLENGE trial.
Between 2009 and 2023, the phase 3 study enrolled 889 adults (median age, 61 years; 51% women) who had completed surgery and adjuvant chemotherapy for stage III (90%) or high-risk stage II (10%) colon cancer. Most patients were from Canada and Australia and were enrolled 2-6 months after completing chemotherapy.
Half of study participants were randomly allocated to a structured exercise program (n = 445) and half to receive standard health education materials promoting physical activity and healthy eating (control individuals, n = 444).
As part of the structured exercise intervention, patients met with a physical activity consultant twice a month for the first 6 months. These sessions included exercise coaching and supervised exercise. Patients could choose their preferred aerobic exercise and most picked brisk walking.
The consultants gave each patient an “exercise prescription” to hit a specific amount of exercise. The target was an additional 10 metabolic equivalent (MET)–hours of aerobic activity per week — about three to four brisk walks each lasting 45-60 minutes. After 6 months, patients met with their consultants once a month, with additional sessions available for extra support if needed.
Structured exercise led to “substantial and sustained” increases in the amount of exercise participants did, as well as physiologic measures of their fitness, with “highly relevant” improvements in VO2 max, 6-minute walk test, and patient-reported physical function, underscoring that participants were not only exercising more but also getting fitter, Booth said.
Exercise was associated with a clinically meaningful and statistically significant 28% reduction in the risk for recurrent or new cancer (hazard ratio [HR], 0.72; P = .017), with a 5-year disease free survival rate of 80% in the exercise group and 74% in the control group.
In other words, “for every 16 patients that went on the exercise program, exercise prevented 1 person from recurrent or new cancer” at 5 years, Booth reported.
Overall survival results were “even more impressive,” he said.
At 8 years, 90% of patients in the exercise program were alive vs 83% of those in the control group, which translated to a 37% lower risk for death (HR, 0.63; P = .022).
“For every 14 patients who went on the exercise program, exercise prevented 1 person from dying” at the 8-year mark, Booth noted.
“Notably, this difference in survival was not driven by difference in cardiovascular deaths but by a reduction in the risk of death from colon cancer,” he said.
Besides a slight uptick in musculoskeletal aches, no major safety signals emerged in the exercise group.
It’s important to note that the survival benefit associated with exercise came after patients had received surgery followed by chemotherapy — in other words, exercise did not replace established cancer treatments. It’s also unclear whether initiating an exercise intervention earlier in the treatment trajectory — before surgery or during chemotherapy, instead of after chemotherapy — could further improve cancer outcomes, the authors noted.
Still, “exercise as an intervention is a no brainer and should be implemented broadly,” said ASCO expert Pamela Kunz, MD, with Yale School of Medicine, New Haven, Connecticut.
Marco Gerlinger, MD, with Barts Cancer Institute, London, England, agreed.
“Oncologists can now make a very clear evidence-based recommendation for patients who just completed their chemotherapy for bowel cancer and are fit enough for such an exercise program,” Gerlinger said in a statement from the nonprofit UK Science Media Centre.
Booth noted that knowledge alone will not be sufficient to allow most patients to change their lifestyle and realize the health benefits.
“The policy implementation piece of this is really key, and we need health systems, hospitals, and payers to invest in these behavior support programs so that patients have access to a physical activity consultant and can realize the health benefits,” he said.
“This intervention is empowering and achievable for patients and with much, much lower cost than many of our therapies. It is also sustainable for health systems,” he concluded.
Diet and Survival
Diet can also affect outcomes in patients with colon cancer.
In the same session describing the CHALLENGE results, Sara Char, MD, with Dana-Farber Cancer Institute in Boston, reported findings showing that consuming a diet high in proinflammatory foods was associated with worse overall survival in patients with stage III colon cancer. A proinflammatory diet includes red and processed meats, sugary drinks, and refined grains, while an anti-inflammatory diet focuses on fruits, vegetables, whole grains, fish, and olive oil.
Chronic systemic inflammation has been implicated in both colon cancer development and in its progression, and elevated levels of inflammatory markers in the blood have previously been associated with worse survival outcomes in patients with stage III colon cancer.
Char and colleagues analyzed dietary patterns of a subset of 1625 patients (mean age, 61 years) with resected stage III colon cancer enrolled in the phase 3 CALGB/SWOG 80702 (Alliance) clinical trial, which compared 3 months of adjuvant chemotherapy with 6 months of adjuvant chemotherapy, with or without the anti-inflammatory medication celecoxib.
As part of the trial, participants reported their diet and exercise habits at various timepoints. Their diets were scored using the validated empirical dietary inflammatory pattern (EDIP) tool, which is a weighted sum of 18 food groups — nine proinflammatory and nine anti-inflammatory. A high EDIP score marks a proinflammatory diet, and a low EDIP score indicates a less inflammatory diet.
During median follow-up of nearly 4 years, researchers noted a trend toward worse disease-free survival in patients with high proinflammatory diets (HR, 1.46), but this association was not significant in the multivariable adjusted model (HR, 1.36; P = .22), Char reported.
However, higher intake of proinflammatory foods was associated with significantly worse overall survival.
Patients who consumed the most proinflammatory foods (top 20%) had an 87% higher risk for death compared with those who consumed the least (bottom 20%; HR, 1.87). The median overall survival in the highest quintile was 7.7 years and was not reached in the lowest quintile.
Combine Exercise and Diet for Best Results
To examine the joint effect of physical activity and diet on overall survival, patients were divided into higher and lower levels of physical activity using a cut-off of 9 MET hours per week, which roughly correlates to 30 minutes of vigorous walking five days a week with a little bit of light yoga, Char explained.
In this analysis, patients with less proinflammatory diets and higher physical activity levels had the best overall survival outcomes, with a 63% lower risk for death compared with peers who consumed more pro-inflammatory diets and exercised less (HR, 0.37; P < .0001).
Daily celecoxib use and low-dose aspirin use (< 100 mg/d) did not affect the association between inflammatory diet and survival.
Char cautioned, that while the EDIP tool is useful to measure the inflammatory potential of a diet, “this is not a dietary recommendation, and we need further studies to be able to tailor our findings into dietary recommendations that can be provided to patients at the bedside.”
Gralow said this “early but promising observational study suggests a powerful synergy: Patients with stage III colon cancer who embraced anti-inflammatory foods and exercised regularly showed the best overall survival compared to those with inflammatory diets and limited exercise.”
The CHALLENGE trial was funded by the Canadian Cancer Society, the National Health and Medical Research Council, Cancer Research UK, and the University of Sydney Cancer Research Fund. Booth had no disclosures. The diet study was funded by the National Institutes of Health, Pfizer, and the Project P Fund. Char disclosed an advisory/consultant role with Goodpath. Kunz, Gralow and Campbell had no relevant disclosures.
A version of this article first appeared on Medscape.com.
Can Lifestyle Changes Save Lives in Colon Cancer?
Can Lifestyle Changes Save Lives in Colon Cancer?
VA to Allow Veteran Referrals to Community Care Without Second Review
VA to Allow Veteran Referrals to Community Care Without Second Review
Veterans enrolled in the US Department of Veterans Affairs (VA) who have been referred to Community Care no longer need a second review from a VA clinician according to a new policy. The provision implements language from the Senator Elizabeth Dole 21st Century Veterans Healthcare and Benefits Improvement Act. VA officials hope that it will speed up access to community care.
The move expands on the 2019 MISSION Act, which allows eligible veterans to access health care from non-VA clinicians that is paid for by the VA when it is in their “best medical interest.” Those decisions, however, were not considered final until reviewed by a second VA doctor.
The Dole Act prohibits VA administrators from overriding a VA doctor’s referral for a patient to receive outside care. According to the law, the ban on administrative review will remain in place for 2 years, after which the VA must report on its effects to Congress. The VA announced it would begin training employees to ensure the community care referral process is followed in compliance with the Dole Act.
Analysis from the Veterans Healthcare Policy Institute claims the best medical interest criterion “is to be considered when a veteran's health and/or well-being would be compromised if they were not able to be seen in the community for the requested clinical service.”
During a March hearing, Rep. Julia Brownley (D-CA), ranking Democrat on the House Veterans’ Affairs subcommittee on health, said any veteran who seeks residential treatment should get it, but noted the VA has not developed a fee schedule for community treatment centers. In at least 1 case, she said, the department was charged up to $6000 a day for 1 patient. Brownley also noted that the VA doesn't track the timeliness or quality of medical care in community residential treatment facilities.
“We have no way of knowing the level of treatment or support they are getting,” she said. “We must find a balance between community care and VA direct care. In my opinion, we have not found that balance when it comes to residential rehabilitation treatment facilities.”
At the same hearing, chair of the House Veterans Affairs health subcommittee Rep. Mariannette Miller-Meeks (R-IA) said more change is needed—specifically to ensure that veterans also can access private residential substance abuse treatment centers. Some, she said, “are told they cannot access community care unless a VA facility fails to meet a 20-day threshold—forcing them to wait, even when immediate, alternative options exist."
The House of Representatives passed H.R. 1969, the No Wrong Door for Veterans Act, in May, which expands the VA suicide prevention grant program. However, the Senate has yet to take up the legislation. “I’ve seen firsthand how difficult it can be for veterans in crisis to navigate a complicated system when every second counts,” Miller-Meeks said. “The No Wrong Door for Veterans Act ensures that our heroes are never turned away or left without help. It streamlines access, strengthens coordination, and reaffirms our promise to those who served.”
Veterans enrolled in the US Department of Veterans Affairs (VA) who have been referred to Community Care no longer need a second review from a VA clinician according to a new policy. The provision implements language from the Senator Elizabeth Dole 21st Century Veterans Healthcare and Benefits Improvement Act. VA officials hope that it will speed up access to community care.
The move expands on the 2019 MISSION Act, which allows eligible veterans to access health care from non-VA clinicians that is paid for by the VA when it is in their “best medical interest.” Those decisions, however, were not considered final until reviewed by a second VA doctor.
The Dole Act prohibits VA administrators from overriding a VA doctor’s referral for a patient to receive outside care. According to the law, the ban on administrative review will remain in place for 2 years, after which the VA must report on its effects to Congress. The VA announced it would begin training employees to ensure the community care referral process is followed in compliance with the Dole Act.
Analysis from the Veterans Healthcare Policy Institute claims the best medical interest criterion “is to be considered when a veteran's health and/or well-being would be compromised if they were not able to be seen in the community for the requested clinical service.”
During a March hearing, Rep. Julia Brownley (D-CA), ranking Democrat on the House Veterans’ Affairs subcommittee on health, said any veteran who seeks residential treatment should get it, but noted the VA has not developed a fee schedule for community treatment centers. In at least 1 case, she said, the department was charged up to $6000 a day for 1 patient. Brownley also noted that the VA doesn't track the timeliness or quality of medical care in community residential treatment facilities.
“We have no way of knowing the level of treatment or support they are getting,” she said. “We must find a balance between community care and VA direct care. In my opinion, we have not found that balance when it comes to residential rehabilitation treatment facilities.”
At the same hearing, chair of the House Veterans Affairs health subcommittee Rep. Mariannette Miller-Meeks (R-IA) said more change is needed—specifically to ensure that veterans also can access private residential substance abuse treatment centers. Some, she said, “are told they cannot access community care unless a VA facility fails to meet a 20-day threshold—forcing them to wait, even when immediate, alternative options exist."
The House of Representatives passed H.R. 1969, the No Wrong Door for Veterans Act, in May, which expands the VA suicide prevention grant program. However, the Senate has yet to take up the legislation. “I’ve seen firsthand how difficult it can be for veterans in crisis to navigate a complicated system when every second counts,” Miller-Meeks said. “The No Wrong Door for Veterans Act ensures that our heroes are never turned away or left without help. It streamlines access, strengthens coordination, and reaffirms our promise to those who served.”
Veterans enrolled in the US Department of Veterans Affairs (VA) who have been referred to Community Care no longer need a second review from a VA clinician according to a new policy. The provision implements language from the Senator Elizabeth Dole 21st Century Veterans Healthcare and Benefits Improvement Act. VA officials hope that it will speed up access to community care.
The move expands on the 2019 MISSION Act, which allows eligible veterans to access health care from non-VA clinicians that is paid for by the VA when it is in their “best medical interest.” Those decisions, however, were not considered final until reviewed by a second VA doctor.
The Dole Act prohibits VA administrators from overriding a VA doctor’s referral for a patient to receive outside care. According to the law, the ban on administrative review will remain in place for 2 years, after which the VA must report on its effects to Congress. The VA announced it would begin training employees to ensure the community care referral process is followed in compliance with the Dole Act.
Analysis from the Veterans Healthcare Policy Institute claims the best medical interest criterion “is to be considered when a veteran's health and/or well-being would be compromised if they were not able to be seen in the community for the requested clinical service.”
During a March hearing, Rep. Julia Brownley (D-CA), ranking Democrat on the House Veterans’ Affairs subcommittee on health, said any veteran who seeks residential treatment should get it, but noted the VA has not developed a fee schedule for community treatment centers. In at least 1 case, she said, the department was charged up to $6000 a day for 1 patient. Brownley also noted that the VA doesn't track the timeliness or quality of medical care in community residential treatment facilities.
“We have no way of knowing the level of treatment or support they are getting,” she said. “We must find a balance between community care and VA direct care. In my opinion, we have not found that balance when it comes to residential rehabilitation treatment facilities.”
At the same hearing, chair of the House Veterans Affairs health subcommittee Rep. Mariannette Miller-Meeks (R-IA) said more change is needed—specifically to ensure that veterans also can access private residential substance abuse treatment centers. Some, she said, “are told they cannot access community care unless a VA facility fails to meet a 20-day threshold—forcing them to wait, even when immediate, alternative options exist."
The House of Representatives passed H.R. 1969, the No Wrong Door for Veterans Act, in May, which expands the VA suicide prevention grant program. However, the Senate has yet to take up the legislation. “I’ve seen firsthand how difficult it can be for veterans in crisis to navigate a complicated system when every second counts,” Miller-Meeks said. “The No Wrong Door for Veterans Act ensures that our heroes are never turned away or left without help. It streamlines access, strengthens coordination, and reaffirms our promise to those who served.”
VA to Allow Veteran Referrals to Community Care Without Second Review
VA to Allow Veteran Referrals to Community Care Without Second Review
Suicide Prevention Grant Program Reauthorized
Suicide Prevention Grant Program Reauthorized
Community-based organizations that provide suicide-prevention services can now access about $52.5 million in US Department of Veterans Affairs (VA) grants. The grant is part of the 3-year Staff Sergeant Fox Suicide Prevention Grant Program, which honors Parker Gordon Fox, a sniper instructor at the U.S. Army Infantry School at Fort Benning, Georgia, who died by suicide in 2020. In consecutive Congressional hearings, lawmakers called for the reauthorization of the program to address gaps in VA care.
“It has been a game-changer for so many veterans,” Sen. Richard Blumenthal (D-CT) said.
The money provides or coordinates primarily nonclinical suicide prevention services, including outreach and linkage to VA and community resources. Services also may include baseline mental health screenings, case management and peer support, education on suicide risk, VA benefits assistance, and emergency clinical services.
Since its inception in 2022, the program has awarded $157.5 million to 95 organizations in 43 states, US territories, and tribal lands. Speaking before the House Committee on Veterans’ Affairs on May 15, VA Secretary Doug Collins praised the Fox program for bringing “different voices into the conversation,” but added it wasn’t enough. He noted that the veteran suicide rate has not changed since 2008, despite the VA annually spending $588 million on suicide prevention over the past few years.
In an op-ed, Russell Lemle, a senior policy analyst at the Veterans Healthcare Policy Institute, disputed Collins' characterization of veteran suicides. Between 2008 and 2022 (the last year for which complete data is available), US deaths by suicide increased 37% while the number of veteran deaths by suicide fell 2%. “This data collection was the single best part of the program,” he argued, calling for reauthorization to continue requiring data-targeted solutions.
According to a 2024 VA interim report on the Fox grant program, grantees had completed > 16,590 outreach contacts and engaged 3204 participants as of September 30, 2023. An additional 864 individuals were onboarding at the time of the report.
The current version of the grant program requires grantees to use validated tools, including the VA Data Collection Tool, and other assessments furnished by VA to determine the effectiveness of the suicide prevention services. They must also provide each participant with a satisfaction survey and submit periodic and annual financial and performance reports.
Despite the Trump administration’s cuts and cancellations to the federal workforce and federal programs, Collins told the Senate committee he is firmly on the side of working with community-based organizations like the Fox grant program to broaden the VA’s reach: “I want to use grants and programs like [the Fox grant program] to reach out beyond the scope of where we’re currently reaching, to say how can we actually touch the veteran that’s not being touched right now by these programs,” Collins said. “We’ve got to do better at using the grants, using our programs to go outside the normal bubble and use others to help get the word out.”
Grant applications are due in July and VA will choose awardees in September. Organizations can apply for grants worth up to $750,000 and may apply to renew awards from year to year throughout the length of the program.
Community-based organizations that provide suicide-prevention services can now access about $52.5 million in US Department of Veterans Affairs (VA) grants. The grant is part of the 3-year Staff Sergeant Fox Suicide Prevention Grant Program, which honors Parker Gordon Fox, a sniper instructor at the U.S. Army Infantry School at Fort Benning, Georgia, who died by suicide in 2020. In consecutive Congressional hearings, lawmakers called for the reauthorization of the program to address gaps in VA care.
“It has been a game-changer for so many veterans,” Sen. Richard Blumenthal (D-CT) said.
The money provides or coordinates primarily nonclinical suicide prevention services, including outreach and linkage to VA and community resources. Services also may include baseline mental health screenings, case management and peer support, education on suicide risk, VA benefits assistance, and emergency clinical services.
Since its inception in 2022, the program has awarded $157.5 million to 95 organizations in 43 states, US territories, and tribal lands. Speaking before the House Committee on Veterans’ Affairs on May 15, VA Secretary Doug Collins praised the Fox program for bringing “different voices into the conversation,” but added it wasn’t enough. He noted that the veteran suicide rate has not changed since 2008, despite the VA annually spending $588 million on suicide prevention over the past few years.
In an op-ed, Russell Lemle, a senior policy analyst at the Veterans Healthcare Policy Institute, disputed Collins' characterization of veteran suicides. Between 2008 and 2022 (the last year for which complete data is available), US deaths by suicide increased 37% while the number of veteran deaths by suicide fell 2%. “This data collection was the single best part of the program,” he argued, calling for reauthorization to continue requiring data-targeted solutions.
According to a 2024 VA interim report on the Fox grant program, grantees had completed > 16,590 outreach contacts and engaged 3204 participants as of September 30, 2023. An additional 864 individuals were onboarding at the time of the report.
The current version of the grant program requires grantees to use validated tools, including the VA Data Collection Tool, and other assessments furnished by VA to determine the effectiveness of the suicide prevention services. They must also provide each participant with a satisfaction survey and submit periodic and annual financial and performance reports.
Despite the Trump administration’s cuts and cancellations to the federal workforce and federal programs, Collins told the Senate committee he is firmly on the side of working with community-based organizations like the Fox grant program to broaden the VA’s reach: “I want to use grants and programs like [the Fox grant program] to reach out beyond the scope of where we’re currently reaching, to say how can we actually touch the veteran that’s not being touched right now by these programs,” Collins said. “We’ve got to do better at using the grants, using our programs to go outside the normal bubble and use others to help get the word out.”
Grant applications are due in July and VA will choose awardees in September. Organizations can apply for grants worth up to $750,000 and may apply to renew awards from year to year throughout the length of the program.
Community-based organizations that provide suicide-prevention services can now access about $52.5 million in US Department of Veterans Affairs (VA) grants. The grant is part of the 3-year Staff Sergeant Fox Suicide Prevention Grant Program, which honors Parker Gordon Fox, a sniper instructor at the U.S. Army Infantry School at Fort Benning, Georgia, who died by suicide in 2020. In consecutive Congressional hearings, lawmakers called for the reauthorization of the program to address gaps in VA care.
“It has been a game-changer for so many veterans,” Sen. Richard Blumenthal (D-CT) said.
The money provides or coordinates primarily nonclinical suicide prevention services, including outreach and linkage to VA and community resources. Services also may include baseline mental health screenings, case management and peer support, education on suicide risk, VA benefits assistance, and emergency clinical services.
Since its inception in 2022, the program has awarded $157.5 million to 95 organizations in 43 states, US territories, and tribal lands. Speaking before the House Committee on Veterans’ Affairs on May 15, VA Secretary Doug Collins praised the Fox program for bringing “different voices into the conversation,” but added it wasn’t enough. He noted that the veteran suicide rate has not changed since 2008, despite the VA annually spending $588 million on suicide prevention over the past few years.
In an op-ed, Russell Lemle, a senior policy analyst at the Veterans Healthcare Policy Institute, disputed Collins' characterization of veteran suicides. Between 2008 and 2022 (the last year for which complete data is available), US deaths by suicide increased 37% while the number of veteran deaths by suicide fell 2%. “This data collection was the single best part of the program,” he argued, calling for reauthorization to continue requiring data-targeted solutions.
According to a 2024 VA interim report on the Fox grant program, grantees had completed > 16,590 outreach contacts and engaged 3204 participants as of September 30, 2023. An additional 864 individuals were onboarding at the time of the report.
The current version of the grant program requires grantees to use validated tools, including the VA Data Collection Tool, and other assessments furnished by VA to determine the effectiveness of the suicide prevention services. They must also provide each participant with a satisfaction survey and submit periodic and annual financial and performance reports.
Despite the Trump administration’s cuts and cancellations to the federal workforce and federal programs, Collins told the Senate committee he is firmly on the side of working with community-based organizations like the Fox grant program to broaden the VA’s reach: “I want to use grants and programs like [the Fox grant program] to reach out beyond the scope of where we’re currently reaching, to say how can we actually touch the veteran that’s not being touched right now by these programs,” Collins said. “We’ve got to do better at using the grants, using our programs to go outside the normal bubble and use others to help get the word out.”
Grant applications are due in July and VA will choose awardees in September. Organizations can apply for grants worth up to $750,000 and may apply to renew awards from year to year throughout the length of the program.
Suicide Prevention Grant Program Reauthorized
Suicide Prevention Grant Program Reauthorized
Hurricanes, Fires, Floods: A Rising Threat to Cancer Care
As Hurricane Helene approached western North Carolina, Martin Palmeri, MD, MBA, didn’t anticipate the storm would disrupt practice operations for more than a day or so.
But the massive rainfall and flooding damage last September proved to be far more challenging. Despite best efforts by the 13-physician practice, basic treatments for most patients were interrupted for about a week.
Flooding washed out some of the major roads leading to the main Asheville clinic and affiliated rural sites, limiting travel and slowing delivery of medications, intravenous (IV) fluids, and other supplies, Palmeri said. Some patients and employees weren’t initially reachable due to the loss of the internet and cell phone service. The storm-related fallout even forced patients to relocate elsewhere for weeks or longer.
During the storm, backup generators kept power on at the Asheville clinic, protecting chemotherapy and other refrigerated drugs, but the storm damaged the municipal water supply.
“Water was the number one thing — how do you get water to the office?” Palmeri said. “You can’t give someone an 8-hour infusion if they don’t have means of going to the toilet or having something to drink.”
Hurricanes. Wildfires. Heat waves. As climate-driven extreme weather has become more common, researchers, oncologists, and patients are increasingly being forced to consider the consequences of these disruptions.
Along with preventing patients and providers from reaching treatment sites, experts said, extreme weather can undercut patients’ health and care in other ways. Patients with more limited lung capacity following lung cancer surgery, for instance, may struggle with breathing during wildfires. Extreme heat can prove risky for patients already dehydrated or weakened by treatment-related side effects. Power outages and severe flooding can affect vital infrastructure, disrupting operations at facilities that manufacture essential drugs. Power outages can also impede radiotherapy, which requires machines powered by electricity.
“Any of these [weather] events can disrupt this critical cancer care continuum among a population of people that already are very vulnerable,” said Joan Casey, PhD, an environmental epidemiologist and associate professor at the University of Washington in Seattle.
Extreme Weather and Cancer Survival
For patients with cancer, survival often relies on highly regimented protocols, which may require surgery plus frequent visits for radiation, chemotherapy, or immunotherapy that can last months, said Eric Bernicker, MD, a Colorado oncologist and lead author of a 2023 American Society of Clinical Oncology position statement about the impact of climate change on cancer care.
Interruptions to care, regardless of the cause, can lead to worse outcomes for patients, Bernicker said. “If you’re in the middle of your post-lumpectomy radiation and your radiation center shuts for 2 weeks,” he said, “that is not good.”
Research indicated that even short treatment disruptions can affect outcomes for patients with cancer and that delays caused by extreme weather — which may last for weeks — can affect survival for these patients.
One analysis, published in JAMA Oncology in 2023, found that patients exposed to wildfire within the first year after potentially curative lung cancer surgery had worse survival outcomes than those who weren’t exposed during their recovery.
In another study, patients with lung cancer who had their radiation interrupted when a hurricane struck had a 19% greater risk of dying overall compared with similar patients who were not affected. Another analysis found that patients with breast cancer who were partway through treatment when Hurricane Katrina hit the Louisiana coastline had a significantly greater risk of dying over a 10-year period compared with patients who lived elsewhere.
The potential threats to survival highlighted the impacts of extreme weather on carefully orchestrated systems of care that place patients facing already fragile situations in impossible binds, Casey said.
Douglas Flora, MD, a Kentucky oncologist and president-elect of the Association of Cancer Care Centers, Rockville, Maryland, agreed.
“We’ve seen this with an increasing frequency over the last several years,” Flora said. “It’s one thing if it’s routine follow-up or surveillance care, but many cancer patients’ survivals are directly related to not having interruptions in their care.”
Challenging Realities
Following Helene, the most pressing issue was the lack of water, Palmeri said.
The lack of reliable clean water created challenges for patients receiving radiation or chemotherapy infusions, which can cause vomiting and diarrhea that leave patients dehydrated. Toilets were also unusable.
Even when the city of Asheville said the water was likely safe enough to bathe in, local leaders still reported potential risks from bacteria and other contaminants in the water, Palmeri said. Those with a fragile immune system or breaks in the skin “could get serious and life-threatening infections,” he explained.
To make matters worse, damage to a North Carolina facility manufacturing IV fluids left the United States in shortage for months. IV fluids are key not only for providing hydration but also for easing nausea, fatigue, and other issues caused by cancer therapies.
With wildfires, as occurred in southern California early this year, patients undergoing cancer treatment might feel they have no option but to remain near home to continue getting care, Casey said. “It’s restricting their agency in the kinds of choices that they have to make during these severe weather events.”
Meanwhile, thick wildfire smoke can confine patients to their homes, said Lawrence Wagman, MD, a surgical oncologist and a regional medical director at the City of Hope network, who described its main facility in Duarte, California, coming within a dozen miles of the Eaton fire. “One of the biggest problems was so much smoke in the air,” he said. “And the air quality was so low that it was, in many ways, dangerous for patients to travel.”
“These fires were so aggressive, and they kept popping up,” Wagman said. Plus, the emotional strain of looming wildfires persisted for both patients and cancer clinicians for weeks on end, he added.
For those who evacuate, the logistics can be complex.
Not only are cancer treatment plans highly structured, but switching care to another facility is far from easy, Bernicker said. The new facility will likely need to submit a treatment plan and get insurance coverage before moving forward.
“I’m not saying that takes forever,” he said. “But what I’m saying is that it’s not like you just roll in and they hang the [infusion] bag.”
Neither is a shelter typically an option for patients during treatment, said Seth Berkowitz, a licensed clinical social worker and director of Strategic Healthcare Partnerships at The Leukemia & Lymphoma Society. “They have to have a place to go that’s safe and germ-free.”
In western North Carolina, the strain on already ill patients and their caregivers could be overwhelming, Palmeri said. He recounted how the husband of one patient with advanced cancer died after the storm came through.
“He tried to go out there with a chainsaw to clear a way out so that they could get out of their house in case he needed to take her to the hospital,” Palmeri said. “And he had a heart attack there in the driveway.”
Rebuilding and Planning Ahead
Experts are only at the early stages of grasping the magnitude of extreme weather on cancer care and developing strategies to curtail care gaps and potential harm to patients, said Katie Lichter, MD, a radiation oncologist at the University of California San Francisco, who studies extreme weather and cancer treatment.
“How does it impact health care delivery services at every step, from prevention to screening to treatment and survivorship?” Lichter asked. “We’re just starting to understand and to even quantify that,” she said, which included identifying patients who are most vulnerable. She worries, in particular, about patients living in rural areas who already travel longer distances and often face more difficulties accessing care.
The gap between research and reality still looms large. A recent analysis, led by Lichter, looked at 176 California radiation oncology clinics and found that all of them were located within 25 miles of a wildfire that had occurred within the prior 5 years. Yet among the 51 clinics that responded to a 2022 survey,just 47% reported that their clinic had a wildfire emergency preparedness plan.
The American Cancer Society does provide some guidance on how patients can prepare for a weather-related crisis, including having extra supplies of medications or special equipment on hand.
Still, providers are often in reaction mode when extreme weather strikes.
Without adequate clean water after Helene, leaders at Palmeri’s practice moved swiftly, purchasing 40,000-50,000 bottles of water and bringing in porta potties from elsewhere.
“I think we were able to get things up and going very quickly,” said Palmeri, who noted that full services resumed about 10 days after the storm. “For most patients, missing a week of treatment would not do a disservice to their well-being or outcome.”
Going forward, to provide a more comprehensive strategy, Lichter is working with colleagues to develop clinical tool kits to help oncology practices and patients prepare for severe weather events, such as outlining backup treatment contingency plans, ensuring early medication refills, and boosting communication with patient alert systems.
Clinicians are also implementing their own strategies. To limit communication gaps during power outages, Palmeri said that, since Helene, his practice has made sure that their clinic sites, physicians, and other key people now have cell phone service through satellite via Starlink.
“No one has phone books anymore,” he said, so cancer clinicians should keep crucial contact information on paper, such as details about businesses that distribute water and porta potties, given that online searches may not be feasible.
Clinicians should also advise patients to keep a hard copy of recent medical findings handy, including medications and lab results, in case they arrive at an emergency room far from home and physicians can’t access their electronic health record, Bernicker said.
When there is enough advance warning of an approaching weather event, clinicians can help patients keep at least a week’s worth of medication on hand for symptom-related issues, such as nausea or pain, as well as antibiotics so patients don’t have to seek out emergency care during the crisis, Bernicker said. However, Bernicker noted, some insurers may be reluctant to fill certain prescriptions in advance, like those for opioids.
Making headway on more robust preparedness strategies may be slowed. As of March, the National Institutes of Health will no longer fund research about the health effects of climate change.
Bernicker hoped that such cutbacks would be rolled back. What’s on the line, he stressed, is maintaining the highest quality of care for patients with cancer.
“We really are in a golden age of oncology therapeutics,” he said. “We have patients living longer than anyone would have predicted 20 or 25 years ago. But all those advances are contingent on people having access to their centers and not having that interrupted.”
A version of this article first appeared on Medscape.com.
As Hurricane Helene approached western North Carolina, Martin Palmeri, MD, MBA, didn’t anticipate the storm would disrupt practice operations for more than a day or so.
But the massive rainfall and flooding damage last September proved to be far more challenging. Despite best efforts by the 13-physician practice, basic treatments for most patients were interrupted for about a week.
Flooding washed out some of the major roads leading to the main Asheville clinic and affiliated rural sites, limiting travel and slowing delivery of medications, intravenous (IV) fluids, and other supplies, Palmeri said. Some patients and employees weren’t initially reachable due to the loss of the internet and cell phone service. The storm-related fallout even forced patients to relocate elsewhere for weeks or longer.
During the storm, backup generators kept power on at the Asheville clinic, protecting chemotherapy and other refrigerated drugs, but the storm damaged the municipal water supply.
“Water was the number one thing — how do you get water to the office?” Palmeri said. “You can’t give someone an 8-hour infusion if they don’t have means of going to the toilet or having something to drink.”
Hurricanes. Wildfires. Heat waves. As climate-driven extreme weather has become more common, researchers, oncologists, and patients are increasingly being forced to consider the consequences of these disruptions.
Along with preventing patients and providers from reaching treatment sites, experts said, extreme weather can undercut patients’ health and care in other ways. Patients with more limited lung capacity following lung cancer surgery, for instance, may struggle with breathing during wildfires. Extreme heat can prove risky for patients already dehydrated or weakened by treatment-related side effects. Power outages and severe flooding can affect vital infrastructure, disrupting operations at facilities that manufacture essential drugs. Power outages can also impede radiotherapy, which requires machines powered by electricity.
“Any of these [weather] events can disrupt this critical cancer care continuum among a population of people that already are very vulnerable,” said Joan Casey, PhD, an environmental epidemiologist and associate professor at the University of Washington in Seattle.
Extreme Weather and Cancer Survival
For patients with cancer, survival often relies on highly regimented protocols, which may require surgery plus frequent visits for radiation, chemotherapy, or immunotherapy that can last months, said Eric Bernicker, MD, a Colorado oncologist and lead author of a 2023 American Society of Clinical Oncology position statement about the impact of climate change on cancer care.
Interruptions to care, regardless of the cause, can lead to worse outcomes for patients, Bernicker said. “If you’re in the middle of your post-lumpectomy radiation and your radiation center shuts for 2 weeks,” he said, “that is not good.”
Research indicated that even short treatment disruptions can affect outcomes for patients with cancer and that delays caused by extreme weather — which may last for weeks — can affect survival for these patients.
One analysis, published in JAMA Oncology in 2023, found that patients exposed to wildfire within the first year after potentially curative lung cancer surgery had worse survival outcomes than those who weren’t exposed during their recovery.
In another study, patients with lung cancer who had their radiation interrupted when a hurricane struck had a 19% greater risk of dying overall compared with similar patients who were not affected. Another analysis found that patients with breast cancer who were partway through treatment when Hurricane Katrina hit the Louisiana coastline had a significantly greater risk of dying over a 10-year period compared with patients who lived elsewhere.
The potential threats to survival highlighted the impacts of extreme weather on carefully orchestrated systems of care that place patients facing already fragile situations in impossible binds, Casey said.
Douglas Flora, MD, a Kentucky oncologist and president-elect of the Association of Cancer Care Centers, Rockville, Maryland, agreed.
“We’ve seen this with an increasing frequency over the last several years,” Flora said. “It’s one thing if it’s routine follow-up or surveillance care, but many cancer patients’ survivals are directly related to not having interruptions in their care.”
Challenging Realities
Following Helene, the most pressing issue was the lack of water, Palmeri said.
The lack of reliable clean water created challenges for patients receiving radiation or chemotherapy infusions, which can cause vomiting and diarrhea that leave patients dehydrated. Toilets were also unusable.
Even when the city of Asheville said the water was likely safe enough to bathe in, local leaders still reported potential risks from bacteria and other contaminants in the water, Palmeri said. Those with a fragile immune system or breaks in the skin “could get serious and life-threatening infections,” he explained.
To make matters worse, damage to a North Carolina facility manufacturing IV fluids left the United States in shortage for months. IV fluids are key not only for providing hydration but also for easing nausea, fatigue, and other issues caused by cancer therapies.
With wildfires, as occurred in southern California early this year, patients undergoing cancer treatment might feel they have no option but to remain near home to continue getting care, Casey said. “It’s restricting their agency in the kinds of choices that they have to make during these severe weather events.”
Meanwhile, thick wildfire smoke can confine patients to their homes, said Lawrence Wagman, MD, a surgical oncologist and a regional medical director at the City of Hope network, who described its main facility in Duarte, California, coming within a dozen miles of the Eaton fire. “One of the biggest problems was so much smoke in the air,” he said. “And the air quality was so low that it was, in many ways, dangerous for patients to travel.”
“These fires were so aggressive, and they kept popping up,” Wagman said. Plus, the emotional strain of looming wildfires persisted for both patients and cancer clinicians for weeks on end, he added.
For those who evacuate, the logistics can be complex.
Not only are cancer treatment plans highly structured, but switching care to another facility is far from easy, Bernicker said. The new facility will likely need to submit a treatment plan and get insurance coverage before moving forward.
“I’m not saying that takes forever,” he said. “But what I’m saying is that it’s not like you just roll in and they hang the [infusion] bag.”
Neither is a shelter typically an option for patients during treatment, said Seth Berkowitz, a licensed clinical social worker and director of Strategic Healthcare Partnerships at The Leukemia & Lymphoma Society. “They have to have a place to go that’s safe and germ-free.”
In western North Carolina, the strain on already ill patients and their caregivers could be overwhelming, Palmeri said. He recounted how the husband of one patient with advanced cancer died after the storm came through.
“He tried to go out there with a chainsaw to clear a way out so that they could get out of their house in case he needed to take her to the hospital,” Palmeri said. “And he had a heart attack there in the driveway.”
Rebuilding and Planning Ahead
Experts are only at the early stages of grasping the magnitude of extreme weather on cancer care and developing strategies to curtail care gaps and potential harm to patients, said Katie Lichter, MD, a radiation oncologist at the University of California San Francisco, who studies extreme weather and cancer treatment.
“How does it impact health care delivery services at every step, from prevention to screening to treatment and survivorship?” Lichter asked. “We’re just starting to understand and to even quantify that,” she said, which included identifying patients who are most vulnerable. She worries, in particular, about patients living in rural areas who already travel longer distances and often face more difficulties accessing care.
The gap between research and reality still looms large. A recent analysis, led by Lichter, looked at 176 California radiation oncology clinics and found that all of them were located within 25 miles of a wildfire that had occurred within the prior 5 years. Yet among the 51 clinics that responded to a 2022 survey,just 47% reported that their clinic had a wildfire emergency preparedness plan.
The American Cancer Society does provide some guidance on how patients can prepare for a weather-related crisis, including having extra supplies of medications or special equipment on hand.
Still, providers are often in reaction mode when extreme weather strikes.
Without adequate clean water after Helene, leaders at Palmeri’s practice moved swiftly, purchasing 40,000-50,000 bottles of water and bringing in porta potties from elsewhere.
“I think we were able to get things up and going very quickly,” said Palmeri, who noted that full services resumed about 10 days after the storm. “For most patients, missing a week of treatment would not do a disservice to their well-being or outcome.”
Going forward, to provide a more comprehensive strategy, Lichter is working with colleagues to develop clinical tool kits to help oncology practices and patients prepare for severe weather events, such as outlining backup treatment contingency plans, ensuring early medication refills, and boosting communication with patient alert systems.
Clinicians are also implementing their own strategies. To limit communication gaps during power outages, Palmeri said that, since Helene, his practice has made sure that their clinic sites, physicians, and other key people now have cell phone service through satellite via Starlink.
“No one has phone books anymore,” he said, so cancer clinicians should keep crucial contact information on paper, such as details about businesses that distribute water and porta potties, given that online searches may not be feasible.
Clinicians should also advise patients to keep a hard copy of recent medical findings handy, including medications and lab results, in case they arrive at an emergency room far from home and physicians can’t access their electronic health record, Bernicker said.
When there is enough advance warning of an approaching weather event, clinicians can help patients keep at least a week’s worth of medication on hand for symptom-related issues, such as nausea or pain, as well as antibiotics so patients don’t have to seek out emergency care during the crisis, Bernicker said. However, Bernicker noted, some insurers may be reluctant to fill certain prescriptions in advance, like those for opioids.
Making headway on more robust preparedness strategies may be slowed. As of March, the National Institutes of Health will no longer fund research about the health effects of climate change.
Bernicker hoped that such cutbacks would be rolled back. What’s on the line, he stressed, is maintaining the highest quality of care for patients with cancer.
“We really are in a golden age of oncology therapeutics,” he said. “We have patients living longer than anyone would have predicted 20 or 25 years ago. But all those advances are contingent on people having access to their centers and not having that interrupted.”
A version of this article first appeared on Medscape.com.
As Hurricane Helene approached western North Carolina, Martin Palmeri, MD, MBA, didn’t anticipate the storm would disrupt practice operations for more than a day or so.
But the massive rainfall and flooding damage last September proved to be far more challenging. Despite best efforts by the 13-physician practice, basic treatments for most patients were interrupted for about a week.
Flooding washed out some of the major roads leading to the main Asheville clinic and affiliated rural sites, limiting travel and slowing delivery of medications, intravenous (IV) fluids, and other supplies, Palmeri said. Some patients and employees weren’t initially reachable due to the loss of the internet and cell phone service. The storm-related fallout even forced patients to relocate elsewhere for weeks or longer.
During the storm, backup generators kept power on at the Asheville clinic, protecting chemotherapy and other refrigerated drugs, but the storm damaged the municipal water supply.
“Water was the number one thing — how do you get water to the office?” Palmeri said. “You can’t give someone an 8-hour infusion if they don’t have means of going to the toilet or having something to drink.”
Hurricanes. Wildfires. Heat waves. As climate-driven extreme weather has become more common, researchers, oncologists, and patients are increasingly being forced to consider the consequences of these disruptions.
Along with preventing patients and providers from reaching treatment sites, experts said, extreme weather can undercut patients’ health and care in other ways. Patients with more limited lung capacity following lung cancer surgery, for instance, may struggle with breathing during wildfires. Extreme heat can prove risky for patients already dehydrated or weakened by treatment-related side effects. Power outages and severe flooding can affect vital infrastructure, disrupting operations at facilities that manufacture essential drugs. Power outages can also impede radiotherapy, which requires machines powered by electricity.
“Any of these [weather] events can disrupt this critical cancer care continuum among a population of people that already are very vulnerable,” said Joan Casey, PhD, an environmental epidemiologist and associate professor at the University of Washington in Seattle.
Extreme Weather and Cancer Survival
For patients with cancer, survival often relies on highly regimented protocols, which may require surgery plus frequent visits for radiation, chemotherapy, or immunotherapy that can last months, said Eric Bernicker, MD, a Colorado oncologist and lead author of a 2023 American Society of Clinical Oncology position statement about the impact of climate change on cancer care.
Interruptions to care, regardless of the cause, can lead to worse outcomes for patients, Bernicker said. “If you’re in the middle of your post-lumpectomy radiation and your radiation center shuts for 2 weeks,” he said, “that is not good.”
Research indicated that even short treatment disruptions can affect outcomes for patients with cancer and that delays caused by extreme weather — which may last for weeks — can affect survival for these patients.
One analysis, published in JAMA Oncology in 2023, found that patients exposed to wildfire within the first year after potentially curative lung cancer surgery had worse survival outcomes than those who weren’t exposed during their recovery.
In another study, patients with lung cancer who had their radiation interrupted when a hurricane struck had a 19% greater risk of dying overall compared with similar patients who were not affected. Another analysis found that patients with breast cancer who were partway through treatment when Hurricane Katrina hit the Louisiana coastline had a significantly greater risk of dying over a 10-year period compared with patients who lived elsewhere.
The potential threats to survival highlighted the impacts of extreme weather on carefully orchestrated systems of care that place patients facing already fragile situations in impossible binds, Casey said.
Douglas Flora, MD, a Kentucky oncologist and president-elect of the Association of Cancer Care Centers, Rockville, Maryland, agreed.
“We’ve seen this with an increasing frequency over the last several years,” Flora said. “It’s one thing if it’s routine follow-up or surveillance care, but many cancer patients’ survivals are directly related to not having interruptions in their care.”
Challenging Realities
Following Helene, the most pressing issue was the lack of water, Palmeri said.
The lack of reliable clean water created challenges for patients receiving radiation or chemotherapy infusions, which can cause vomiting and diarrhea that leave patients dehydrated. Toilets were also unusable.
Even when the city of Asheville said the water was likely safe enough to bathe in, local leaders still reported potential risks from bacteria and other contaminants in the water, Palmeri said. Those with a fragile immune system or breaks in the skin “could get serious and life-threatening infections,” he explained.
To make matters worse, damage to a North Carolina facility manufacturing IV fluids left the United States in shortage for months. IV fluids are key not only for providing hydration but also for easing nausea, fatigue, and other issues caused by cancer therapies.
With wildfires, as occurred in southern California early this year, patients undergoing cancer treatment might feel they have no option but to remain near home to continue getting care, Casey said. “It’s restricting their agency in the kinds of choices that they have to make during these severe weather events.”
Meanwhile, thick wildfire smoke can confine patients to their homes, said Lawrence Wagman, MD, a surgical oncologist and a regional medical director at the City of Hope network, who described its main facility in Duarte, California, coming within a dozen miles of the Eaton fire. “One of the biggest problems was so much smoke in the air,” he said. “And the air quality was so low that it was, in many ways, dangerous for patients to travel.”
“These fires were so aggressive, and they kept popping up,” Wagman said. Plus, the emotional strain of looming wildfires persisted for both patients and cancer clinicians for weeks on end, he added.
For those who evacuate, the logistics can be complex.
Not only are cancer treatment plans highly structured, but switching care to another facility is far from easy, Bernicker said. The new facility will likely need to submit a treatment plan and get insurance coverage before moving forward.
“I’m not saying that takes forever,” he said. “But what I’m saying is that it’s not like you just roll in and they hang the [infusion] bag.”
Neither is a shelter typically an option for patients during treatment, said Seth Berkowitz, a licensed clinical social worker and director of Strategic Healthcare Partnerships at The Leukemia & Lymphoma Society. “They have to have a place to go that’s safe and germ-free.”
In western North Carolina, the strain on already ill patients and their caregivers could be overwhelming, Palmeri said. He recounted how the husband of one patient with advanced cancer died after the storm came through.
“He tried to go out there with a chainsaw to clear a way out so that they could get out of their house in case he needed to take her to the hospital,” Palmeri said. “And he had a heart attack there in the driveway.”
Rebuilding and Planning Ahead
Experts are only at the early stages of grasping the magnitude of extreme weather on cancer care and developing strategies to curtail care gaps and potential harm to patients, said Katie Lichter, MD, a radiation oncologist at the University of California San Francisco, who studies extreme weather and cancer treatment.
“How does it impact health care delivery services at every step, from prevention to screening to treatment and survivorship?” Lichter asked. “We’re just starting to understand and to even quantify that,” she said, which included identifying patients who are most vulnerable. She worries, in particular, about patients living in rural areas who already travel longer distances and often face more difficulties accessing care.
The gap between research and reality still looms large. A recent analysis, led by Lichter, looked at 176 California radiation oncology clinics and found that all of them were located within 25 miles of a wildfire that had occurred within the prior 5 years. Yet among the 51 clinics that responded to a 2022 survey,just 47% reported that their clinic had a wildfire emergency preparedness plan.
The American Cancer Society does provide some guidance on how patients can prepare for a weather-related crisis, including having extra supplies of medications or special equipment on hand.
Still, providers are often in reaction mode when extreme weather strikes.
Without adequate clean water after Helene, leaders at Palmeri’s practice moved swiftly, purchasing 40,000-50,000 bottles of water and bringing in porta potties from elsewhere.
“I think we were able to get things up and going very quickly,” said Palmeri, who noted that full services resumed about 10 days after the storm. “For most patients, missing a week of treatment would not do a disservice to their well-being or outcome.”
Going forward, to provide a more comprehensive strategy, Lichter is working with colleagues to develop clinical tool kits to help oncology practices and patients prepare for severe weather events, such as outlining backup treatment contingency plans, ensuring early medication refills, and boosting communication with patient alert systems.
Clinicians are also implementing their own strategies. To limit communication gaps during power outages, Palmeri said that, since Helene, his practice has made sure that their clinic sites, physicians, and other key people now have cell phone service through satellite via Starlink.
“No one has phone books anymore,” he said, so cancer clinicians should keep crucial contact information on paper, such as details about businesses that distribute water and porta potties, given that online searches may not be feasible.
Clinicians should also advise patients to keep a hard copy of recent medical findings handy, including medications and lab results, in case they arrive at an emergency room far from home and physicians can’t access their electronic health record, Bernicker said.
When there is enough advance warning of an approaching weather event, clinicians can help patients keep at least a week’s worth of medication on hand for symptom-related issues, such as nausea or pain, as well as antibiotics so patients don’t have to seek out emergency care during the crisis, Bernicker said. However, Bernicker noted, some insurers may be reluctant to fill certain prescriptions in advance, like those for opioids.
Making headway on more robust preparedness strategies may be slowed. As of March, the National Institutes of Health will no longer fund research about the health effects of climate change.
Bernicker hoped that such cutbacks would be rolled back. What’s on the line, he stressed, is maintaining the highest quality of care for patients with cancer.
“We really are in a golden age of oncology therapeutics,” he said. “We have patients living longer than anyone would have predicted 20 or 25 years ago. But all those advances are contingent on people having access to their centers and not having that interrupted.”
A version of this article first appeared on Medscape.com.
Many Early-Onset Cancers Increasing, Particularly in Women
Rates of certain cancers in the United States — including breast, colorectal, and thyroid cancers — increased between 2010 and 2019 among patients aged less than 50 years, while overall cancer incidence and mortality rates did not increase, a new study found.
Among the more than two million cases of early-onset cancer diagnosed during this period, 63.2% were in women, researchers reported recently in Cancer Discovery.
Breast cancer, thyroid cancer, and melanoma were the most common early-onset cancers in women. Among men, the most common were colorectal cancer, testicular cancer, and melanoma.
Researchers from the National Cancer Institute analyzed cancer incidence data from the United States Cancer Statistics database for 2010-2019 and national death certificate data from the National Center for Health Statistics from 2010 to 2022. The team excluded incidence data from 2020 and 2021, which was artificially low due to COVID.
The researchers divided the data according to age groups: The early-onset age groups were 15-29, 30-39, and 40-49 years, and the late-onset groups were 50-59, 60-69, and 70-79 years. The team also estimated the expected number of early-onset cases in 2019 by multiplying 2010 age-specific cancer incidence rates by population counts for 2019.
First author Meredith Shiels, of the National Cancer Institute, and colleagues found that the largest absolute increase in incidence of early-onset cancers, compared with expected incidence, were for breast (n = 4834 additional cancers), colorectal (n = 2099), kidney (n = 1793), and uterine cancers (n = 1209). These diagnoses accounted for 80% of the additional cancer diagnoses in 2019 vs 2010.
Looking at increases by age group, Shiels and colleagues reported that 1.9% of all cancers occurred in overall early-onset cohort 15- to 49-year-olds (age-standardized incidence rate of 39.8 per 100,000), and the incidence was greater in the older cohorts: 3.6% for 30- to 39-year-olds (123.5 per 100,000) and 8.8% for 40- to 49-year-olds (293.9 per 100,000).
Overall, 14 of 33 cancer types significantly increased in incidence in at least one early-onset age group. Among these 14 cancer types, five — melanoma, plasma cell neoplasms, cervical cancer, stomach cancer, and cancer of the bones and joints — showed increases only in early-onset age groups, not in late-onset age groups. For example, between 2010 and 2019, cervical cancer rates increased by 1.39% per year among 30- to 39-year-olds, melanoma rates increased by 0.82% per year among 40- to 49-year-olds, and stomach cancer rates increased by 1.38% per year.
The remaining nine cancer types increased in at least one early-onset and one late-onset group. These included female breast, colorectal, kidney, testicular, uterine, pancreatic cancers as well as precursor B-cell non-Hodgkin lymphoma, diffuse large B-cell lymphoma, and mycosis fungoides/Sézary syndrome.
For four of the 14 cancer types with increasing incidence rates — testicular cancer, uterine cancer, colorectal cancer, and cancer of the bones and joints — mortality also increased in at least one early-onset age group, whereas the remaining 10 cancer types increased in incidence without an increase in mortality for any age group.
Shiels and her colleagues aren’t the first to address the rising incidence of early-onset cancers. In a keynote lecture at the European Society of Medical Oncology (ESMO) 2024 Annual Meeting, Irit Ben-Aharon, MD, PhD, from the Rambam Health Care Campus in Haifa, Israel, noted that from 1990-2019, the global incidence of early-onset cancer increased by 79%.
Although the current study doesn’t identify drivers of rising cancer rates in younger patients, “descriptive data like these provide a critical starting point for understanding the drivers of rising rates of cancer in early-onset age groups and could translate to effective cancer prevention and early detection efforts,” Shiels said in a press release. For instance, “recent guidelines have lowered the age of initiation for breast and colorectal cancer screening based, at least partially, on observations that rates for these cancers are increasing at younger ages.”
This study is “a great step forward” toward understanding the increasing incidence of early-onset cancers, agreed Shuji Ogino, MD, PhD, from Harvard Medical School and Brigham and Women’s Hospital in Boston, who wasn’t involved in the research.
The investigators provide new details, particularly by breaking down the early- and late-onset age groups into subcategories and by comparing incidence and mortality rates, Ogino noted.
“Mortality is a great endpoint because if the increased in early incidence is just an effect of [increased] screening we won’t see a mortality increase,” Ogino said. But “we need more data and some way to tease out the screening effect.” Plus, he added, “we need more mechanistic studies and tissue-based analyses to determine if early-onset cancers that are increasing in incidence are a different beast, rather than just an earlier beast.”
This study was funded by the Intramural Research Program of the National Cancer Institute of the National Institutes of Health and the Institute of Cancer Research. Shiels declared no conflicts of interest.
version of this article first appeared on Medscape.com.
Rates of certain cancers in the United States — including breast, colorectal, and thyroid cancers — increased between 2010 and 2019 among patients aged less than 50 years, while overall cancer incidence and mortality rates did not increase, a new study found.
Among the more than two million cases of early-onset cancer diagnosed during this period, 63.2% were in women, researchers reported recently in Cancer Discovery.
Breast cancer, thyroid cancer, and melanoma were the most common early-onset cancers in women. Among men, the most common were colorectal cancer, testicular cancer, and melanoma.
Researchers from the National Cancer Institute analyzed cancer incidence data from the United States Cancer Statistics database for 2010-2019 and national death certificate data from the National Center for Health Statistics from 2010 to 2022. The team excluded incidence data from 2020 and 2021, which was artificially low due to COVID.
The researchers divided the data according to age groups: The early-onset age groups were 15-29, 30-39, and 40-49 years, and the late-onset groups were 50-59, 60-69, and 70-79 years. The team also estimated the expected number of early-onset cases in 2019 by multiplying 2010 age-specific cancer incidence rates by population counts for 2019.
First author Meredith Shiels, of the National Cancer Institute, and colleagues found that the largest absolute increase in incidence of early-onset cancers, compared with expected incidence, were for breast (n = 4834 additional cancers), colorectal (n = 2099), kidney (n = 1793), and uterine cancers (n = 1209). These diagnoses accounted for 80% of the additional cancer diagnoses in 2019 vs 2010.
Looking at increases by age group, Shiels and colleagues reported that 1.9% of all cancers occurred in overall early-onset cohort 15- to 49-year-olds (age-standardized incidence rate of 39.8 per 100,000), and the incidence was greater in the older cohorts: 3.6% for 30- to 39-year-olds (123.5 per 100,000) and 8.8% for 40- to 49-year-olds (293.9 per 100,000).
Overall, 14 of 33 cancer types significantly increased in incidence in at least one early-onset age group. Among these 14 cancer types, five — melanoma, plasma cell neoplasms, cervical cancer, stomach cancer, and cancer of the bones and joints — showed increases only in early-onset age groups, not in late-onset age groups. For example, between 2010 and 2019, cervical cancer rates increased by 1.39% per year among 30- to 39-year-olds, melanoma rates increased by 0.82% per year among 40- to 49-year-olds, and stomach cancer rates increased by 1.38% per year.
The remaining nine cancer types increased in at least one early-onset and one late-onset group. These included female breast, colorectal, kidney, testicular, uterine, pancreatic cancers as well as precursor B-cell non-Hodgkin lymphoma, diffuse large B-cell lymphoma, and mycosis fungoides/Sézary syndrome.
For four of the 14 cancer types with increasing incidence rates — testicular cancer, uterine cancer, colorectal cancer, and cancer of the bones and joints — mortality also increased in at least one early-onset age group, whereas the remaining 10 cancer types increased in incidence without an increase in mortality for any age group.
Shiels and her colleagues aren’t the first to address the rising incidence of early-onset cancers. In a keynote lecture at the European Society of Medical Oncology (ESMO) 2024 Annual Meeting, Irit Ben-Aharon, MD, PhD, from the Rambam Health Care Campus in Haifa, Israel, noted that from 1990-2019, the global incidence of early-onset cancer increased by 79%.
Although the current study doesn’t identify drivers of rising cancer rates in younger patients, “descriptive data like these provide a critical starting point for understanding the drivers of rising rates of cancer in early-onset age groups and could translate to effective cancer prevention and early detection efforts,” Shiels said in a press release. For instance, “recent guidelines have lowered the age of initiation for breast and colorectal cancer screening based, at least partially, on observations that rates for these cancers are increasing at younger ages.”
This study is “a great step forward” toward understanding the increasing incidence of early-onset cancers, agreed Shuji Ogino, MD, PhD, from Harvard Medical School and Brigham and Women’s Hospital in Boston, who wasn’t involved in the research.
The investigators provide new details, particularly by breaking down the early- and late-onset age groups into subcategories and by comparing incidence and mortality rates, Ogino noted.
“Mortality is a great endpoint because if the increased in early incidence is just an effect of [increased] screening we won’t see a mortality increase,” Ogino said. But “we need more data and some way to tease out the screening effect.” Plus, he added, “we need more mechanistic studies and tissue-based analyses to determine if early-onset cancers that are increasing in incidence are a different beast, rather than just an earlier beast.”
This study was funded by the Intramural Research Program of the National Cancer Institute of the National Institutes of Health and the Institute of Cancer Research. Shiels declared no conflicts of interest.
version of this article first appeared on Medscape.com.
Rates of certain cancers in the United States — including breast, colorectal, and thyroid cancers — increased between 2010 and 2019 among patients aged less than 50 years, while overall cancer incidence and mortality rates did not increase, a new study found.
Among the more than two million cases of early-onset cancer diagnosed during this period, 63.2% were in women, researchers reported recently in Cancer Discovery.
Breast cancer, thyroid cancer, and melanoma were the most common early-onset cancers in women. Among men, the most common were colorectal cancer, testicular cancer, and melanoma.
Researchers from the National Cancer Institute analyzed cancer incidence data from the United States Cancer Statistics database for 2010-2019 and national death certificate data from the National Center for Health Statistics from 2010 to 2022. The team excluded incidence data from 2020 and 2021, which was artificially low due to COVID.
The researchers divided the data according to age groups: The early-onset age groups were 15-29, 30-39, and 40-49 years, and the late-onset groups were 50-59, 60-69, and 70-79 years. The team also estimated the expected number of early-onset cases in 2019 by multiplying 2010 age-specific cancer incidence rates by population counts for 2019.
First author Meredith Shiels, of the National Cancer Institute, and colleagues found that the largest absolute increase in incidence of early-onset cancers, compared with expected incidence, were for breast (n = 4834 additional cancers), colorectal (n = 2099), kidney (n = 1793), and uterine cancers (n = 1209). These diagnoses accounted for 80% of the additional cancer diagnoses in 2019 vs 2010.
Looking at increases by age group, Shiels and colleagues reported that 1.9% of all cancers occurred in overall early-onset cohort 15- to 49-year-olds (age-standardized incidence rate of 39.8 per 100,000), and the incidence was greater in the older cohorts: 3.6% for 30- to 39-year-olds (123.5 per 100,000) and 8.8% for 40- to 49-year-olds (293.9 per 100,000).
Overall, 14 of 33 cancer types significantly increased in incidence in at least one early-onset age group. Among these 14 cancer types, five — melanoma, plasma cell neoplasms, cervical cancer, stomach cancer, and cancer of the bones and joints — showed increases only in early-onset age groups, not in late-onset age groups. For example, between 2010 and 2019, cervical cancer rates increased by 1.39% per year among 30- to 39-year-olds, melanoma rates increased by 0.82% per year among 40- to 49-year-olds, and stomach cancer rates increased by 1.38% per year.
The remaining nine cancer types increased in at least one early-onset and one late-onset group. These included female breast, colorectal, kidney, testicular, uterine, pancreatic cancers as well as precursor B-cell non-Hodgkin lymphoma, diffuse large B-cell lymphoma, and mycosis fungoides/Sézary syndrome.
For four of the 14 cancer types with increasing incidence rates — testicular cancer, uterine cancer, colorectal cancer, and cancer of the bones and joints — mortality also increased in at least one early-onset age group, whereas the remaining 10 cancer types increased in incidence without an increase in mortality for any age group.
Shiels and her colleagues aren’t the first to address the rising incidence of early-onset cancers. In a keynote lecture at the European Society of Medical Oncology (ESMO) 2024 Annual Meeting, Irit Ben-Aharon, MD, PhD, from the Rambam Health Care Campus in Haifa, Israel, noted that from 1990-2019, the global incidence of early-onset cancer increased by 79%.
Although the current study doesn’t identify drivers of rising cancer rates in younger patients, “descriptive data like these provide a critical starting point for understanding the drivers of rising rates of cancer in early-onset age groups and could translate to effective cancer prevention and early detection efforts,” Shiels said in a press release. For instance, “recent guidelines have lowered the age of initiation for breast and colorectal cancer screening based, at least partially, on observations that rates for these cancers are increasing at younger ages.”
This study is “a great step forward” toward understanding the increasing incidence of early-onset cancers, agreed Shuji Ogino, MD, PhD, from Harvard Medical School and Brigham and Women’s Hospital in Boston, who wasn’t involved in the research.
The investigators provide new details, particularly by breaking down the early- and late-onset age groups into subcategories and by comparing incidence and mortality rates, Ogino noted.
“Mortality is a great endpoint because if the increased in early incidence is just an effect of [increased] screening we won’t see a mortality increase,” Ogino said. But “we need more data and some way to tease out the screening effect.” Plus, he added, “we need more mechanistic studies and tissue-based analyses to determine if early-onset cancers that are increasing in incidence are a different beast, rather than just an earlier beast.”
This study was funded by the Intramural Research Program of the National Cancer Institute of the National Institutes of Health and the Institute of Cancer Research. Shiels declared no conflicts of interest.
version of this article first appeared on Medscape.com.
Collins Lays Out Plans to Reduce VA by 15% in Congressional Hearings
Collins Lays Out Plans to Reduce VA by 15% in Senate Hearing
US Department of Veterans Affairs (VA) Secretary Doug Collins testified in US House of Representatives and US Senate committees hearings that bringing staff numbers down to fiscal year 2019 figures was simply a goal: “Our goal, as we look at it, as everything goes forward, is a 15% decrease,” he told the senators. “It’s a goal. You have to start somewhere.”
“It’s a process we’re going through and I’m not going to work out a process in front of a committee or anywhere else,” Collins testified in the Senate on May 6, adding that it would be “incompetence” or “malpractice” to do so before time. “[When] we’re doing something as large as we are in an organization as sensitive on this Hill, it would not be right for us to do that in public. It would not be right for us to just come out and say here’s everything that we got and then have everybody scared because in the end it may not be the final decision.”
“We’re going to come to the best possible decision we can for the veterans in this country so they can have a VA system that actually works,” Collins argued in the Senate. “The VA’s been an issue for a long time. We’re trying to not make it an issue anymore.”
Collins later told a House committee on May 15 that VA was conducting a thorough review of department structure and staffing across the enterpise. "Our goal is to increase productivity and efficiency and to eliminate waste and bureaucracy improving health care delivery and benefits to our veterans. We are going to maintain VA essential jobs like doctors and nurses and claims processors" but eliminate positions it deemed "nonmission-critical" and consolidating areas of "overlap and waste."
Senate ranking member Richard Blumenthal (D-CT) and Chairman Jerry Moran (R-KS) both placed an emphasis on accountability for responsible resizing at the hearing.
“The department is at a critical juncture,” Moran said. “Perhaps that’s always true, and I want to hear from you that the changes under way at the VA are backed by data, informed by veteran demand, focused on improving outcomes for men and women the VA serves, and will be carried out in close coordination with this committee, as well as with veterans, VA staff, and veteran organizations.” Moran stressed that cutting should be about right-sizing, done carefully, and while treating people “with gratitude and respect.”
Blumenthal was more direct in his criticism of the approach: “You cannot slash and trash the VA without eliminating those essential positions which provide access and availability of health care. It simply cannot be done,” he told Collins.
In response, Collins replied, “You have stated on several occasions already that I am saying we are going to fire 83,000 employees. That is wrong.” Collins insisted that the VA was “looking at a goal of how many employees we have and how many employees that are actually working in the front line taking care. I have doctors and nurses right now that do not see patients. Is that helping veteran health care?”
Collins defended the actions of the VA and spoke about challenges he was “constantly fighting” in the early weeks of his tenure. “We’ve been hit by a barrage of false rumors, innuendo, disinformation, speculation implying firing doctors and nurses, and forcing staff to work in closets and showers and that there’s chaos in the department, none of which have been backed up. Why? Because we canceled some contracts that worked for the VA that we should be doing in-house and we let go of less than one half of one percent of nonmission critical employees.”
The Trump Administration offered federal employees the option of resigning, which purportedly will go toward meeting the 15% target. NPR reported that VA employees have since shared data showing that 11,273 agency employees nationwide have applied for deferred resignation. Most of those employees are nurses (about 1300), medical support assistants (about 800), and social workers (about 300).
Collins stressed that the aim of restructuring was to protect veterans’ health care. By getting rid of DEI initiatives, the VA saved $14 million, which he said was redirected to veterans with disabilities who need prosthetics.
Sen. Bernie Sanders (D-VT) addressed concerns about the existing shortage of clinicians at the VA, asking Collins what he was doing to bring in more doctors, nurses, and social workers. In addition to moving doctors and nurses from nonpatient care to patient care, Collins said, he planned to work with Congress to make salaries more competitive.
But money and adding more employees are not always the solution, Collins said. For example, he said, the VA has been spending $588 million a year veteran suicide research, its top clinical priority. Yet, he said there has not been a significant decrease in veteran suicide rates since 2008.
The most recent VA suicide report, released in 2024, indicates suicide rates have remained steady since 2001. However, in 2022, the number of suicides among veterans (6407) was actually lower than in 12 of the previous 14 years.
According to media reports, congressional lawmakers, and union officials, Veteran Crisis Line (VCL) staff were among the 2400 probationary employees fired in February. In a Feb. 20 video, Collins accused Democrats of spreading lies and insisted no one who answered the phone was fired.
Later, in a letter to senators, Collins admitted that 24 VCL support staff were “erroneously” sent termination notices. The firings were later reversed, Collins said, and all VCL employees had been reinstated at the same position they previously held. “Ensuring the VCL is always accessible 24/7 is one of the department’s top priorities,” Collins insisted.
Collins shared his approval of keeping and expanding VA programs and studies on psychedelic treatments for patients with posttraumatic stress disorder and traumatic brain injury. He also spoke to the proposed 2026 budget calling for a $5.4 billion increase for the VA. If approved, that money would be targeted for medical care and homelessness.
US Department of Veterans Affairs (VA) Secretary Doug Collins testified in US House of Representatives and US Senate committees hearings that bringing staff numbers down to fiscal year 2019 figures was simply a goal: “Our goal, as we look at it, as everything goes forward, is a 15% decrease,” he told the senators. “It’s a goal. You have to start somewhere.”
“It’s a process we’re going through and I’m not going to work out a process in front of a committee or anywhere else,” Collins testified in the Senate on May 6, adding that it would be “incompetence” or “malpractice” to do so before time. “[When] we’re doing something as large as we are in an organization as sensitive on this Hill, it would not be right for us to do that in public. It would not be right for us to just come out and say here’s everything that we got and then have everybody scared because in the end it may not be the final decision.”
“We’re going to come to the best possible decision we can for the veterans in this country so they can have a VA system that actually works,” Collins argued in the Senate. “The VA’s been an issue for a long time. We’re trying to not make it an issue anymore.”
Collins later told a House committee on May 15 that VA was conducting a thorough review of department structure and staffing across the enterpise. "Our goal is to increase productivity and efficiency and to eliminate waste and bureaucracy improving health care delivery and benefits to our veterans. We are going to maintain VA essential jobs like doctors and nurses and claims processors" but eliminate positions it deemed "nonmission-critical" and consolidating areas of "overlap and waste."
Senate ranking member Richard Blumenthal (D-CT) and Chairman Jerry Moran (R-KS) both placed an emphasis on accountability for responsible resizing at the hearing.
“The department is at a critical juncture,” Moran said. “Perhaps that’s always true, and I want to hear from you that the changes under way at the VA are backed by data, informed by veteran demand, focused on improving outcomes for men and women the VA serves, and will be carried out in close coordination with this committee, as well as with veterans, VA staff, and veteran organizations.” Moran stressed that cutting should be about right-sizing, done carefully, and while treating people “with gratitude and respect.”
Blumenthal was more direct in his criticism of the approach: “You cannot slash and trash the VA without eliminating those essential positions which provide access and availability of health care. It simply cannot be done,” he told Collins.
In response, Collins replied, “You have stated on several occasions already that I am saying we are going to fire 83,000 employees. That is wrong.” Collins insisted that the VA was “looking at a goal of how many employees we have and how many employees that are actually working in the front line taking care. I have doctors and nurses right now that do not see patients. Is that helping veteran health care?”
Collins defended the actions of the VA and spoke about challenges he was “constantly fighting” in the early weeks of his tenure. “We’ve been hit by a barrage of false rumors, innuendo, disinformation, speculation implying firing doctors and nurses, and forcing staff to work in closets and showers and that there’s chaos in the department, none of which have been backed up. Why? Because we canceled some contracts that worked for the VA that we should be doing in-house and we let go of less than one half of one percent of nonmission critical employees.”
The Trump Administration offered federal employees the option of resigning, which purportedly will go toward meeting the 15% target. NPR reported that VA employees have since shared data showing that 11,273 agency employees nationwide have applied for deferred resignation. Most of those employees are nurses (about 1300), medical support assistants (about 800), and social workers (about 300).
Collins stressed that the aim of restructuring was to protect veterans’ health care. By getting rid of DEI initiatives, the VA saved $14 million, which he said was redirected to veterans with disabilities who need prosthetics.
Sen. Bernie Sanders (D-VT) addressed concerns about the existing shortage of clinicians at the VA, asking Collins what he was doing to bring in more doctors, nurses, and social workers. In addition to moving doctors and nurses from nonpatient care to patient care, Collins said, he planned to work with Congress to make salaries more competitive.
But money and adding more employees are not always the solution, Collins said. For example, he said, the VA has been spending $588 million a year veteran suicide research, its top clinical priority. Yet, he said there has not been a significant decrease in veteran suicide rates since 2008.
The most recent VA suicide report, released in 2024, indicates suicide rates have remained steady since 2001. However, in 2022, the number of suicides among veterans (6407) was actually lower than in 12 of the previous 14 years.
According to media reports, congressional lawmakers, and union officials, Veteran Crisis Line (VCL) staff were among the 2400 probationary employees fired in February. In a Feb. 20 video, Collins accused Democrats of spreading lies and insisted no one who answered the phone was fired.
Later, in a letter to senators, Collins admitted that 24 VCL support staff were “erroneously” sent termination notices. The firings were later reversed, Collins said, and all VCL employees had been reinstated at the same position they previously held. “Ensuring the VCL is always accessible 24/7 is one of the department’s top priorities,” Collins insisted.
Collins shared his approval of keeping and expanding VA programs and studies on psychedelic treatments for patients with posttraumatic stress disorder and traumatic brain injury. He also spoke to the proposed 2026 budget calling for a $5.4 billion increase for the VA. If approved, that money would be targeted for medical care and homelessness.
US Department of Veterans Affairs (VA) Secretary Doug Collins testified in US House of Representatives and US Senate committees hearings that bringing staff numbers down to fiscal year 2019 figures was simply a goal: “Our goal, as we look at it, as everything goes forward, is a 15% decrease,” he told the senators. “It’s a goal. You have to start somewhere.”
“It’s a process we’re going through and I’m not going to work out a process in front of a committee or anywhere else,” Collins testified in the Senate on May 6, adding that it would be “incompetence” or “malpractice” to do so before time. “[When] we’re doing something as large as we are in an organization as sensitive on this Hill, it would not be right for us to do that in public. It would not be right for us to just come out and say here’s everything that we got and then have everybody scared because in the end it may not be the final decision.”
“We’re going to come to the best possible decision we can for the veterans in this country so they can have a VA system that actually works,” Collins argued in the Senate. “The VA’s been an issue for a long time. We’re trying to not make it an issue anymore.”
Collins later told a House committee on May 15 that VA was conducting a thorough review of department structure and staffing across the enterpise. "Our goal is to increase productivity and efficiency and to eliminate waste and bureaucracy improving health care delivery and benefits to our veterans. We are going to maintain VA essential jobs like doctors and nurses and claims processors" but eliminate positions it deemed "nonmission-critical" and consolidating areas of "overlap and waste."
Senate ranking member Richard Blumenthal (D-CT) and Chairman Jerry Moran (R-KS) both placed an emphasis on accountability for responsible resizing at the hearing.
“The department is at a critical juncture,” Moran said. “Perhaps that’s always true, and I want to hear from you that the changes under way at the VA are backed by data, informed by veteran demand, focused on improving outcomes for men and women the VA serves, and will be carried out in close coordination with this committee, as well as with veterans, VA staff, and veteran organizations.” Moran stressed that cutting should be about right-sizing, done carefully, and while treating people “with gratitude and respect.”
Blumenthal was more direct in his criticism of the approach: “You cannot slash and trash the VA without eliminating those essential positions which provide access and availability of health care. It simply cannot be done,” he told Collins.
In response, Collins replied, “You have stated on several occasions already that I am saying we are going to fire 83,000 employees. That is wrong.” Collins insisted that the VA was “looking at a goal of how many employees we have and how many employees that are actually working in the front line taking care. I have doctors and nurses right now that do not see patients. Is that helping veteran health care?”
Collins defended the actions of the VA and spoke about challenges he was “constantly fighting” in the early weeks of his tenure. “We’ve been hit by a barrage of false rumors, innuendo, disinformation, speculation implying firing doctors and nurses, and forcing staff to work in closets and showers and that there’s chaos in the department, none of which have been backed up. Why? Because we canceled some contracts that worked for the VA that we should be doing in-house and we let go of less than one half of one percent of nonmission critical employees.”
The Trump Administration offered federal employees the option of resigning, which purportedly will go toward meeting the 15% target. NPR reported that VA employees have since shared data showing that 11,273 agency employees nationwide have applied for deferred resignation. Most of those employees are nurses (about 1300), medical support assistants (about 800), and social workers (about 300).
Collins stressed that the aim of restructuring was to protect veterans’ health care. By getting rid of DEI initiatives, the VA saved $14 million, which he said was redirected to veterans with disabilities who need prosthetics.
Sen. Bernie Sanders (D-VT) addressed concerns about the existing shortage of clinicians at the VA, asking Collins what he was doing to bring in more doctors, nurses, and social workers. In addition to moving doctors and nurses from nonpatient care to patient care, Collins said, he planned to work with Congress to make salaries more competitive.
But money and adding more employees are not always the solution, Collins said. For example, he said, the VA has been spending $588 million a year veteran suicide research, its top clinical priority. Yet, he said there has not been a significant decrease in veteran suicide rates since 2008.
The most recent VA suicide report, released in 2024, indicates suicide rates have remained steady since 2001. However, in 2022, the number of suicides among veterans (6407) was actually lower than in 12 of the previous 14 years.
According to media reports, congressional lawmakers, and union officials, Veteran Crisis Line (VCL) staff were among the 2400 probationary employees fired in February. In a Feb. 20 video, Collins accused Democrats of spreading lies and insisted no one who answered the phone was fired.
Later, in a letter to senators, Collins admitted that 24 VCL support staff were “erroneously” sent termination notices. The firings were later reversed, Collins said, and all VCL employees had been reinstated at the same position they previously held. “Ensuring the VCL is always accessible 24/7 is one of the department’s top priorities,” Collins insisted.
Collins shared his approval of keeping and expanding VA programs and studies on psychedelic treatments for patients with posttraumatic stress disorder and traumatic brain injury. He also spoke to the proposed 2026 budget calling for a $5.4 billion increase for the VA. If approved, that money would be targeted for medical care and homelessness.
Collins Lays Out Plans to Reduce VA by 15% in Senate Hearing
Collins Lays Out Plans to Reduce VA by 15% in Senate Hearing
Impact of Multisite Patient Education on Pharmacotherapy for Veterans With Alcohol Use Disorder
Impact of Multisite Patient Education on Pharmacotherapy for Veterans With Alcohol Use Disorder
Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.1Alcohol-related deaths increased by 49% between 2006 and 2019.2 This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.3 This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.4
Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.5
These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).
According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.8 Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.9-11
Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.12
A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.13 At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.
ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. 15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. 17 This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.
METHODS
A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.18 Prescriptions filled outside the VA were not included in this dashboard.
Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.
Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.
Education
The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.
A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.
The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.
Matching and Analysis
A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).19

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.
RESULTS
Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).



DISCUSSION
This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.
Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.20 Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.19 Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.21 These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.22
Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.23
There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. 15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.
Strengths and Limitations
This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.24
The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.
There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.
The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.
CONCLUSIONS
Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.
- Centers for Disease Control and Prevention. Facts about U.S. deaths from excessive alcohol use. August 6, 2024. Accessed February 5, 2025. https://www.cdc.gov/alcohol/facts-stats/
- State Health Access Data Assistance Center. Escalating alcohol-involved death rates: trends and variation across the nation and in the states from 2006 to 2019. April 19, 2021. Accessed February 5, 2025. https://www.shadac.org/escalating-alcohol-involved-death-rates-trends-and-variation-across-nation-and-states-2006-2019
- National Institute on Alcohol Abuse and Alcoholism. Alcohol- related emergencies and deaths in the United States. Updated November 2024. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-related-emergencies-and-deaths-united-states
- Esser MB, Sherk A, Liu Y, Naimi TS. Deaths from excessive alcohol use - United States, 2016- 2021. MMWR Morb Mortal Wkly Rep. 2024;73(8):154-161. doi:10.15585/mmwr.mm7308a1
- Canver BR, Newman RK, Gomez AE. Alcohol Withdrawal Syndrome. In: StatPearls. StatPearls Publishing; 2024.
- National Institute on Alcohol Abuse and Alcoholism. Alcohol treatment in the United States. Updated January 2025. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-treatment-united-states
- National Institute on Alcohol Abuse and Alcoholism. Alcohol use disorder (AUD) in the United States: age groups and demographic characteristics. Updated September 2024. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-use-disorder-aud-united-states-age-groups-and-demographic-characteristics
- Reus VI, Fochtmann LJ, Bukstein O, et al. The American Psychiatric Association practice guideline for the pharmacological treatment of patients with alcohol use disorder. Am J Psychiatry. 2018;175(1):86-90. doi:10.1176/appi.ajp.2017.1750101
- Blodgett JC, Del Re AC, Maisel NC, Finney JW. A meta-analysis of topiramate’s effects for individuals with alcohol use disorders. Alcohol Clin Exp Res. 2014;38(6):1481-1488. doi:10.1111/acer.12411
- Maisel NC, Blodgett JC, Wilbourne PL, Humphreys K, Finney JW. Meta-analysis of naltrexone and acamprosate for treating alcohol use disorders: when are these medications most helpful? Addiction. 2013;108(2):275-293. doi:10.1111/j.1360-0443.2012.04054.x
- Jonas DE, Amick HR, Feltner C, et al. Pharmacotherapy for adults with alcohol use disorders in outpatient settings: a systematic review and meta-analysis. JAMA. 2014;311(18):1889-1900. doi:10.1001/jama.2014.3628
- US Department of Veterans Affairs, Department of Defense. VA/DoD clinical practice guideline for the management of substance use disorders. August 2021. Accessed February 5, 2025. https://www.healthquality.va.gov/guidelines/MH/sud/VADODSUDCPG.pdf
- Ranney RM, Bernhard PA, Vogt D, et al. Alcohol use and treatment utilization in a national sample of veterans and nonveterans. J Subst Use Addict Treat. 2023;146:208964. doi:10.1016/j.josat.2023.208964
- US Department of Veterans Affairs, Pharmacy Benefit Management Service, Academic Detailing Service. AUD Trend Report. https://vaww.pbi.cdw.va.gov/PBIRS/Pages/ReportViewer.aspx?/GPE/PBM_AD/SSRS/AUD/AUD_TrendReport
- Mendes MA, Smith JP, Marin JK, et al. Reducing benzodiazepine prescribing in older veterans: a direct-to-consumer educational brochure. Fed Pract. 2018;35(9):36-43.
- Tannenbaum C, Martin P, Tamblyn R, Benedetti A, Ahmed S. Reduction of inappropriate benzodiazepine prescriptions among older adults through direct patient education: the EMPOWER cluster randomized trial. JAMA Intern Med. 2014;174(6):890-898. doi:10.1001/jamainternmed.2014.949
- Maloney R, Funmilayo M. Acting on the AUDIT-C: implementation of direct-to-consumer education on unhealth alcohol use. Presented on March 31, 2023; Central Virginia Veterans Affairs Health Care System, Richmond, Virginia.
- US Department of Veterans Affairs, Pharmacy Benefit Management Service. Alcohol use disorder (AUD) – leading the charge in the treatment of AUD: a VA clinician’s guide. February 2022. Accessed February 5, 2025. https://www.pbm.va.gov/PBM/AcademicDetailingService/Documents/508/10-1530_AUD_ClinicianGuide_508Conformant.pdf
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi:10.1080/00273171.2011.568786
- National Institute on Alcohol Abuse and Alcoholism. Stigma: overcoming a pervasive barrier to optimal care. Updated January 6, 2025. Accessed February 5, 2025. https://www.niaaa.nih.gov/health-professionals-communities/core-resource-on-alcohol/stigma-overcoming-pervasive-barrier-optimal-care
- Sudhinaraset M, Wigglesworth C, Takeuchi DT. Social and cultural contexts of alcohol use: influences in a socialecological framework. Alcohol Res. 2016;38(1):35-45.
- Tanski SE, McClure AC, Li Z, et al. Cued recall of alcohol advertising on television and underage drinking behavior. JAMA Pediatr. 2015;169(3):264-271. doi:10.1001/jamapediatrics.2014.3345
- Hyland CJ, McDowell MJ, Bain PA, Huskamp HA, Busch AB. Integration of pharmacotherapy for alcohol use disorder treatment in primary care settings: a scoping review. J Subst Abuse Treat. 2023;144:108919. doi:10.1016/j.jsat.2022.108919
- National Institute on Alcohol Abuse and Alcoholism. The truth about holiday spirits. Updated November 2023. Accessed February 5, 2025. ,a href="https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/truth-about-holiday-spirits">https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/truth-about-holiday-spirits
Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.1Alcohol-related deaths increased by 49% between 2006 and 2019.2 This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.3 This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.4
Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.5
These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).
According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.8 Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.9-11
Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.12
A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.13 At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.
ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. 15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. 17 This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.
METHODS
A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.18 Prescriptions filled outside the VA were not included in this dashboard.
Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.
Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.
Education
The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.
A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.
The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.
Matching and Analysis
A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).19

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.
RESULTS
Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).



DISCUSSION
This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.
Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.20 Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.19 Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.21 These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.22
Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.23
There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. 15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.
Strengths and Limitations
This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.24
The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.
There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.
The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.
CONCLUSIONS
Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.
Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.1Alcohol-related deaths increased by 49% between 2006 and 2019.2 This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.3 This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.4
Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.5
These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).
According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.8 Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.9-11
Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.12
A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.13 At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.
ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. 15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. 17 This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.
METHODS
A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.18 Prescriptions filled outside the VA were not included in this dashboard.
Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.
Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.
Education
The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.
A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.
The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.
Matching and Analysis
A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).19

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.
RESULTS
Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).



DISCUSSION
This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.
Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.20 Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.19 Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.21 These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.22
Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.23
There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. 15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.
Strengths and Limitations
This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.24
The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.
There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.
The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.
CONCLUSIONS
Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.
- Centers for Disease Control and Prevention. Facts about U.S. deaths from excessive alcohol use. August 6, 2024. Accessed February 5, 2025. https://www.cdc.gov/alcohol/facts-stats/
- State Health Access Data Assistance Center. Escalating alcohol-involved death rates: trends and variation across the nation and in the states from 2006 to 2019. April 19, 2021. Accessed February 5, 2025. https://www.shadac.org/escalating-alcohol-involved-death-rates-trends-and-variation-across-nation-and-states-2006-2019
- National Institute on Alcohol Abuse and Alcoholism. Alcohol- related emergencies and deaths in the United States. Updated November 2024. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-related-emergencies-and-deaths-united-states
- Esser MB, Sherk A, Liu Y, Naimi TS. Deaths from excessive alcohol use - United States, 2016- 2021. MMWR Morb Mortal Wkly Rep. 2024;73(8):154-161. doi:10.15585/mmwr.mm7308a1
- Canver BR, Newman RK, Gomez AE. Alcohol Withdrawal Syndrome. In: StatPearls. StatPearls Publishing; 2024.
- National Institute on Alcohol Abuse and Alcoholism. Alcohol treatment in the United States. Updated January 2025. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-treatment-united-states
- National Institute on Alcohol Abuse and Alcoholism. Alcohol use disorder (AUD) in the United States: age groups and demographic characteristics. Updated September 2024. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-use-disorder-aud-united-states-age-groups-and-demographic-characteristics
- Reus VI, Fochtmann LJ, Bukstein O, et al. The American Psychiatric Association practice guideline for the pharmacological treatment of patients with alcohol use disorder. Am J Psychiatry. 2018;175(1):86-90. doi:10.1176/appi.ajp.2017.1750101
- Blodgett JC, Del Re AC, Maisel NC, Finney JW. A meta-analysis of topiramate’s effects for individuals with alcohol use disorders. Alcohol Clin Exp Res. 2014;38(6):1481-1488. doi:10.1111/acer.12411
- Maisel NC, Blodgett JC, Wilbourne PL, Humphreys K, Finney JW. Meta-analysis of naltrexone and acamprosate for treating alcohol use disorders: when are these medications most helpful? Addiction. 2013;108(2):275-293. doi:10.1111/j.1360-0443.2012.04054.x
- Jonas DE, Amick HR, Feltner C, et al. Pharmacotherapy for adults with alcohol use disorders in outpatient settings: a systematic review and meta-analysis. JAMA. 2014;311(18):1889-1900. doi:10.1001/jama.2014.3628
- US Department of Veterans Affairs, Department of Defense. VA/DoD clinical practice guideline for the management of substance use disorders. August 2021. Accessed February 5, 2025. https://www.healthquality.va.gov/guidelines/MH/sud/VADODSUDCPG.pdf
- Ranney RM, Bernhard PA, Vogt D, et al. Alcohol use and treatment utilization in a national sample of veterans and nonveterans. J Subst Use Addict Treat. 2023;146:208964. doi:10.1016/j.josat.2023.208964
- US Department of Veterans Affairs, Pharmacy Benefit Management Service, Academic Detailing Service. AUD Trend Report. https://vaww.pbi.cdw.va.gov/PBIRS/Pages/ReportViewer.aspx?/GPE/PBM_AD/SSRS/AUD/AUD_TrendReport
- Mendes MA, Smith JP, Marin JK, et al. Reducing benzodiazepine prescribing in older veterans: a direct-to-consumer educational brochure. Fed Pract. 2018;35(9):36-43.
- Tannenbaum C, Martin P, Tamblyn R, Benedetti A, Ahmed S. Reduction of inappropriate benzodiazepine prescriptions among older adults through direct patient education: the EMPOWER cluster randomized trial. JAMA Intern Med. 2014;174(6):890-898. doi:10.1001/jamainternmed.2014.949
- Maloney R, Funmilayo M. Acting on the AUDIT-C: implementation of direct-to-consumer education on unhealth alcohol use. Presented on March 31, 2023; Central Virginia Veterans Affairs Health Care System, Richmond, Virginia.
- US Department of Veterans Affairs, Pharmacy Benefit Management Service. Alcohol use disorder (AUD) – leading the charge in the treatment of AUD: a VA clinician’s guide. February 2022. Accessed February 5, 2025. https://www.pbm.va.gov/PBM/AcademicDetailingService/Documents/508/10-1530_AUD_ClinicianGuide_508Conformant.pdf
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi:10.1080/00273171.2011.568786
- National Institute on Alcohol Abuse and Alcoholism. Stigma: overcoming a pervasive barrier to optimal care. Updated January 6, 2025. Accessed February 5, 2025. https://www.niaaa.nih.gov/health-professionals-communities/core-resource-on-alcohol/stigma-overcoming-pervasive-barrier-optimal-care
- Sudhinaraset M, Wigglesworth C, Takeuchi DT. Social and cultural contexts of alcohol use: influences in a socialecological framework. Alcohol Res. 2016;38(1):35-45.
- Tanski SE, McClure AC, Li Z, et al. Cued recall of alcohol advertising on television and underage drinking behavior. JAMA Pediatr. 2015;169(3):264-271. doi:10.1001/jamapediatrics.2014.3345
- Hyland CJ, McDowell MJ, Bain PA, Huskamp HA, Busch AB. Integration of pharmacotherapy for alcohol use disorder treatment in primary care settings: a scoping review. J Subst Abuse Treat. 2023;144:108919. doi:10.1016/j.jsat.2022.108919
- National Institute on Alcohol Abuse and Alcoholism. The truth about holiday spirits. Updated November 2023. Accessed February 5, 2025. ,a href="https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/truth-about-holiday-spirits">https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/truth-about-holiday-spirits
- Centers for Disease Control and Prevention. Facts about U.S. deaths from excessive alcohol use. August 6, 2024. Accessed February 5, 2025. https://www.cdc.gov/alcohol/facts-stats/
- State Health Access Data Assistance Center. Escalating alcohol-involved death rates: trends and variation across the nation and in the states from 2006 to 2019. April 19, 2021. Accessed February 5, 2025. https://www.shadac.org/escalating-alcohol-involved-death-rates-trends-and-variation-across-nation-and-states-2006-2019
- National Institute on Alcohol Abuse and Alcoholism. Alcohol- related emergencies and deaths in the United States. Updated November 2024. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-related-emergencies-and-deaths-united-states
- Esser MB, Sherk A, Liu Y, Naimi TS. Deaths from excessive alcohol use - United States, 2016- 2021. MMWR Morb Mortal Wkly Rep. 2024;73(8):154-161. doi:10.15585/mmwr.mm7308a1
- Canver BR, Newman RK, Gomez AE. Alcohol Withdrawal Syndrome. In: StatPearls. StatPearls Publishing; 2024.
- National Institute on Alcohol Abuse and Alcoholism. Alcohol treatment in the United States. Updated January 2025. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-treatment-united-states
- National Institute on Alcohol Abuse and Alcoholism. Alcohol use disorder (AUD) in the United States: age groups and demographic characteristics. Updated September 2024. Accessed February 5, 2025. https://www.niaaa.nih.gov/alcohols-effects-health/alcohol-topics/alcohol-facts-and-statistics/alcohol-use-disorder-aud-united-states-age-groups-and-demographic-characteristics
- Reus VI, Fochtmann LJ, Bukstein O, et al. The American Psychiatric Association practice guideline for the pharmacological treatment of patients with alcohol use disorder. Am J Psychiatry. 2018;175(1):86-90. doi:10.1176/appi.ajp.2017.1750101
- Blodgett JC, Del Re AC, Maisel NC, Finney JW. A meta-analysis of topiramate’s effects for individuals with alcohol use disorders. Alcohol Clin Exp Res. 2014;38(6):1481-1488. doi:10.1111/acer.12411
- Maisel NC, Blodgett JC, Wilbourne PL, Humphreys K, Finney JW. Meta-analysis of naltrexone and acamprosate for treating alcohol use disorders: when are these medications most helpful? Addiction. 2013;108(2):275-293. doi:10.1111/j.1360-0443.2012.04054.x
- Jonas DE, Amick HR, Feltner C, et al. Pharmacotherapy for adults with alcohol use disorders in outpatient settings: a systematic review and meta-analysis. JAMA. 2014;311(18):1889-1900. doi:10.1001/jama.2014.3628
- US Department of Veterans Affairs, Department of Defense. VA/DoD clinical practice guideline for the management of substance use disorders. August 2021. Accessed February 5, 2025. https://www.healthquality.va.gov/guidelines/MH/sud/VADODSUDCPG.pdf
- Ranney RM, Bernhard PA, Vogt D, et al. Alcohol use and treatment utilization in a national sample of veterans and nonveterans. J Subst Use Addict Treat. 2023;146:208964. doi:10.1016/j.josat.2023.208964
- US Department of Veterans Affairs, Pharmacy Benefit Management Service, Academic Detailing Service. AUD Trend Report. https://vaww.pbi.cdw.va.gov/PBIRS/Pages/ReportViewer.aspx?/GPE/PBM_AD/SSRS/AUD/AUD_TrendReport
- Mendes MA, Smith JP, Marin JK, et al. Reducing benzodiazepine prescribing in older veterans: a direct-to-consumer educational brochure. Fed Pract. 2018;35(9):36-43.
- Tannenbaum C, Martin P, Tamblyn R, Benedetti A, Ahmed S. Reduction of inappropriate benzodiazepine prescriptions among older adults through direct patient education: the EMPOWER cluster randomized trial. JAMA Intern Med. 2014;174(6):890-898. doi:10.1001/jamainternmed.2014.949
- Maloney R, Funmilayo M. Acting on the AUDIT-C: implementation of direct-to-consumer education on unhealth alcohol use. Presented on March 31, 2023; Central Virginia Veterans Affairs Health Care System, Richmond, Virginia.
- US Department of Veterans Affairs, Pharmacy Benefit Management Service. Alcohol use disorder (AUD) – leading the charge in the treatment of AUD: a VA clinician’s guide. February 2022. Accessed February 5, 2025. https://www.pbm.va.gov/PBM/AcademicDetailingService/Documents/508/10-1530_AUD_ClinicianGuide_508Conformant.pdf
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi:10.1080/00273171.2011.568786
- National Institute on Alcohol Abuse and Alcoholism. Stigma: overcoming a pervasive barrier to optimal care. Updated January 6, 2025. Accessed February 5, 2025. https://www.niaaa.nih.gov/health-professionals-communities/core-resource-on-alcohol/stigma-overcoming-pervasive-barrier-optimal-care
- Sudhinaraset M, Wigglesworth C, Takeuchi DT. Social and cultural contexts of alcohol use: influences in a socialecological framework. Alcohol Res. 2016;38(1):35-45.
- Tanski SE, McClure AC, Li Z, et al. Cued recall of alcohol advertising on television and underage drinking behavior. JAMA Pediatr. 2015;169(3):264-271. doi:10.1001/jamapediatrics.2014.3345
- Hyland CJ, McDowell MJ, Bain PA, Huskamp HA, Busch AB. Integration of pharmacotherapy for alcohol use disorder treatment in primary care settings: a scoping review. J Subst Abuse Treat. 2023;144:108919. doi:10.1016/j.jsat.2022.108919
- National Institute on Alcohol Abuse and Alcoholism. The truth about holiday spirits. Updated November 2023. Accessed February 5, 2025. ,a href="https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/truth-about-holiday-spirits">https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/truth-about-holiday-spirits
Impact of Multisite Patient Education on Pharmacotherapy for Veterans With Alcohol Use Disorder
Impact of Multisite Patient Education on Pharmacotherapy for Veterans With Alcohol Use Disorder
Improved Pharmacogenomic Testing Process for Veterans in Outpatient Settings by Clinical Pharmacist Practitioners
Peer-review, evidence-based, detailed gene/drug clinical practice guidelines suggest that genetic variations can impact how individuals metabolize medications, which is sometimes included in medication prescribing information.1-3 Pharmacogenomic testing identifies genetic markers so medication selection and dosing can be tailored to each individual by identifying whether a specific medication is likely to be safe and effective prior to prescribing.4
Pharmacogenomics can be a valuable tool for personalizing medicine but has had suboptimal implementation since its discovery. The US Department of Veterans Affairs (VA) health care system reviewed the implementation of the Pharmacogenomic Testing for Veterans (PHASER) program. This review identified clinician barriers pre- and post-PHASER program implementation; staffing issues, competing clinical priorities, and inadequate PHASER program resources were the most frequently reported barriers to implementation of pharmacogenomic testing.5
Another evaluation of the implementation of the PHASER program that surveyed VA patients found that patients could be separated into 3 groups. Acceptors of pharmacogenomic testing emphasized potential health benefits of testing. Patients that declined testing often cited concerns for genetic information affecting insurance coverage, being misused, or being susceptible to data breach. The third group—identified as contemplators—reported the need for clinician outreach to impact their decision on whether or not to receive pharmacogenomic testing.6 These studies suggest that removing barriers by providing ample pharmacogenomics resources to clinicians, in addition to detailed training on how to offer and follow up with patients regarding pharmacogenomic testing, is crucial to successful implementation of the PHASER program.
PHASER
In 2019, the VA began working with Sanford Health to establish the PHASER program and offer pharmacogenomic testing. PHASER has since expanded to 25 VA medical centers, including the VA Central Ohio Healthcare System (VACOHCS).7,8 Pharmacogenomic testing through PHASER is conducted using a standardized laboratory panel that includes 12 different medication classes.9 The drug classes include certain anti-infective, anticoagulant, antiplatelet, cardiovascular, cholesterol, gastrointestinal, mental health, neurological, oncology, pain, transplant, and other miscellaneous medications. Medications are correlated to each class and assessed for therapeutic impacts based on gene panel results.
Clinical recommendations for medication-gene interactions can range from monitoring for increased risk of adverse effects or therapeutic failure to recommending avoiding a medication. For example, patients who test positive for the HLA-B gene have significantly increased risk of hypersensitivity to abacavir, an HIV treatment.10
Similarly, patients who cannot adequately metabolize cytochrome P450 2C19 should consider avoiding clopidogrel as they are unlikely to convert clopidogrel to its active prodrug, which reduces its effectiveness.11 Pharmacists can play a critical role educating patients about pharmacogenomic testing, especially within hematology and oncology.12 Patients can benefit from this testing even if they are not currently taking medications with known concerns as they could be prescribed in the future. The SLCO1B1 gene-drug test, for example, can identify risk for statin-associated muscle symptoms.13
Clinical pharmacist practitioners (CPPs) can increase access to genetic testing because they interact with patients in a variety of settings and can order this laboratory test.12,14 Recent research has demonstrated that most VA patients carry ≥ 1 genetic variant that may influence medication decisions and that half of veterans are prescribed a medication with known gene-drug interactions.15 CPP ordering of pharmacogenomic tests at the VACOHCS outpatient clinic was evaluated through collection of baseline data from March 8, 2023, to September 8, 2023. A goal was identified to increase orders by 50% for a patient care quality improvement initiative and use CPPs to increase access to pharmacogenomic testing. The purpose of this quality improvement initiative was to expand access to pharmacogenomic testing through process implementation and improvement within CPP-led clinic settings.
Gap Analysis
Lean Six Sigma A3 methodology was used to identify ways to increase the use of pharmacogenomic testing for veterans at VACOHCS and develop an improved process for increased ordering of pharmacogenomic testing. Lean Six Sigma A3 methodology is a stepwise approach to process improvement that helps identify gaps in efficiency, sustainable changes, and eliminate waste.16 Baseline data were collected from March 8, 2023, to September 8, 2023, to determine the frequency of CPPs ordering pharmacogenomic laboratory panels during clinic appointments. The ordering of pharmacogenomic panels was monitored by the VACOHCS PHASER coordinator.
CPPs were surveyed to identify perceived barriers to PHASER implementation. A gap analysis was conducted using Lean Six Sigma A3 methodology. Gap analyses use lean tools such as a Fishbone Diagram to illustrate and identify the gap between current state and ideal state. (Figure 1).The following barriers were identified: lack of clinician education materials, lack of a standardized patient screening process, time constraints on patient education and ordering, higher priority clinical needs, forgetting to order, lack of comfort with pharmacogenomics ordering and education, lack of support for the initiative, and increased workload and burnout. Among these perceived barriers, higher priority clinical needs, forgetting to order, and time constraints ranked highest in importance among CPPs.
In line with Lean Six Sigma A3 methodology, several tests of change were used to improve pharmacogenomic testing ordering. These changes focused on increasing patient and clinician awareness, facilitating discussion, educating clinicians, and simplifying documentation to ease time constraints. Several strategies were employed postimplementation (Figure 2). Prefilled templates simplified documentation. These templates helped identify patients without pharmacogenomic testing, provided reminders, and saved documentation time during visits. CPPs also received training and materials on PHASER ordering and documentation within encounter notes. Additionally, patient-directed advertisements were displayed in CPP examination rooms to help inspire and facilitate discussion between veterans and CPPs.
Process Improvement Data
The quality improvement project goal was to increase PHASER orders by 50% after 3 months. PHASER orders increased from 87 at baseline (March 8, 2023, to September 8, 2023) to 196 during the intervention (November 16, 2023, to February 16, 2024), a 125% increase. Changes were consistent and sustained with 65 orders the first month, 67 orders the second month, and 64 orders the third month.
Discussion
Using Lean Six Sigma A3 methodology for a quality improvement process to increase PHASER orders by CPPs revealed barriers and guided potential solutions to overcome these barriers. Interventions included additional CPP training and ordering, tools for easier identification of potential patients, documentation best practices, patient-directed advertisements to facilitate conversations. These interventions required about 8 hours for preparation, distribution, development, and interpretation of surveys, education, and documentation materials. The financial impact of these interventions was already included in allotted office materials budgeted and provided. Additional funding was not needed to provide patient-directed advertisements or education materials. The VACOHCS pharmacogenomics CPP discusses PHASER test results with patients at a separate appointment.
Future directions include educating other CPPs to assist in discussing results with veterans. Overall, the changes implemented to improve the PHASER ordering process were low effort and exemplify the ease of streamlining future initiatives, allowing for sustained optimal implementation of pharmacogenomic testing.
Conclusions
A quality improvement initiative resulted in increased PHASER orders and a clearly defined process, allowing for a continued increase and sustained support. Perceived barriers were identified, and the changes implemented were often low effort but exhibited a sustained impact. The insights gleaned from this process will shape future process development initiatives and continue to sustain pharmacogenomic testing ordering by CPPs. This process will be extended to other VACOHCS clinical departments to further support increased access to pharmacogenomic testing, reduce medication trial and error, and reduce hospitalizations from adverse effects for veterans.
Cecchin E, Stocco G. Pharmacogenomics and personalized medicine. Genes (Basel). 2020;11(6):679. doi:10.3390/genes11060679
Guidelines. CPIC. Accessed April 16, 2025. https://cpicpgx.org/guidelines/
PharmGKB. PharmGKB. 2025. Accessed April 16, 2025. https://www.pharmgkb.org
Centers for Disease Control and Prevention. Pharmacogenomics. Updated November 13, 2024. Accessed April 16, 2024. https://www.cdc.gov/genomics-and-health/pharmacogenomics/
Dong OM, Roberts MC, Wu RR, et al. Evaluation of the Veterans Affairs Pharmacogenomic Testing for Veterans (PHASER) clinical program at initial test sites. Pharmacogenomics. 2021;22(17):1121-1133. doi:10.2217/pgs-2021-0089
Melendez K, Gutierrez-Meza D, Gavin KL, et al. Patient perspectives of barriers and facilitators for the uptake of pharmacogenomic testing in Veterans Affairs’ pharmacogenomic testing for the veterans (PHASER) program. J Pers Med. 2023;13(9):1367. doi:10.3390/jpm13091367
Sanford Health Imagenetics. FREQUENTLY ASKED QUESTIONS (FAQs) about the “Pharmacogenomic Teting for Vetans” (PHASER) Program. US Department of Veterans Affairs. December 20, 2019. Accessed April 16, 2025. https://www.va.gov/opa/publications/factsheets/PHASER-FLYER-VA-Patient-FAQ.pdf
Peterson H. PHASER program testing informs how you respond to medicines. VA News. September 6, 2022. Accessed April 16, 2025. https://news.va.gov/108091/phaser-program-testing-respond-medicines/
Pharmacogenomics (PGx). Sanford Health Imagenetics. 2025. Accessed April 16, 2025. https://imagenetics.sanfordhealth.org/pharmacogenomics/
Martin MA, Hoffman JM, Freimuth RR, et al. Clinical pharmacogenetics implementation consortium guidelines for HLA-B genotype and abacavir dosing: 2014 update. Clin Pharmacol Ther. 2014;95(5):499-500. doi:10.1038/clpt.2014.38
Lee CR, Luzum JA, Sangkuhl K, et al. Clinical pharmacogenetics implementation consortium guideline for CYP2C19 genotype and clopidogrel therapy: 2022 update. Clin Pharmacol Ther. 2022;112(5):959-967. doi:10.1002/cpt.2526
Dreischmeier E, Hecht H, Crocker E, et al. Integration of a clinical pharmacist practitioner-led pharmacogenomics service in a Veterans Affairs hematology/oncology clinic. Am J Health Syst Pharm. 2024;81(19):e634-e639. doi:10.1093/ajhp/zxae122
Tomcsanyi KM, Tran KA, Bates J, et al. Veterans Health Administration: implementation of pharmacogenomic clinical decision support with statin medications and the SLCO1B1 gene as an exemplar. Am J Health Syst Pharm. 2023;80(16):1082-1089. doi:10.1093/ajhp/zxad111
Gammal RS, Lee YM, Petry NJ, et al. Pharmacists leading the way to precision medicine: updates to the core pharmacist competencies in genomics. Am J Pharm Educ. 2022;86(4):8634. doi:10.5688/ajpe8634
Chanfreau-Coffinier C, Hull LE, Lynch JA, et al. Projected prevalence of actionable pharmacogenetic variants and level A drugs prescribed among US Veterans Health Administration pharmacy users. JAMA Netw Open. 2019;2(6):e195345. doi:10.1001/jamanetworkopen.2019.5345
Shaffie S, Shahbazi S. The McGraw-Hill 36-Hour Course: Lean Six Sigma. McGraw-Hill; 2012.
Peer-review, evidence-based, detailed gene/drug clinical practice guidelines suggest that genetic variations can impact how individuals metabolize medications, which is sometimes included in medication prescribing information.1-3 Pharmacogenomic testing identifies genetic markers so medication selection and dosing can be tailored to each individual by identifying whether a specific medication is likely to be safe and effective prior to prescribing.4
Pharmacogenomics can be a valuable tool for personalizing medicine but has had suboptimal implementation since its discovery. The US Department of Veterans Affairs (VA) health care system reviewed the implementation of the Pharmacogenomic Testing for Veterans (PHASER) program. This review identified clinician barriers pre- and post-PHASER program implementation; staffing issues, competing clinical priorities, and inadequate PHASER program resources were the most frequently reported barriers to implementation of pharmacogenomic testing.5
Another evaluation of the implementation of the PHASER program that surveyed VA patients found that patients could be separated into 3 groups. Acceptors of pharmacogenomic testing emphasized potential health benefits of testing. Patients that declined testing often cited concerns for genetic information affecting insurance coverage, being misused, or being susceptible to data breach. The third group—identified as contemplators—reported the need for clinician outreach to impact their decision on whether or not to receive pharmacogenomic testing.6 These studies suggest that removing barriers by providing ample pharmacogenomics resources to clinicians, in addition to detailed training on how to offer and follow up with patients regarding pharmacogenomic testing, is crucial to successful implementation of the PHASER program.
PHASER
In 2019, the VA began working with Sanford Health to establish the PHASER program and offer pharmacogenomic testing. PHASER has since expanded to 25 VA medical centers, including the VA Central Ohio Healthcare System (VACOHCS).7,8 Pharmacogenomic testing through PHASER is conducted using a standardized laboratory panel that includes 12 different medication classes.9 The drug classes include certain anti-infective, anticoagulant, antiplatelet, cardiovascular, cholesterol, gastrointestinal, mental health, neurological, oncology, pain, transplant, and other miscellaneous medications. Medications are correlated to each class and assessed for therapeutic impacts based on gene panel results.
Clinical recommendations for medication-gene interactions can range from monitoring for increased risk of adverse effects or therapeutic failure to recommending avoiding a medication. For example, patients who test positive for the HLA-B gene have significantly increased risk of hypersensitivity to abacavir, an HIV treatment.10
Similarly, patients who cannot adequately metabolize cytochrome P450 2C19 should consider avoiding clopidogrel as they are unlikely to convert clopidogrel to its active prodrug, which reduces its effectiveness.11 Pharmacists can play a critical role educating patients about pharmacogenomic testing, especially within hematology and oncology.12 Patients can benefit from this testing even if they are not currently taking medications with known concerns as they could be prescribed in the future. The SLCO1B1 gene-drug test, for example, can identify risk for statin-associated muscle symptoms.13
Clinical pharmacist practitioners (CPPs) can increase access to genetic testing because they interact with patients in a variety of settings and can order this laboratory test.12,14 Recent research has demonstrated that most VA patients carry ≥ 1 genetic variant that may influence medication decisions and that half of veterans are prescribed a medication with known gene-drug interactions.15 CPP ordering of pharmacogenomic tests at the VACOHCS outpatient clinic was evaluated through collection of baseline data from March 8, 2023, to September 8, 2023. A goal was identified to increase orders by 50% for a patient care quality improvement initiative and use CPPs to increase access to pharmacogenomic testing. The purpose of this quality improvement initiative was to expand access to pharmacogenomic testing through process implementation and improvement within CPP-led clinic settings.
Gap Analysis
Lean Six Sigma A3 methodology was used to identify ways to increase the use of pharmacogenomic testing for veterans at VACOHCS and develop an improved process for increased ordering of pharmacogenomic testing. Lean Six Sigma A3 methodology is a stepwise approach to process improvement that helps identify gaps in efficiency, sustainable changes, and eliminate waste.16 Baseline data were collected from March 8, 2023, to September 8, 2023, to determine the frequency of CPPs ordering pharmacogenomic laboratory panels during clinic appointments. The ordering of pharmacogenomic panels was monitored by the VACOHCS PHASER coordinator.
CPPs were surveyed to identify perceived barriers to PHASER implementation. A gap analysis was conducted using Lean Six Sigma A3 methodology. Gap analyses use lean tools such as a Fishbone Diagram to illustrate and identify the gap between current state and ideal state. (Figure 1).The following barriers were identified: lack of clinician education materials, lack of a standardized patient screening process, time constraints on patient education and ordering, higher priority clinical needs, forgetting to order, lack of comfort with pharmacogenomics ordering and education, lack of support for the initiative, and increased workload and burnout. Among these perceived barriers, higher priority clinical needs, forgetting to order, and time constraints ranked highest in importance among CPPs.
In line with Lean Six Sigma A3 methodology, several tests of change were used to improve pharmacogenomic testing ordering. These changes focused on increasing patient and clinician awareness, facilitating discussion, educating clinicians, and simplifying documentation to ease time constraints. Several strategies were employed postimplementation (Figure 2). Prefilled templates simplified documentation. These templates helped identify patients without pharmacogenomic testing, provided reminders, and saved documentation time during visits. CPPs also received training and materials on PHASER ordering and documentation within encounter notes. Additionally, patient-directed advertisements were displayed in CPP examination rooms to help inspire and facilitate discussion between veterans and CPPs.
Process Improvement Data
The quality improvement project goal was to increase PHASER orders by 50% after 3 months. PHASER orders increased from 87 at baseline (March 8, 2023, to September 8, 2023) to 196 during the intervention (November 16, 2023, to February 16, 2024), a 125% increase. Changes were consistent and sustained with 65 orders the first month, 67 orders the second month, and 64 orders the third month.
Discussion
Using Lean Six Sigma A3 methodology for a quality improvement process to increase PHASER orders by CPPs revealed barriers and guided potential solutions to overcome these barriers. Interventions included additional CPP training and ordering, tools for easier identification of potential patients, documentation best practices, patient-directed advertisements to facilitate conversations. These interventions required about 8 hours for preparation, distribution, development, and interpretation of surveys, education, and documentation materials. The financial impact of these interventions was already included in allotted office materials budgeted and provided. Additional funding was not needed to provide patient-directed advertisements or education materials. The VACOHCS pharmacogenomics CPP discusses PHASER test results with patients at a separate appointment.
Future directions include educating other CPPs to assist in discussing results with veterans. Overall, the changes implemented to improve the PHASER ordering process were low effort and exemplify the ease of streamlining future initiatives, allowing for sustained optimal implementation of pharmacogenomic testing.
Conclusions
A quality improvement initiative resulted in increased PHASER orders and a clearly defined process, allowing for a continued increase and sustained support. Perceived barriers were identified, and the changes implemented were often low effort but exhibited a sustained impact. The insights gleaned from this process will shape future process development initiatives and continue to sustain pharmacogenomic testing ordering by CPPs. This process will be extended to other VACOHCS clinical departments to further support increased access to pharmacogenomic testing, reduce medication trial and error, and reduce hospitalizations from adverse effects for veterans.
Peer-review, evidence-based, detailed gene/drug clinical practice guidelines suggest that genetic variations can impact how individuals metabolize medications, which is sometimes included in medication prescribing information.1-3 Pharmacogenomic testing identifies genetic markers so medication selection and dosing can be tailored to each individual by identifying whether a specific medication is likely to be safe and effective prior to prescribing.4
Pharmacogenomics can be a valuable tool for personalizing medicine but has had suboptimal implementation since its discovery. The US Department of Veterans Affairs (VA) health care system reviewed the implementation of the Pharmacogenomic Testing for Veterans (PHASER) program. This review identified clinician barriers pre- and post-PHASER program implementation; staffing issues, competing clinical priorities, and inadequate PHASER program resources were the most frequently reported barriers to implementation of pharmacogenomic testing.5
Another evaluation of the implementation of the PHASER program that surveyed VA patients found that patients could be separated into 3 groups. Acceptors of pharmacogenomic testing emphasized potential health benefits of testing. Patients that declined testing often cited concerns for genetic information affecting insurance coverage, being misused, or being susceptible to data breach. The third group—identified as contemplators—reported the need for clinician outreach to impact their decision on whether or not to receive pharmacogenomic testing.6 These studies suggest that removing barriers by providing ample pharmacogenomics resources to clinicians, in addition to detailed training on how to offer and follow up with patients regarding pharmacogenomic testing, is crucial to successful implementation of the PHASER program.
PHASER
In 2019, the VA began working with Sanford Health to establish the PHASER program and offer pharmacogenomic testing. PHASER has since expanded to 25 VA medical centers, including the VA Central Ohio Healthcare System (VACOHCS).7,8 Pharmacogenomic testing through PHASER is conducted using a standardized laboratory panel that includes 12 different medication classes.9 The drug classes include certain anti-infective, anticoagulant, antiplatelet, cardiovascular, cholesterol, gastrointestinal, mental health, neurological, oncology, pain, transplant, and other miscellaneous medications. Medications are correlated to each class and assessed for therapeutic impacts based on gene panel results.
Clinical recommendations for medication-gene interactions can range from monitoring for increased risk of adverse effects or therapeutic failure to recommending avoiding a medication. For example, patients who test positive for the HLA-B gene have significantly increased risk of hypersensitivity to abacavir, an HIV treatment.10
Similarly, patients who cannot adequately metabolize cytochrome P450 2C19 should consider avoiding clopidogrel as they are unlikely to convert clopidogrel to its active prodrug, which reduces its effectiveness.11 Pharmacists can play a critical role educating patients about pharmacogenomic testing, especially within hematology and oncology.12 Patients can benefit from this testing even if they are not currently taking medications with known concerns as they could be prescribed in the future. The SLCO1B1 gene-drug test, for example, can identify risk for statin-associated muscle symptoms.13
Clinical pharmacist practitioners (CPPs) can increase access to genetic testing because they interact with patients in a variety of settings and can order this laboratory test.12,14 Recent research has demonstrated that most VA patients carry ≥ 1 genetic variant that may influence medication decisions and that half of veterans are prescribed a medication with known gene-drug interactions.15 CPP ordering of pharmacogenomic tests at the VACOHCS outpatient clinic was evaluated through collection of baseline data from March 8, 2023, to September 8, 2023. A goal was identified to increase orders by 50% for a patient care quality improvement initiative and use CPPs to increase access to pharmacogenomic testing. The purpose of this quality improvement initiative was to expand access to pharmacogenomic testing through process implementation and improvement within CPP-led clinic settings.
Gap Analysis
Lean Six Sigma A3 methodology was used to identify ways to increase the use of pharmacogenomic testing for veterans at VACOHCS and develop an improved process for increased ordering of pharmacogenomic testing. Lean Six Sigma A3 methodology is a stepwise approach to process improvement that helps identify gaps in efficiency, sustainable changes, and eliminate waste.16 Baseline data were collected from March 8, 2023, to September 8, 2023, to determine the frequency of CPPs ordering pharmacogenomic laboratory panels during clinic appointments. The ordering of pharmacogenomic panels was monitored by the VACOHCS PHASER coordinator.
CPPs were surveyed to identify perceived barriers to PHASER implementation. A gap analysis was conducted using Lean Six Sigma A3 methodology. Gap analyses use lean tools such as a Fishbone Diagram to illustrate and identify the gap between current state and ideal state. (Figure 1).The following barriers were identified: lack of clinician education materials, lack of a standardized patient screening process, time constraints on patient education and ordering, higher priority clinical needs, forgetting to order, lack of comfort with pharmacogenomics ordering and education, lack of support for the initiative, and increased workload and burnout. Among these perceived barriers, higher priority clinical needs, forgetting to order, and time constraints ranked highest in importance among CPPs.
In line with Lean Six Sigma A3 methodology, several tests of change were used to improve pharmacogenomic testing ordering. These changes focused on increasing patient and clinician awareness, facilitating discussion, educating clinicians, and simplifying documentation to ease time constraints. Several strategies were employed postimplementation (Figure 2). Prefilled templates simplified documentation. These templates helped identify patients without pharmacogenomic testing, provided reminders, and saved documentation time during visits. CPPs also received training and materials on PHASER ordering and documentation within encounter notes. Additionally, patient-directed advertisements were displayed in CPP examination rooms to help inspire and facilitate discussion between veterans and CPPs.
Process Improvement Data
The quality improvement project goal was to increase PHASER orders by 50% after 3 months. PHASER orders increased from 87 at baseline (March 8, 2023, to September 8, 2023) to 196 during the intervention (November 16, 2023, to February 16, 2024), a 125% increase. Changes were consistent and sustained with 65 orders the first month, 67 orders the second month, and 64 orders the third month.
Discussion
Using Lean Six Sigma A3 methodology for a quality improvement process to increase PHASER orders by CPPs revealed barriers and guided potential solutions to overcome these barriers. Interventions included additional CPP training and ordering, tools for easier identification of potential patients, documentation best practices, patient-directed advertisements to facilitate conversations. These interventions required about 8 hours for preparation, distribution, development, and interpretation of surveys, education, and documentation materials. The financial impact of these interventions was already included in allotted office materials budgeted and provided. Additional funding was not needed to provide patient-directed advertisements or education materials. The VACOHCS pharmacogenomics CPP discusses PHASER test results with patients at a separate appointment.
Future directions include educating other CPPs to assist in discussing results with veterans. Overall, the changes implemented to improve the PHASER ordering process were low effort and exemplify the ease of streamlining future initiatives, allowing for sustained optimal implementation of pharmacogenomic testing.
Conclusions
A quality improvement initiative resulted in increased PHASER orders and a clearly defined process, allowing for a continued increase and sustained support. Perceived barriers were identified, and the changes implemented were often low effort but exhibited a sustained impact. The insights gleaned from this process will shape future process development initiatives and continue to sustain pharmacogenomic testing ordering by CPPs. This process will be extended to other VACOHCS clinical departments to further support increased access to pharmacogenomic testing, reduce medication trial and error, and reduce hospitalizations from adverse effects for veterans.
Cecchin E, Stocco G. Pharmacogenomics and personalized medicine. Genes (Basel). 2020;11(6):679. doi:10.3390/genes11060679
Guidelines. CPIC. Accessed April 16, 2025. https://cpicpgx.org/guidelines/
PharmGKB. PharmGKB. 2025. Accessed April 16, 2025. https://www.pharmgkb.org
Centers for Disease Control and Prevention. Pharmacogenomics. Updated November 13, 2024. Accessed April 16, 2024. https://www.cdc.gov/genomics-and-health/pharmacogenomics/
Dong OM, Roberts MC, Wu RR, et al. Evaluation of the Veterans Affairs Pharmacogenomic Testing for Veterans (PHASER) clinical program at initial test sites. Pharmacogenomics. 2021;22(17):1121-1133. doi:10.2217/pgs-2021-0089
Melendez K, Gutierrez-Meza D, Gavin KL, et al. Patient perspectives of barriers and facilitators for the uptake of pharmacogenomic testing in Veterans Affairs’ pharmacogenomic testing for the veterans (PHASER) program. J Pers Med. 2023;13(9):1367. doi:10.3390/jpm13091367
Sanford Health Imagenetics. FREQUENTLY ASKED QUESTIONS (FAQs) about the “Pharmacogenomic Teting for Vetans” (PHASER) Program. US Department of Veterans Affairs. December 20, 2019. Accessed April 16, 2025. https://www.va.gov/opa/publications/factsheets/PHASER-FLYER-VA-Patient-FAQ.pdf
Peterson H. PHASER program testing informs how you respond to medicines. VA News. September 6, 2022. Accessed April 16, 2025. https://news.va.gov/108091/phaser-program-testing-respond-medicines/
Pharmacogenomics (PGx). Sanford Health Imagenetics. 2025. Accessed April 16, 2025. https://imagenetics.sanfordhealth.org/pharmacogenomics/
Martin MA, Hoffman JM, Freimuth RR, et al. Clinical pharmacogenetics implementation consortium guidelines for HLA-B genotype and abacavir dosing: 2014 update. Clin Pharmacol Ther. 2014;95(5):499-500. doi:10.1038/clpt.2014.38
Lee CR, Luzum JA, Sangkuhl K, et al. Clinical pharmacogenetics implementation consortium guideline for CYP2C19 genotype and clopidogrel therapy: 2022 update. Clin Pharmacol Ther. 2022;112(5):959-967. doi:10.1002/cpt.2526
Dreischmeier E, Hecht H, Crocker E, et al. Integration of a clinical pharmacist practitioner-led pharmacogenomics service in a Veterans Affairs hematology/oncology clinic. Am J Health Syst Pharm. 2024;81(19):e634-e639. doi:10.1093/ajhp/zxae122
Tomcsanyi KM, Tran KA, Bates J, et al. Veterans Health Administration: implementation of pharmacogenomic clinical decision support with statin medications and the SLCO1B1 gene as an exemplar. Am J Health Syst Pharm. 2023;80(16):1082-1089. doi:10.1093/ajhp/zxad111
Gammal RS, Lee YM, Petry NJ, et al. Pharmacists leading the way to precision medicine: updates to the core pharmacist competencies in genomics. Am J Pharm Educ. 2022;86(4):8634. doi:10.5688/ajpe8634
Chanfreau-Coffinier C, Hull LE, Lynch JA, et al. Projected prevalence of actionable pharmacogenetic variants and level A drugs prescribed among US Veterans Health Administration pharmacy users. JAMA Netw Open. 2019;2(6):e195345. doi:10.1001/jamanetworkopen.2019.5345
Shaffie S, Shahbazi S. The McGraw-Hill 36-Hour Course: Lean Six Sigma. McGraw-Hill; 2012.
Cecchin E, Stocco G. Pharmacogenomics and personalized medicine. Genes (Basel). 2020;11(6):679. doi:10.3390/genes11060679
Guidelines. CPIC. Accessed April 16, 2025. https://cpicpgx.org/guidelines/
PharmGKB. PharmGKB. 2025. Accessed April 16, 2025. https://www.pharmgkb.org
Centers for Disease Control and Prevention. Pharmacogenomics. Updated November 13, 2024. Accessed April 16, 2024. https://www.cdc.gov/genomics-and-health/pharmacogenomics/
Dong OM, Roberts MC, Wu RR, et al. Evaluation of the Veterans Affairs Pharmacogenomic Testing for Veterans (PHASER) clinical program at initial test sites. Pharmacogenomics. 2021;22(17):1121-1133. doi:10.2217/pgs-2021-0089
Melendez K, Gutierrez-Meza D, Gavin KL, et al. Patient perspectives of barriers and facilitators for the uptake of pharmacogenomic testing in Veterans Affairs’ pharmacogenomic testing for the veterans (PHASER) program. J Pers Med. 2023;13(9):1367. doi:10.3390/jpm13091367
Sanford Health Imagenetics. FREQUENTLY ASKED QUESTIONS (FAQs) about the “Pharmacogenomic Teting for Vetans” (PHASER) Program. US Department of Veterans Affairs. December 20, 2019. Accessed April 16, 2025. https://www.va.gov/opa/publications/factsheets/PHASER-FLYER-VA-Patient-FAQ.pdf
Peterson H. PHASER program testing informs how you respond to medicines. VA News. September 6, 2022. Accessed April 16, 2025. https://news.va.gov/108091/phaser-program-testing-respond-medicines/
Pharmacogenomics (PGx). Sanford Health Imagenetics. 2025. Accessed April 16, 2025. https://imagenetics.sanfordhealth.org/pharmacogenomics/
Martin MA, Hoffman JM, Freimuth RR, et al. Clinical pharmacogenetics implementation consortium guidelines for HLA-B genotype and abacavir dosing: 2014 update. Clin Pharmacol Ther. 2014;95(5):499-500. doi:10.1038/clpt.2014.38
Lee CR, Luzum JA, Sangkuhl K, et al. Clinical pharmacogenetics implementation consortium guideline for CYP2C19 genotype and clopidogrel therapy: 2022 update. Clin Pharmacol Ther. 2022;112(5):959-967. doi:10.1002/cpt.2526
Dreischmeier E, Hecht H, Crocker E, et al. Integration of a clinical pharmacist practitioner-led pharmacogenomics service in a Veterans Affairs hematology/oncology clinic. Am J Health Syst Pharm. 2024;81(19):e634-e639. doi:10.1093/ajhp/zxae122
Tomcsanyi KM, Tran KA, Bates J, et al. Veterans Health Administration: implementation of pharmacogenomic clinical decision support with statin medications and the SLCO1B1 gene as an exemplar. Am J Health Syst Pharm. 2023;80(16):1082-1089. doi:10.1093/ajhp/zxad111
Gammal RS, Lee YM, Petry NJ, et al. Pharmacists leading the way to precision medicine: updates to the core pharmacist competencies in genomics. Am J Pharm Educ. 2022;86(4):8634. doi:10.5688/ajpe8634
Chanfreau-Coffinier C, Hull LE, Lynch JA, et al. Projected prevalence of actionable pharmacogenetic variants and level A drugs prescribed among US Veterans Health Administration pharmacy users. JAMA Netw Open. 2019;2(6):e195345. doi:10.1001/jamanetworkopen.2019.5345
Shaffie S, Shahbazi S. The McGraw-Hill 36-Hour Course: Lean Six Sigma. McGraw-Hill; 2012.
Safety and Efficacy of Ezetimibe in Patients With and Without Chronic Kidney Disease at a Pharmacist-Managed Clinic
Statins are widely used to reduce low-density lipoprotein (LDL) and non-high-density lipoprotein (HDL) levels for the prevention of atherosclerotic cardiovascular disease (ASCVD).1 However, despite maximally tolerated statin therapy, many patients may not reach their LDL and non-HDL goals. Some patients may experience adverse events (AEs), particularly muscle-related AEs, which can limit the use of these medications.
The 2022 American College of Cardiology (ACC) expert consensus pathway recommends a goal LDL of < 55 mg/dL in very high-risk patients, defined as those with a history of multiple major ASCVD events or 1 major ASCVD event and multiple high-risk conditions.2 Major ASCVD events include acute coronary syndrome within 12 months, history of myocardial infarction (MI) or ischemic stroke, and symptomatic peripheral arterial disease (ie, claudication with ankle-brachial index < 0.85 or previous revascularization or amputation). Factors for being considered high risk include age > 65 years, heterozygous familial hypercholesterolemia, history of prior coronary artery bypass surgery or percutaneous coronary intervention outside the major ASCVD events, diabetes, hypertension, chronic kidney disease (CKD) (estimated glomerular filtration rate [eGFR] 15-59 mL/min/1.73 m2), current smoking, persistently elevated LDL cholesterol (LDL-C) levels despite maximally tolerated statin therapy and ezetimibe, and history of congestive heart failure.2 For these patients, statin therapy alone may not achieve LDL goal.
The ACC recommends ezetimibe as the initial nonstatin therapy in patients who are not at their goal LDL.2 Ezetimibe works by inhibiting Niemann-Pick C1-Like 1 protein, which causes reduced cholesterol absorption in the small intestine.2,3 Previous studies have shown the benefit of ezetimibe for LDL reduction and ASCVD prevention.4-7 The 2015 IMPROVE-IT study found the combination of simvastatin and ezetimibe resulted in a significantly lower risk of cardiovascular events than simvastatin monotherapy. IMPROVE-IT also reported a further clinical benefit when lower LDL targets (ie, < 55 mg/dL) are achieved, which aligns with the expert consensus pathway recommendations for a lower LDL goal for very high-risk patients.2,5
The RACING trial found that treatment with a moderate-intensity statin and ezetimibe was noninferior to treatment with a high-intensity statin for the primary outcome of occurrence of cardiovascular death, major cardiovascular events, or nonfatal stroke within 3 years. The combination of moderate-intensity statin and ezetimibe achieved lower LDL-C levels and lower incidence of drug intolerance compared to high intensity statin monotherapy.6 The SHARP-CKD study assessed major atherosclerotic events in patients with CKD who had no history of MI or coronary revascularization. The study found that lowering LDL-C with the combination of simvastatin plus ezetimibe safely reduces the risk of major atherosclerotic events in a wide range of patients with CKD.7
Lastly, the 2019 EWTOPIA 75 study found that ezetimibe noted a statistically significant reduction in the incidence of the composite of sudden cardiac death, MI, coronary revascularization, or stroke compared to placebo. Ezetimibe showed benefits in preventing ASCVD events independently of statin therapy.8 These clinical trials provided evidence for the efficacy of ezetimibe for secondary or primary prevention of ASCVD, patients with CKD, and patients who are not at their LDL goal despite maximally tolerated statin therapy.
Reductions in LDL levels with ezetimibe are reported to be 15% to 19% for monotherapy and 13% to 25% when used in combination with a statin.4 Given that the ACC now recommends lower LDL goals, patients may need additional lowering despite taking maximally tolerated statin therapy.2 Additionally, the package insert for ezetimibe reports increased area under the curve (AUC) values of ezetimibe and its metabolites in patients with severe renal disease. It is anticipated that ezetimibe may show an increased reduction of LDL and non-HDL, but there may also be an increased risk for muscle-related AEs.3
This quality-assurance quality improvement project investigated the use of ezetimibe in patients with CKD to determine whether there is further LDL and non-HDL reduction in this patient population. It sought to determine the LDL and non-HDL percentage reduction in patients with and without CKD at the Wilkes-Barre Veterans Affairs Medical Center (WBVAMC) and whether there is an increased risk for muscle-related AEs. Determining the percentage reduction of LDL and non-HDL within this population can help increase use of ezetimibe in patients not at their LDL or non-HDL goal or for those patients unable to tolerate statin therapy.
Methods
This single-center retrospective chart review investigated patients prescribed ezetimibe by a patient aligned care team (PACT) pharmacist at WBVAMC between September 1, 2021, and September 1, 2023. This project was determined to be nonresearch by the Veterans Integrated Service Network 4 multisite institutional review board. Patients were excluded from the review if they started taking ezetimibe outside of the prespecified time frame, if ezetimibe was initiated by a non-WBVAMC PACT pharmacist, or if there was no follow-up lipid panel obtained within 6 months of initiation of ezetimibe.
The primary outcomes were to determine the percentage mean change in LDL and non-HDL reduction and the incidence of muscle-related AEs after initiation of ezetimibe in patients without CKD. The secondary outcomes were to determine the percentage mean change in LDL and non-HDL levels and the incidence of muscle-related AEs after initiation of ezetimibe in patients with CKD. For this study, CKD was defined as a patient having an eGFR 15 to 60 ml/min/1.73 m2. Non-HDL is the combination of LDL-C and very LDL-C and represents all potentially atherogenic particles. The 2022 Expert Consensus Pathway included non-HDL goals in addition to LDL goals.2 Non-HDL cholesterol levels can be used for patients with elevated triglycerides where LDL levels may not be as accurate. To account for instances of elevated triglycerides, this study assessed changes in both LDL and non-HDL levels.
Data were collected from the US Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) and recorded in a spreadsheet. Collected data included age, sex, race, concomitant cholesterol-lowering medications (statin, proprotein convertase subtilisin/kexin type 9 [PCSK9] inhibitor, bempedoic acid, fish oil, niacin, bile acid sequestrants, and fibrates), baseline lipid panel, lipid panel within 6 months of ezetimibe initiation, and eGFR level. If the patient’s LDL or non-HDL levels worsened on the follow-up lipid panel, their baseline LDL and non-HDL levels were used to calculate the percentage reduction; thus, the percentage reduction would be 0%. This strategy was used in prior research, notably the IMPROVE-IT and SHARP-CKD trials.
Ezetimibe 5 mg once daily was used in this study based on a 2008 VA study that evaluated the use of ezetimibe 5 mg vs ezetimibe 10 mg and the percentage reduction of LDL with each dose. The study found no significant difference between the 5 mg and 10 mg dose.9 Most patients included in this study received the 5 mg dose.
Results
This retrospective chart review consisted of 173 patients, 137 (79.2%) without CKD and 36 (20.8%) with CKD at baseline. The mean age was 69.6 years, 155 (89.6%) patients were male, and 18 (10.4%) were female. There were 164 concomitant medications, including 115 patients prescribed a statin and 38 patients prescribed fish oil (Table 1).
Patients without CKD had mean reductions in LDL levels of 23.5% and non-HDL levels of 21.7% (Figure). Patients who had an increase in LDL and non-HDL levels were excluded to control for potential confounding factors such as dietary changes, discontinuation of ezetimibe therapy, nonadherence to ezetimibe, and medication changes that impacted follow-up laboratory tests such as discontinuation of a statin. Fifteen patients experienced an increase in LDL or non-HDL levels. After excluding these patients, those without CKD had a mean reduction in LDL levels of 28.0% and non-HDL levels of 25.5%. Nineteen (13.9%) patients without CKD experienced a muscle-related AE (Table 2). One patient discontinued ezetimibe and statin use following a Lyme disease diagnosis due to concerns over potential muscle-related AEs.
Patients with CKD had a mean reduction in LDL and non-HDL levels of 27.0% and 24.8%, respectively. Patients with an increase in LDL or non-HDL levels were also excluded to help control for potential confounding factors. After excluding 4 patients with increased LDL and non-HDL levels, the mean reduction in LDL and non-HDL levels was 30.5% and 27.5%, respectively. Five (13.9%) patients with CKD experienced muscle-related AEs thought to be due to ezetimibe. Other AEs (eg, urticaria, diarrhea, reflux, dizziness, headache, upset stomach) were reported that led to discontinuation of ezetimibe, but only muscle-related AEs were analyzed.
Discussion
This retrospective chart review found larger reductions in LDL and non-HDL levels for patients with CKD than reported in the literature.4 Based on the findings that indicate a greater cholesterol reduction with ezetimibe, the results suggest an underutilization of ezetimibe in clinical practice, which may be due to clinicians favoring statin therapy and overlooking ezetimibe as a viable option based on recommendation in earlier guidelines. The 2022 guidelines transitioned from a statin focus to a focus on LDL targets and goals.2
According to the ACC, there is evidence to support a direct relationship between LDL-C levels, atherosclerosis progression, and ASCVD event risk.2 Absolute LDL-C level reduction is directly associated with ASCVD risk reduction which supports the LDL hypothesis. There appears to be no specific LDL-C level below which benefit ceases.2 This suggests that lower LDL-C targets (< 55 mg/dL) should be used when clinically indicated. Many patients are either unable to reach their goal LDL levels with statin monotherapy or are unable to tolerate statin therapy at higher doses, which may require additional pharmacotherapy to reach goal LDL-C. The ACC expert consensus pathway recommends ezetimibe as the initial add-on treatment to statins.2 The RACING trial showed the benefit of adding ezetimibe to a moderate-intensity statin vs increasing to a high-intensity statin dose. This trial found patients had lower LDL levels and lower rates of intolerances, which further supports ezetimibe use.6
This quality improvement project assessed LDL and non-HDL level reduction in patients with CKD. As anticipated, there was greater reduction in LDL and non-HDL levels seen in patients with CKD. The SHARP-CKD trial also found reductions in LDL levels with ezetimibe in patients with CKD.7 Given the reduction in LDL and non-HDL levels with ezetimibe in patients with or without CKD, add-on therapy of ezetimibe should be recommended for patients who do not achieve their LDL goals with statin therapy or for patients who intolerant to statin therapy.
The ezetimibe package insert reports myalgias incidence to be < 5% in patients and research has shown up to a 20% incidence of muscle-related AEs with statin therapy.3,10 Based on the package information reporting increased AUC values of ezetimibe and its metabolites in patients with severe renal disease, it was anticipated there may be an increased risk of muscle-related AEs in patients with CKD.3 However, this study found the same incidence of muscle-related AEs in patients with and without CKD. Previous research on statin-intolerant patients found the incidence of muscle-related AEs with ezetimibe to be 23.0% and 28.8%.11,12 This increased incidence of muscle-related AEs may be the result of including patients with a history of statin intolerance. Collectively, data from clinical trials and this study indicate that patients with prior intolerances to statins appear to have a higher likelihood of developing a muscle-related AEs with ezetimibe.11,12 Clinicians and patients should be educated on the potential for these AEs and be aware that the likelihood may be greater if there is a history of statin intolerance. To our knowledge, this was the first study to evaluate muscle-related AEs with ezetimibe in patients with and without CKD.
Limitations
This retrospective chart review was performed over a prespecified period and only patients initiated on ezetimibe by a PACT pharmacist were included. This study did not assess the percentage of LDL reduction in patients on concomitant statins vs those who were not on concomitant statins. The study only included 173 patients. Additionally, the study was primarily composed of White men and may not be representative of other populations. In addition, veterans may not be representative of the general population given their high comorbidity burden and other exposures. Some reported muscle-related AEs associated with ezetimibe may be attributed to the nocebo effect.
Conclusions
The results of this retrospective chart review suggest there may be a larger mean reduction in LDL and non-HDL levels seen with ezetimibe therapy than reported within the literature. There was a larger mean reduction in LDL and non-HDL levels in patients with CKD than in patients without CKD. Additionally, there were the same rates of muscle-related AEs with ezetimibe therapy in patients with and without CKD. The rates of muscle-related AEs with ezetimibe therapy were higher than reported in the medication’s package insert, but lower than reported in literature that included statin-intolerant patients. These results indicate there may be a benefit to an increase in use of ezetimibe in clinical practice due to its increased effectiveness and safety in patients with and without CKD. Ultimately, this can help patients achieve their LDL goals as recommended by ACC clinical practice guidelines.
Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24) e285-e350. doi:10.1016/j.jacc.2018.11.003
Writing Committee, Lloyd-Jones DM, Morris PB, et al. 2022 ACC expert consensus decision pathway on the role of nonstatin therapies for LDL-cholesterol lowering in the management of atherosclerotic cardiovascular disease risk: a report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2022;80(14):1366-1418. doi:10.1016/j.jacc.2022.07.006
US Food and Drug Administration. Ezetimibe. 2007. Accessed April 1, 2025. https://www.accessdata.fda.gov/drugsatfda_docs/label/2008/021445s019lbl.pdf
Singh A, Cho LS. Nonstatin therapy to reduce low-density lipoprotein cholesterol and improve cardiovascular outcomes. Cleve Clin J Med. 2024;91(1):53-63. doi:10.3949/ccjm.91a.23058
Cannon CP, Blazing MA, Giugliano RP, et al. Ezetimibe added to statin therapy after acute coronary syndromes. N Engl J Med. 2015;372(25):2387-2397. doi:10.1056/NEJMoa1410489
Kim B, Hong S, Lee Y, et al. Long-term efficacy and safety of moderate-intensity statin with ezetimibe combination therapy versus high-intensity statin monotherapy in patients with atherosclerotic cardiovascular disease (RACING): a randomised, open-label, non-inferiority trial. Lancet. 2022;400(10349):380-390. doi:10.1016/S0140-6736(22)00916-3
Baigent C, Landray MJ, Reith C, et al. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011;377(9784):2181-2192. doi:10.1016/S0140-6736(11)60739-3
Ouchi Y, Sasaki J, Arai H, et al. Ezetimibe lipid-lowering trial on prevention of atherosclerotic cardiovascular disease in 75 or older (EWTOPIA 75): a randomized, controlled trial. Circulation. 2019;140:992-1003. doi:10.1161/CIRCULATIONAHA.118.039415
Baruch L, Gupta B, Lieberman-Blum SS, Agarwal S, Eng C. Ezetimibe 5 and 10 mg for lowering LDL-C: potential billion-dollar savings with improved tolerability. Am J Manag Care. 2008;14(10):637-641. https://www.ajmc.com/view/oct08-3644p637-641
Stroes ES, Thompson PD, Corsini A, et al. Statin-associated muscle symptoms: impact on statin therapy-European Atherosclerosis Society Consensus Panel Statement on Assessment, Aetiology and Management. Eur Heart J. 2015;36(17):1012-1022. doi:10.1093/eurheartj/ehv043
Stroes E, Colquhoun D, Sullivan D, et al. Anti-PCSK9 antibody effectively lowers cholesterol in patients with statin intolerance: the GAUSS-2 randomized, placebo-controlled phase 3 clinical trial of evolocumab. J Am Coll Cardiol. 2014;63(23):2541-2548. doi:10.1016/j.jacc.2014.03.019
Nissen SE, Stroes E, Dent-Acosta RE, et al. Efficacy and tolerability of evolocumab vs ezetimibe in patients with muscle-related statin intolerance: the GAUSS-3 randomized clinical trial. JAMA. 2016;315(15):1580-1590. doi:10.1001/jama.2016.3608
Statins are widely used to reduce low-density lipoprotein (LDL) and non-high-density lipoprotein (HDL) levels for the prevention of atherosclerotic cardiovascular disease (ASCVD).1 However, despite maximally tolerated statin therapy, many patients may not reach their LDL and non-HDL goals. Some patients may experience adverse events (AEs), particularly muscle-related AEs, which can limit the use of these medications.
The 2022 American College of Cardiology (ACC) expert consensus pathway recommends a goal LDL of < 55 mg/dL in very high-risk patients, defined as those with a history of multiple major ASCVD events or 1 major ASCVD event and multiple high-risk conditions.2 Major ASCVD events include acute coronary syndrome within 12 months, history of myocardial infarction (MI) or ischemic stroke, and symptomatic peripheral arterial disease (ie, claudication with ankle-brachial index < 0.85 or previous revascularization or amputation). Factors for being considered high risk include age > 65 years, heterozygous familial hypercholesterolemia, history of prior coronary artery bypass surgery or percutaneous coronary intervention outside the major ASCVD events, diabetes, hypertension, chronic kidney disease (CKD) (estimated glomerular filtration rate [eGFR] 15-59 mL/min/1.73 m2), current smoking, persistently elevated LDL cholesterol (LDL-C) levels despite maximally tolerated statin therapy and ezetimibe, and history of congestive heart failure.2 For these patients, statin therapy alone may not achieve LDL goal.
The ACC recommends ezetimibe as the initial nonstatin therapy in patients who are not at their goal LDL.2 Ezetimibe works by inhibiting Niemann-Pick C1-Like 1 protein, which causes reduced cholesterol absorption in the small intestine.2,3 Previous studies have shown the benefit of ezetimibe for LDL reduction and ASCVD prevention.4-7 The 2015 IMPROVE-IT study found the combination of simvastatin and ezetimibe resulted in a significantly lower risk of cardiovascular events than simvastatin monotherapy. IMPROVE-IT also reported a further clinical benefit when lower LDL targets (ie, < 55 mg/dL) are achieved, which aligns with the expert consensus pathway recommendations for a lower LDL goal for very high-risk patients.2,5
The RACING trial found that treatment with a moderate-intensity statin and ezetimibe was noninferior to treatment with a high-intensity statin for the primary outcome of occurrence of cardiovascular death, major cardiovascular events, or nonfatal stroke within 3 years. The combination of moderate-intensity statin and ezetimibe achieved lower LDL-C levels and lower incidence of drug intolerance compared to high intensity statin monotherapy.6 The SHARP-CKD study assessed major atherosclerotic events in patients with CKD who had no history of MI or coronary revascularization. The study found that lowering LDL-C with the combination of simvastatin plus ezetimibe safely reduces the risk of major atherosclerotic events in a wide range of patients with CKD.7
Lastly, the 2019 EWTOPIA 75 study found that ezetimibe noted a statistically significant reduction in the incidence of the composite of sudden cardiac death, MI, coronary revascularization, or stroke compared to placebo. Ezetimibe showed benefits in preventing ASCVD events independently of statin therapy.8 These clinical trials provided evidence for the efficacy of ezetimibe for secondary or primary prevention of ASCVD, patients with CKD, and patients who are not at their LDL goal despite maximally tolerated statin therapy.
Reductions in LDL levels with ezetimibe are reported to be 15% to 19% for monotherapy and 13% to 25% when used in combination with a statin.4 Given that the ACC now recommends lower LDL goals, patients may need additional lowering despite taking maximally tolerated statin therapy.2 Additionally, the package insert for ezetimibe reports increased area under the curve (AUC) values of ezetimibe and its metabolites in patients with severe renal disease. It is anticipated that ezetimibe may show an increased reduction of LDL and non-HDL, but there may also be an increased risk for muscle-related AEs.3
This quality-assurance quality improvement project investigated the use of ezetimibe in patients with CKD to determine whether there is further LDL and non-HDL reduction in this patient population. It sought to determine the LDL and non-HDL percentage reduction in patients with and without CKD at the Wilkes-Barre Veterans Affairs Medical Center (WBVAMC) and whether there is an increased risk for muscle-related AEs. Determining the percentage reduction of LDL and non-HDL within this population can help increase use of ezetimibe in patients not at their LDL or non-HDL goal or for those patients unable to tolerate statin therapy.
Methods
This single-center retrospective chart review investigated patients prescribed ezetimibe by a patient aligned care team (PACT) pharmacist at WBVAMC between September 1, 2021, and September 1, 2023. This project was determined to be nonresearch by the Veterans Integrated Service Network 4 multisite institutional review board. Patients were excluded from the review if they started taking ezetimibe outside of the prespecified time frame, if ezetimibe was initiated by a non-WBVAMC PACT pharmacist, or if there was no follow-up lipid panel obtained within 6 months of initiation of ezetimibe.
The primary outcomes were to determine the percentage mean change in LDL and non-HDL reduction and the incidence of muscle-related AEs after initiation of ezetimibe in patients without CKD. The secondary outcomes were to determine the percentage mean change in LDL and non-HDL levels and the incidence of muscle-related AEs after initiation of ezetimibe in patients with CKD. For this study, CKD was defined as a patient having an eGFR 15 to 60 ml/min/1.73 m2. Non-HDL is the combination of LDL-C and very LDL-C and represents all potentially atherogenic particles. The 2022 Expert Consensus Pathway included non-HDL goals in addition to LDL goals.2 Non-HDL cholesterol levels can be used for patients with elevated triglycerides where LDL levels may not be as accurate. To account for instances of elevated triglycerides, this study assessed changes in both LDL and non-HDL levels.
Data were collected from the US Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) and recorded in a spreadsheet. Collected data included age, sex, race, concomitant cholesterol-lowering medications (statin, proprotein convertase subtilisin/kexin type 9 [PCSK9] inhibitor, bempedoic acid, fish oil, niacin, bile acid sequestrants, and fibrates), baseline lipid panel, lipid panel within 6 months of ezetimibe initiation, and eGFR level. If the patient’s LDL or non-HDL levels worsened on the follow-up lipid panel, their baseline LDL and non-HDL levels were used to calculate the percentage reduction; thus, the percentage reduction would be 0%. This strategy was used in prior research, notably the IMPROVE-IT and SHARP-CKD trials.
Ezetimibe 5 mg once daily was used in this study based on a 2008 VA study that evaluated the use of ezetimibe 5 mg vs ezetimibe 10 mg and the percentage reduction of LDL with each dose. The study found no significant difference between the 5 mg and 10 mg dose.9 Most patients included in this study received the 5 mg dose.
Results
This retrospective chart review consisted of 173 patients, 137 (79.2%) without CKD and 36 (20.8%) with CKD at baseline. The mean age was 69.6 years, 155 (89.6%) patients were male, and 18 (10.4%) were female. There were 164 concomitant medications, including 115 patients prescribed a statin and 38 patients prescribed fish oil (Table 1).
Patients without CKD had mean reductions in LDL levels of 23.5% and non-HDL levels of 21.7% (Figure). Patients who had an increase in LDL and non-HDL levels were excluded to control for potential confounding factors such as dietary changes, discontinuation of ezetimibe therapy, nonadherence to ezetimibe, and medication changes that impacted follow-up laboratory tests such as discontinuation of a statin. Fifteen patients experienced an increase in LDL or non-HDL levels. After excluding these patients, those without CKD had a mean reduction in LDL levels of 28.0% and non-HDL levels of 25.5%. Nineteen (13.9%) patients without CKD experienced a muscle-related AE (Table 2). One patient discontinued ezetimibe and statin use following a Lyme disease diagnosis due to concerns over potential muscle-related AEs.
Patients with CKD had a mean reduction in LDL and non-HDL levels of 27.0% and 24.8%, respectively. Patients with an increase in LDL or non-HDL levels were also excluded to help control for potential confounding factors. After excluding 4 patients with increased LDL and non-HDL levels, the mean reduction in LDL and non-HDL levels was 30.5% and 27.5%, respectively. Five (13.9%) patients with CKD experienced muscle-related AEs thought to be due to ezetimibe. Other AEs (eg, urticaria, diarrhea, reflux, dizziness, headache, upset stomach) were reported that led to discontinuation of ezetimibe, but only muscle-related AEs were analyzed.
Discussion
This retrospective chart review found larger reductions in LDL and non-HDL levels for patients with CKD than reported in the literature.4 Based on the findings that indicate a greater cholesterol reduction with ezetimibe, the results suggest an underutilization of ezetimibe in clinical practice, which may be due to clinicians favoring statin therapy and overlooking ezetimibe as a viable option based on recommendation in earlier guidelines. The 2022 guidelines transitioned from a statin focus to a focus on LDL targets and goals.2
According to the ACC, there is evidence to support a direct relationship between LDL-C levels, atherosclerosis progression, and ASCVD event risk.2 Absolute LDL-C level reduction is directly associated with ASCVD risk reduction which supports the LDL hypothesis. There appears to be no specific LDL-C level below which benefit ceases.2 This suggests that lower LDL-C targets (< 55 mg/dL) should be used when clinically indicated. Many patients are either unable to reach their goal LDL levels with statin monotherapy or are unable to tolerate statin therapy at higher doses, which may require additional pharmacotherapy to reach goal LDL-C. The ACC expert consensus pathway recommends ezetimibe as the initial add-on treatment to statins.2 The RACING trial showed the benefit of adding ezetimibe to a moderate-intensity statin vs increasing to a high-intensity statin dose. This trial found patients had lower LDL levels and lower rates of intolerances, which further supports ezetimibe use.6
This quality improvement project assessed LDL and non-HDL level reduction in patients with CKD. As anticipated, there was greater reduction in LDL and non-HDL levels seen in patients with CKD. The SHARP-CKD trial also found reductions in LDL levels with ezetimibe in patients with CKD.7 Given the reduction in LDL and non-HDL levels with ezetimibe in patients with or without CKD, add-on therapy of ezetimibe should be recommended for patients who do not achieve their LDL goals with statin therapy or for patients who intolerant to statin therapy.
The ezetimibe package insert reports myalgias incidence to be < 5% in patients and research has shown up to a 20% incidence of muscle-related AEs with statin therapy.3,10 Based on the package information reporting increased AUC values of ezetimibe and its metabolites in patients with severe renal disease, it was anticipated there may be an increased risk of muscle-related AEs in patients with CKD.3 However, this study found the same incidence of muscle-related AEs in patients with and without CKD. Previous research on statin-intolerant patients found the incidence of muscle-related AEs with ezetimibe to be 23.0% and 28.8%.11,12 This increased incidence of muscle-related AEs may be the result of including patients with a history of statin intolerance. Collectively, data from clinical trials and this study indicate that patients with prior intolerances to statins appear to have a higher likelihood of developing a muscle-related AEs with ezetimibe.11,12 Clinicians and patients should be educated on the potential for these AEs and be aware that the likelihood may be greater if there is a history of statin intolerance. To our knowledge, this was the first study to evaluate muscle-related AEs with ezetimibe in patients with and without CKD.
Limitations
This retrospective chart review was performed over a prespecified period and only patients initiated on ezetimibe by a PACT pharmacist were included. This study did not assess the percentage of LDL reduction in patients on concomitant statins vs those who were not on concomitant statins. The study only included 173 patients. Additionally, the study was primarily composed of White men and may not be representative of other populations. In addition, veterans may not be representative of the general population given their high comorbidity burden and other exposures. Some reported muscle-related AEs associated with ezetimibe may be attributed to the nocebo effect.
Conclusions
The results of this retrospective chart review suggest there may be a larger mean reduction in LDL and non-HDL levels seen with ezetimibe therapy than reported within the literature. There was a larger mean reduction in LDL and non-HDL levels in patients with CKD than in patients without CKD. Additionally, there were the same rates of muscle-related AEs with ezetimibe therapy in patients with and without CKD. The rates of muscle-related AEs with ezetimibe therapy were higher than reported in the medication’s package insert, but lower than reported in literature that included statin-intolerant patients. These results indicate there may be a benefit to an increase in use of ezetimibe in clinical practice due to its increased effectiveness and safety in patients with and without CKD. Ultimately, this can help patients achieve their LDL goals as recommended by ACC clinical practice guidelines.
Statins are widely used to reduce low-density lipoprotein (LDL) and non-high-density lipoprotein (HDL) levels for the prevention of atherosclerotic cardiovascular disease (ASCVD).1 However, despite maximally tolerated statin therapy, many patients may not reach their LDL and non-HDL goals. Some patients may experience adverse events (AEs), particularly muscle-related AEs, which can limit the use of these medications.
The 2022 American College of Cardiology (ACC) expert consensus pathway recommends a goal LDL of < 55 mg/dL in very high-risk patients, defined as those with a history of multiple major ASCVD events or 1 major ASCVD event and multiple high-risk conditions.2 Major ASCVD events include acute coronary syndrome within 12 months, history of myocardial infarction (MI) or ischemic stroke, and symptomatic peripheral arterial disease (ie, claudication with ankle-brachial index < 0.85 or previous revascularization or amputation). Factors for being considered high risk include age > 65 years, heterozygous familial hypercholesterolemia, history of prior coronary artery bypass surgery or percutaneous coronary intervention outside the major ASCVD events, diabetes, hypertension, chronic kidney disease (CKD) (estimated glomerular filtration rate [eGFR] 15-59 mL/min/1.73 m2), current smoking, persistently elevated LDL cholesterol (LDL-C) levels despite maximally tolerated statin therapy and ezetimibe, and history of congestive heart failure.2 For these patients, statin therapy alone may not achieve LDL goal.
The ACC recommends ezetimibe as the initial nonstatin therapy in patients who are not at their goal LDL.2 Ezetimibe works by inhibiting Niemann-Pick C1-Like 1 protein, which causes reduced cholesterol absorption in the small intestine.2,3 Previous studies have shown the benefit of ezetimibe for LDL reduction and ASCVD prevention.4-7 The 2015 IMPROVE-IT study found the combination of simvastatin and ezetimibe resulted in a significantly lower risk of cardiovascular events than simvastatin monotherapy. IMPROVE-IT also reported a further clinical benefit when lower LDL targets (ie, < 55 mg/dL) are achieved, which aligns with the expert consensus pathway recommendations for a lower LDL goal for very high-risk patients.2,5
The RACING trial found that treatment with a moderate-intensity statin and ezetimibe was noninferior to treatment with a high-intensity statin for the primary outcome of occurrence of cardiovascular death, major cardiovascular events, or nonfatal stroke within 3 years. The combination of moderate-intensity statin and ezetimibe achieved lower LDL-C levels and lower incidence of drug intolerance compared to high intensity statin monotherapy.6 The SHARP-CKD study assessed major atherosclerotic events in patients with CKD who had no history of MI or coronary revascularization. The study found that lowering LDL-C with the combination of simvastatin plus ezetimibe safely reduces the risk of major atherosclerotic events in a wide range of patients with CKD.7
Lastly, the 2019 EWTOPIA 75 study found that ezetimibe noted a statistically significant reduction in the incidence of the composite of sudden cardiac death, MI, coronary revascularization, or stroke compared to placebo. Ezetimibe showed benefits in preventing ASCVD events independently of statin therapy.8 These clinical trials provided evidence for the efficacy of ezetimibe for secondary or primary prevention of ASCVD, patients with CKD, and patients who are not at their LDL goal despite maximally tolerated statin therapy.
Reductions in LDL levels with ezetimibe are reported to be 15% to 19% for monotherapy and 13% to 25% when used in combination with a statin.4 Given that the ACC now recommends lower LDL goals, patients may need additional lowering despite taking maximally tolerated statin therapy.2 Additionally, the package insert for ezetimibe reports increased area under the curve (AUC) values of ezetimibe and its metabolites in patients with severe renal disease. It is anticipated that ezetimibe may show an increased reduction of LDL and non-HDL, but there may also be an increased risk for muscle-related AEs.3
This quality-assurance quality improvement project investigated the use of ezetimibe in patients with CKD to determine whether there is further LDL and non-HDL reduction in this patient population. It sought to determine the LDL and non-HDL percentage reduction in patients with and without CKD at the Wilkes-Barre Veterans Affairs Medical Center (WBVAMC) and whether there is an increased risk for muscle-related AEs. Determining the percentage reduction of LDL and non-HDL within this population can help increase use of ezetimibe in patients not at their LDL or non-HDL goal or for those patients unable to tolerate statin therapy.
Methods
This single-center retrospective chart review investigated patients prescribed ezetimibe by a patient aligned care team (PACT) pharmacist at WBVAMC between September 1, 2021, and September 1, 2023. This project was determined to be nonresearch by the Veterans Integrated Service Network 4 multisite institutional review board. Patients were excluded from the review if they started taking ezetimibe outside of the prespecified time frame, if ezetimibe was initiated by a non-WBVAMC PACT pharmacist, or if there was no follow-up lipid panel obtained within 6 months of initiation of ezetimibe.
The primary outcomes were to determine the percentage mean change in LDL and non-HDL reduction and the incidence of muscle-related AEs after initiation of ezetimibe in patients without CKD. The secondary outcomes were to determine the percentage mean change in LDL and non-HDL levels and the incidence of muscle-related AEs after initiation of ezetimibe in patients with CKD. For this study, CKD was defined as a patient having an eGFR 15 to 60 ml/min/1.73 m2. Non-HDL is the combination of LDL-C and very LDL-C and represents all potentially atherogenic particles. The 2022 Expert Consensus Pathway included non-HDL goals in addition to LDL goals.2 Non-HDL cholesterol levels can be used for patients with elevated triglycerides where LDL levels may not be as accurate. To account for instances of elevated triglycerides, this study assessed changes in both LDL and non-HDL levels.
Data were collected from the US Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) and recorded in a spreadsheet. Collected data included age, sex, race, concomitant cholesterol-lowering medications (statin, proprotein convertase subtilisin/kexin type 9 [PCSK9] inhibitor, bempedoic acid, fish oil, niacin, bile acid sequestrants, and fibrates), baseline lipid panel, lipid panel within 6 months of ezetimibe initiation, and eGFR level. If the patient’s LDL or non-HDL levels worsened on the follow-up lipid panel, their baseline LDL and non-HDL levels were used to calculate the percentage reduction; thus, the percentage reduction would be 0%. This strategy was used in prior research, notably the IMPROVE-IT and SHARP-CKD trials.
Ezetimibe 5 mg once daily was used in this study based on a 2008 VA study that evaluated the use of ezetimibe 5 mg vs ezetimibe 10 mg and the percentage reduction of LDL with each dose. The study found no significant difference between the 5 mg and 10 mg dose.9 Most patients included in this study received the 5 mg dose.
Results
This retrospective chart review consisted of 173 patients, 137 (79.2%) without CKD and 36 (20.8%) with CKD at baseline. The mean age was 69.6 years, 155 (89.6%) patients were male, and 18 (10.4%) were female. There were 164 concomitant medications, including 115 patients prescribed a statin and 38 patients prescribed fish oil (Table 1).
Patients without CKD had mean reductions in LDL levels of 23.5% and non-HDL levels of 21.7% (Figure). Patients who had an increase in LDL and non-HDL levels were excluded to control for potential confounding factors such as dietary changes, discontinuation of ezetimibe therapy, nonadherence to ezetimibe, and medication changes that impacted follow-up laboratory tests such as discontinuation of a statin. Fifteen patients experienced an increase in LDL or non-HDL levels. After excluding these patients, those without CKD had a mean reduction in LDL levels of 28.0% and non-HDL levels of 25.5%. Nineteen (13.9%) patients without CKD experienced a muscle-related AE (Table 2). One patient discontinued ezetimibe and statin use following a Lyme disease diagnosis due to concerns over potential muscle-related AEs.
Patients with CKD had a mean reduction in LDL and non-HDL levels of 27.0% and 24.8%, respectively. Patients with an increase in LDL or non-HDL levels were also excluded to help control for potential confounding factors. After excluding 4 patients with increased LDL and non-HDL levels, the mean reduction in LDL and non-HDL levels was 30.5% and 27.5%, respectively. Five (13.9%) patients with CKD experienced muscle-related AEs thought to be due to ezetimibe. Other AEs (eg, urticaria, diarrhea, reflux, dizziness, headache, upset stomach) were reported that led to discontinuation of ezetimibe, but only muscle-related AEs were analyzed.
Discussion
This retrospective chart review found larger reductions in LDL and non-HDL levels for patients with CKD than reported in the literature.4 Based on the findings that indicate a greater cholesterol reduction with ezetimibe, the results suggest an underutilization of ezetimibe in clinical practice, which may be due to clinicians favoring statin therapy and overlooking ezetimibe as a viable option based on recommendation in earlier guidelines. The 2022 guidelines transitioned from a statin focus to a focus on LDL targets and goals.2
According to the ACC, there is evidence to support a direct relationship between LDL-C levels, atherosclerosis progression, and ASCVD event risk.2 Absolute LDL-C level reduction is directly associated with ASCVD risk reduction which supports the LDL hypothesis. There appears to be no specific LDL-C level below which benefit ceases.2 This suggests that lower LDL-C targets (< 55 mg/dL) should be used when clinically indicated. Many patients are either unable to reach their goal LDL levels with statin monotherapy or are unable to tolerate statin therapy at higher doses, which may require additional pharmacotherapy to reach goal LDL-C. The ACC expert consensus pathway recommends ezetimibe as the initial add-on treatment to statins.2 The RACING trial showed the benefit of adding ezetimibe to a moderate-intensity statin vs increasing to a high-intensity statin dose. This trial found patients had lower LDL levels and lower rates of intolerances, which further supports ezetimibe use.6
This quality improvement project assessed LDL and non-HDL level reduction in patients with CKD. As anticipated, there was greater reduction in LDL and non-HDL levels seen in patients with CKD. The SHARP-CKD trial also found reductions in LDL levels with ezetimibe in patients with CKD.7 Given the reduction in LDL and non-HDL levels with ezetimibe in patients with or without CKD, add-on therapy of ezetimibe should be recommended for patients who do not achieve their LDL goals with statin therapy or for patients who intolerant to statin therapy.
The ezetimibe package insert reports myalgias incidence to be < 5% in patients and research has shown up to a 20% incidence of muscle-related AEs with statin therapy.3,10 Based on the package information reporting increased AUC values of ezetimibe and its metabolites in patients with severe renal disease, it was anticipated there may be an increased risk of muscle-related AEs in patients with CKD.3 However, this study found the same incidence of muscle-related AEs in patients with and without CKD. Previous research on statin-intolerant patients found the incidence of muscle-related AEs with ezetimibe to be 23.0% and 28.8%.11,12 This increased incidence of muscle-related AEs may be the result of including patients with a history of statin intolerance. Collectively, data from clinical trials and this study indicate that patients with prior intolerances to statins appear to have a higher likelihood of developing a muscle-related AEs with ezetimibe.11,12 Clinicians and patients should be educated on the potential for these AEs and be aware that the likelihood may be greater if there is a history of statin intolerance. To our knowledge, this was the first study to evaluate muscle-related AEs with ezetimibe in patients with and without CKD.
Limitations
This retrospective chart review was performed over a prespecified period and only patients initiated on ezetimibe by a PACT pharmacist were included. This study did not assess the percentage of LDL reduction in patients on concomitant statins vs those who were not on concomitant statins. The study only included 173 patients. Additionally, the study was primarily composed of White men and may not be representative of other populations. In addition, veterans may not be representative of the general population given their high comorbidity burden and other exposures. Some reported muscle-related AEs associated with ezetimibe may be attributed to the nocebo effect.
Conclusions
The results of this retrospective chart review suggest there may be a larger mean reduction in LDL and non-HDL levels seen with ezetimibe therapy than reported within the literature. There was a larger mean reduction in LDL and non-HDL levels in patients with CKD than in patients without CKD. Additionally, there were the same rates of muscle-related AEs with ezetimibe therapy in patients with and without CKD. The rates of muscle-related AEs with ezetimibe therapy were higher than reported in the medication’s package insert, but lower than reported in literature that included statin-intolerant patients. These results indicate there may be a benefit to an increase in use of ezetimibe in clinical practice due to its increased effectiveness and safety in patients with and without CKD. Ultimately, this can help patients achieve their LDL goals as recommended by ACC clinical practice guidelines.
Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24) e285-e350. doi:10.1016/j.jacc.2018.11.003
Writing Committee, Lloyd-Jones DM, Morris PB, et al. 2022 ACC expert consensus decision pathway on the role of nonstatin therapies for LDL-cholesterol lowering in the management of atherosclerotic cardiovascular disease risk: a report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2022;80(14):1366-1418. doi:10.1016/j.jacc.2022.07.006
US Food and Drug Administration. Ezetimibe. 2007. Accessed April 1, 2025. https://www.accessdata.fda.gov/drugsatfda_docs/label/2008/021445s019lbl.pdf
Singh A, Cho LS. Nonstatin therapy to reduce low-density lipoprotein cholesterol and improve cardiovascular outcomes. Cleve Clin J Med. 2024;91(1):53-63. doi:10.3949/ccjm.91a.23058
Cannon CP, Blazing MA, Giugliano RP, et al. Ezetimibe added to statin therapy after acute coronary syndromes. N Engl J Med. 2015;372(25):2387-2397. doi:10.1056/NEJMoa1410489
Kim B, Hong S, Lee Y, et al. Long-term efficacy and safety of moderate-intensity statin with ezetimibe combination therapy versus high-intensity statin monotherapy in patients with atherosclerotic cardiovascular disease (RACING): a randomised, open-label, non-inferiority trial. Lancet. 2022;400(10349):380-390. doi:10.1016/S0140-6736(22)00916-3
Baigent C, Landray MJ, Reith C, et al. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011;377(9784):2181-2192. doi:10.1016/S0140-6736(11)60739-3
Ouchi Y, Sasaki J, Arai H, et al. Ezetimibe lipid-lowering trial on prevention of atherosclerotic cardiovascular disease in 75 or older (EWTOPIA 75): a randomized, controlled trial. Circulation. 2019;140:992-1003. doi:10.1161/CIRCULATIONAHA.118.039415
Baruch L, Gupta B, Lieberman-Blum SS, Agarwal S, Eng C. Ezetimibe 5 and 10 mg for lowering LDL-C: potential billion-dollar savings with improved tolerability. Am J Manag Care. 2008;14(10):637-641. https://www.ajmc.com/view/oct08-3644p637-641
Stroes ES, Thompson PD, Corsini A, et al. Statin-associated muscle symptoms: impact on statin therapy-European Atherosclerosis Society Consensus Panel Statement on Assessment, Aetiology and Management. Eur Heart J. 2015;36(17):1012-1022. doi:10.1093/eurheartj/ehv043
Stroes E, Colquhoun D, Sullivan D, et al. Anti-PCSK9 antibody effectively lowers cholesterol in patients with statin intolerance: the GAUSS-2 randomized, placebo-controlled phase 3 clinical trial of evolocumab. J Am Coll Cardiol. 2014;63(23):2541-2548. doi:10.1016/j.jacc.2014.03.019
Nissen SE, Stroes E, Dent-Acosta RE, et al. Efficacy and tolerability of evolocumab vs ezetimibe in patients with muscle-related statin intolerance: the GAUSS-3 randomized clinical trial. JAMA. 2016;315(15):1580-1590. doi:10.1001/jama.2016.3608
Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24) e285-e350. doi:10.1016/j.jacc.2018.11.003
Writing Committee, Lloyd-Jones DM, Morris PB, et al. 2022 ACC expert consensus decision pathway on the role of nonstatin therapies for LDL-cholesterol lowering in the management of atherosclerotic cardiovascular disease risk: a report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2022;80(14):1366-1418. doi:10.1016/j.jacc.2022.07.006
US Food and Drug Administration. Ezetimibe. 2007. Accessed April 1, 2025. https://www.accessdata.fda.gov/drugsatfda_docs/label/2008/021445s019lbl.pdf
Singh A, Cho LS. Nonstatin therapy to reduce low-density lipoprotein cholesterol and improve cardiovascular outcomes. Cleve Clin J Med. 2024;91(1):53-63. doi:10.3949/ccjm.91a.23058
Cannon CP, Blazing MA, Giugliano RP, et al. Ezetimibe added to statin therapy after acute coronary syndromes. N Engl J Med. 2015;372(25):2387-2397. doi:10.1056/NEJMoa1410489
Kim B, Hong S, Lee Y, et al. Long-term efficacy and safety of moderate-intensity statin with ezetimibe combination therapy versus high-intensity statin monotherapy in patients with atherosclerotic cardiovascular disease (RACING): a randomised, open-label, non-inferiority trial. Lancet. 2022;400(10349):380-390. doi:10.1016/S0140-6736(22)00916-3
Baigent C, Landray MJ, Reith C, et al. The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial. Lancet. 2011;377(9784):2181-2192. doi:10.1016/S0140-6736(11)60739-3
Ouchi Y, Sasaki J, Arai H, et al. Ezetimibe lipid-lowering trial on prevention of atherosclerotic cardiovascular disease in 75 or older (EWTOPIA 75): a randomized, controlled trial. Circulation. 2019;140:992-1003. doi:10.1161/CIRCULATIONAHA.118.039415
Baruch L, Gupta B, Lieberman-Blum SS, Agarwal S, Eng C. Ezetimibe 5 and 10 mg for lowering LDL-C: potential billion-dollar savings with improved tolerability. Am J Manag Care. 2008;14(10):637-641. https://www.ajmc.com/view/oct08-3644p637-641
Stroes ES, Thompson PD, Corsini A, et al. Statin-associated muscle symptoms: impact on statin therapy-European Atherosclerosis Society Consensus Panel Statement on Assessment, Aetiology and Management. Eur Heart J. 2015;36(17):1012-1022. doi:10.1093/eurheartj/ehv043
Stroes E, Colquhoun D, Sullivan D, et al. Anti-PCSK9 antibody effectively lowers cholesterol in patients with statin intolerance: the GAUSS-2 randomized, placebo-controlled phase 3 clinical trial of evolocumab. J Am Coll Cardiol. 2014;63(23):2541-2548. doi:10.1016/j.jacc.2014.03.019
Nissen SE, Stroes E, Dent-Acosta RE, et al. Efficacy and tolerability of evolocumab vs ezetimibe in patients with muscle-related statin intolerance: the GAUSS-3 randomized clinical trial. JAMA. 2016;315(15):1580-1590. doi:10.1001/jama.2016.3608
Multiagent AI Systems in Health Care: Envisioning Next-Generation Intelligence
Artificial intelligence (AI) is rapidly evolving, with large language models (LLMs) marking a significant milestone in processing and generating human-like responses to natural language prompts. However, this advancement only signals the beginning of a more profound transformation in AI capabilities. The development of AI agents represents a new paradigm at the forefront of this evolution.
BACKGROUND
AI agents represent a leap forward from traditional LLM applications. While definitions may vary slightly among technology developers, the core concept remains: these agents are autonomous software entities designed to interact with their environment, make independent decisions, and execute tasks based on predefined goals.1-3 What sets AI agents apart is their combination of sophisticated components within structured architectures. At their core, AI agents incorporate an LLM for response generation, which is augmented by a suite of tools to optimize workflow and complete tasks, memory capabilities for personalized interactions, and autonomous reasoning. This combination allows AI agents to plan, create subtasks, gather information, and learn iteratively from their own experiences or other AI agents.
The true potential of this technology becomes apparent when multiple AI agents collaborate within multiagent AI systems. This concept introduces a new level of flexibility and capability in tackling complex tasks. Autogen, CrewAI, and LangChain offer various agent network configurations, including hierarchical, sequential, conditional, or even parallel task execution.4-6 This adaptability opens up a world of possibilities across various industries, but perhaps nowhere is the potential impact more exciting and profound than in health care.
AI agents in health care present an opportunity to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. This review examines 3 scenarios that illustrate the impact of AI agents in health care: a hypothetical sepsis management system, chronic disease management, and hospital patient flow optimization. This article will provide a detailed look at the technical implementation challenges, including the integration with existing health care IT systems, data privacy considerations, and the crucial role of explainable AI in maintaining trust and transparency.
It is challenging to implement AI agents in health care. Concerns include ensuring data quality and mitigating bias, seamlessly integrating these systems into existing clinical workflows, and navigating the complex ethical considerations that arise when deploying autonomous systems in health care. The integration with Internet of Things (IoT) devices for real-time patient data monitoring and the development of more sophisticated natural language interfaces to enhance future human-AI collaboration.
The adoption of AI agents in health care is only beginning, and it promises to be transformative. As AI continues to evolve, a comprehensive understanding of its applications, limitations, and ethical considerations is essential. This report provides a comprehensive overview of the current state, potential applications, and future directions of AI agents in health care, offering insights valuable to researchers, clinicians, and policymakers.
MultiAgent AI architecture
Sepsis Management
Despite advancements in broad-spectrum antibiotics, imaging, and life support systems, mortality rates associated with sepsis remain high. The complexity of optimizing care in clinical settings has hindered progress in managing sepsis. Previous attempts to develop predictive sepsis models have proven challenging.7 This report proposes a multiagent AI system designed to enhance comprehensive patient monitoring and care through coordinated AI-driven interventions.
Data Collection and Integration Agent. Powered by a controlled vocabulary to specify all data, the primary function for the data collection and integration agent is to clean, transform, and organize patient data from structured and unstructured sources. This agent prepares succinct summaries of consultant notes and formats data for human and machine consumption. All numerical data are presented graphically, including relevant historical data trends. The agent also digitally captures all orders in a structured format using a specified controlled vocabulary. This structured data feed supports the output of other agents, including documentation, treatment planning, and risk stratification, while also supplying the data structures for future training.
Diagnostic Agent. Critical illness is characterized by multiple abnormalities across a wide array of tests, ranging from plain chest X-ray, computed tomography (CT), blood cell composition, plasma chemistry, and microscopic evaluation of specimens. Additionally, life support parameters provide insights into disease severity and can inform management recommendations. These data offer a wide array of visual and numerical data to be used as input for computation, recommendation, and further training. For example, to evaluate fluid overload on chest X-rays or tissue histopathology slides, an AI agent can leverage deep learning models such as convolutional neural networks and vision transformers to analyze images like radiographs and histopathology slides.8,9 Recurrent neural networks or transformer models process sequential data like time-series vital signs. The agent also implements ensemble methods that combine multiple machine learning algorithms to enhance diagnostic accuracy.
Risk Stratification Agent. This assesses severity and predicts potential outcomes. Morbidity and mortality risks are calculated using an established scoring system and individualized based on the history of other agents’ conditional patients. These are presented graphically, with major risk factors highlighted for explainability.
Treatment Recommendation Agent. Using a reinforcement learning framework supplemented by up-to-date clinical guidelines, this system leverages historical data structured with standardized vocabulary to analyze patients with similar clinical features. Training is also conducted on the patient’s physiological data. All recommendations are presented via a dedicated user interface in a readable format, along with recommendations for editable, orderable items, references, and full-text snippets from previous research. Stop rules end computing if confidence in recommendations is too broad or no clear pathway can be computed with certainty, prompting human mitigation.
Resource Management Agent. This agent coordinates hospital resources using constraint programming techniques for optimal resource allocation, uses queueing theory models to predict and manage patient flow, and implements genetic algorithms for complex scheduling problems.10,11
Monitoring and Alert Agent. By tracking patients’ progress and alerting staff to changes, this agent uses anomaly detection algorithms to identify unusual patterns in patient data and implement time-series forecasting models, such as autoregressive integrated moving average and prophet, to predict future patient states. The agent also uses stream-processing techniques for real-time data analysis.12,13
Documentation and Reporting Agent. This agent maintains comprehensive medical records and generates reports. It employs advanced natural language processing techniques for automated report generation, uses advanced LLMs fine-tuned on medical corpora for narrative creation, and implements information-retrieval techniques to efficiently query patient records.
CLINICAL CASE STUDIES
To illustrate the functionality of a multiagent system, this report examines its application for managing sepsis. The data collection and integration agent continuously aggregates patient data from various sources, normalizing and timestamping it for consistent processing. The diagnostic agent analyzes this integrated data in real time, applying sepsis criteria and utilizing a deep learning model trained on a large sepsis dataset to detect subtle patterns.
The risk stratification agent calculates severity scores, such as the Sepsis-related Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Acute Physiology and Chronic Health Evaluation II, upon detecting a possible sepsis case.14 It predicts the likelihood of specific outcomes and estimates the potential trajectory of the patient’s condition for the next 24 to 48 hours. Based on this assessment, the treatment recommendation agent suggests an initial treatment plan, including appropriate antibiotics, fluid resuscitation protocols, and vasopressor recommendations, recommendations when indicated.
Concurrently, the resource management agent checks the availability of necessary resources and prioritizes allocation based on the severity. The monitoring agent tracks the patient’s response to interventions in real time, alerting the care team to any concerning changes or lack of expected improvement. Throughout this process, the documentation agent ensures that all actions, responses, and outcomes are meticulously recorded in a structured format and generates real-time updates for the patient’s electronic health record (EHR) and preparing summary reports for handoffs between care teams.
Administrative Workflow Support
Modern health care operations are resource-intensive, requiring coordination of advanced imaging, procedures, laboratory testing, and professional consultations.15 AI-powered health care administrative workflow systems are revolutionizing how medical facilities coordinate patient care. For patients with chronic cough, these systems seamlessly integrate scheduling, imaging, diagnostics, and follow-up care into a cohesive process that reduces administrative burden while improving patient outcomes. Through an intuitive interface and automated assistance, health care practitioners (HCPs) can track patient progress from initial consultation through diagnosis and treatment.
The process begins when an HCP enters a patient into the system, which triggers an automated CT scan scheduling system. The system considers factors like urgency, facility availability, and patient preferences to suggest optimal appointment times. Once imaging is complete, AI agents analyze the radiology reports, extract key findings, and generate structured summaries that highlight critical information such as “mild bronchial wall thickening with patchy ground-glass opacities” or “findings consistent with chronic bronchitis.”
Based on these findings, the system automatically generates evidence-based recommendations for follow-up care, such as pulmonology consultations or follow-up imaging in 3 months. These recommendations are presented to the ordering clinician, along with suggested appointment slots for specialist consultations. The system then manages the coordination of multiple appointments, ensuring each step in the patient’s care plan is properly sequenced and scheduled.
The entire process is monitored through a comprehensive dashboard that provides real-time updates on patient status, appointment schedules, and clinical recommendations. HCPs can track which patients require immediate attention, view upcoming appointments, and monitor the progress of ongoing care plans.
Multiagent AI Operation Optimization
Hospitals are complex entities that must function at different scales and respond in an agile, timely manner at all hours, deploying staff at various positions.16 A system of AI agents can receive signals from sensors monitoring foot traffic in the emergency department and trauma unit, as well as the availability of operating room staff, equipment, and intensive care unit beds. Smart sensors enable this monitoring through IoT networks. These networks benefit from advances in adaptive and consensus networking algorithms, along with recent advances in bioengineering and biocomputing.17
For example, in the case of imaging for suspected abdominal obstruction, an AI agent tasked with scheduling CTs could time the patient’s arrival based on acuity. Another AI agent could alert staff transporting the patient to the CT appointment, with the next location contingent on a clinical decision to proceed to the operating room. Yet another AI agent could summarize radiology interpretations and alert the surgery and anesthesia teams to a potential case, while others could notify operating room staff of equipment needs or reserve a bed. In this paradigm, AI agents facilitate more precise and timely communication between multiple staff members.
TECHNICAL IMPLEMENTATION
Large Language Models
Each agent uses a different LLM optimized for its specific task. For example, the diagnostic agent uses an LLM pretrained on a large corpus of biomedical literature and fine-tuned on a dataset of confirmed sepsis cases and their presentations.18 It implements few-shot learning techniques to adapt to rare or atypical presentations. The treatment recommendation agent also uses an LLM, employing a retrieval-augmented generation approach to access the latest clinical guidelines during inference. The documentation agent uses another advanced language model, fine-tuned on a large corpus of high-quality medical documentation, implementing controlled text generation techniques and utilizing a separate smaller model for real-time error checking and correction.
Interagent Quality Control
Agents learn from their own experience and the experience of other agents. They are equipped with user-defined rule-based and model-based systems for quality assurance, with clear stopping rules for human involvement and mitigation.
Sophisticated quality control measures bolster the system’s reliability, including ensemble techniques for result comparison, redundancy for critical tasks, and automatic human review for disagreements above a certain threshold. Each agent provides a calibrated confidence score with its output, used to weigh inputs in downstream tasks and trigger additional checks for low-confidence outputs.
A dedicated quality control agent monitors output from all agents, employing both supervised and unsupervised anomaly detection techniques. Feedback loops allow agents to evaluate the quality and utility of information received from other agents. The system implements a multiarmed bandit approach to dynamically adjust the influence of different agents based on their performance and periodically retrains agent models using federated learning techniques.19
Electronic Health Record Integration
Seamless EHR integration is crucial for practical implementation. The system has secure application programming interface access to various EHR platforms, implements OAuth 2.0 for authentication, and use HTTPS with perfect forward secrecy for all communications.20 It works with HL7 FHIR to ensure interoperability and uses SNOMED CT for clinical terminology to ensure semantic interoperability across different EHRs.21,22
The system implements a multilevel approval system for write-backs to EHRs, with different thresholds based on the information’s criticality. It uses digital signatures to ensure the integrity and nonrepudiation of AI-generated entries and implements blockchain technology to create an immutable and distributed ledger of all AI system actions.23
Decision Transparency
To ensure transparency in decision-making processes, the system applies techniques (eg, local interpretable model-agnostic explanations and Shapley additive explanations) to provide insights into agent decision-making processes.24-26 It provides customized visualizations for different stakeholders and allows users to explore alternative decision paths through what-if scenario modeling.27
The system provides calibrated confidence indicators for each recommendation or decision, implementing a novel confidence calibration agent that continuously monitors and adjusts confidence scores based on observed outcomes.
Continuous Learning and Adaptation
The system employs several techniques to remain current with evolving medical knowledge. Federated learning includes information from diverse datasets across multiple institutions without compromising patient privacy.28 A/B testing is used to safely deploy and compare new agent versions in controlled settings, implementing multiarmed bandit algorithms to efficiently explore new models while minimizing potential negative impacts. Human-in-the-loop learning and active learning techniques are used to incorporate feedback from HCPs and efficiently solicit expert input on the most informative data.29
CLINICAL IMPLICATIONS
The implementation of multiagent AI systems in health care has several potential benefits: enhanced diagnostic accuracy, personalized treatment, improved efficiency, continuous monitoring, and resource optimization. A recent review of AI sepsis predictive models exhibited superior results to standard clinical scoring methods like qSOFA.30 In oncology, such systems can result in more tailored treatments, enhancing outcomes.31 The implementation of an ambient dictation system can improve workflow and prevent HCP burnout.32
ETHICAL CONSIDERATIONS AND AI OVERSIGHT
Integrating AI agents into health care raises significant ethical considerations that must be carefully addressed to ensure equitable and effective care delivery. One primary concern involves cultural and linguistic competency, as AI systems may struggle with cultural nuances, idioms, and context-specific communication patterns. This becomes particularly challenging in regions with diverse ethnic populations or immigrant communities, where medical terminology may not have direct translations and cultural beliefs significantly influence health care decisions. AI systems also may inherit and amplify existing biases in health care delivery, whether through HCP bias reflected in training data, patient bias affecting acceptance of AI-assisted care, or demographic underrepresentation during system development.
AI agents present unique opportunities for improving health care access and outcomes through community engagement, though such initiatives require thoughtful implementation. Predictive analytics can identify high-risk individuals within communities who may benefit from preventive care, while analysis of social determinants of health can enable more targeted interventions. However, these capabilities must be balanced with privacy concerns and the risk of surveillance, particularly in communities that distrust health care institutions. The potential for AI to bridge health care gaps must be weighed against the need to maintain cultural sensitivity and community trust.
The governance and oversight of health care AI systems requires a multistakeholder approach with clear lines of responsibility and accountability. This includes involvement from government health care agencies, professional medical associations, ethics boards, and independent auditors, all working together to establish and enforce standards while monitoring system performance and addressing potential biases. Health care organizations must maintain transparent policies about AI use, implement regular monitoring and evaluation protocols, and establish precise mechanisms for patient feedback and grievance resolution. Ongoing assessment and adjustment of these systems, informed by community feedback and outcomes data, will be crucial for their ethical implementation, ensuring that AI agents complement, rather than replace, human judgment and cultural sensitivity.
FUTURE DIRECTIONS
Despite the potential benefits, implementing multiagent AI systems in health care faces significant challenges that require careful consideration. Beyond the fundamental issues such as data quality and bias mitigation, health care organizations struggle with fragmented systems, inconsistent data formats, and varying quality. Technical infrastructure requirements are substantial, particularly in rural or underserved areas that lack robust networks and cybersecurity. HCPs already face significant cognitive load and time pressures, making integrating AI agents into existing workflows particularly challenging. There is also the critical issue of transparency and interpretability, as health care decisions require clear reasoning and accountability that many black-box AI systems struggle to provide.
The legal landscape introduces another layer of complexity, particularly regarding liability, consent, and privacy questions. When AI agents contribute to medical decisions, establishing clear lines of responsibility becomes crucial. There are also serious concerns about algorithmic fairness and the potential for AI systems to perpetuate or amplify existing inequities. The cost of implementation remains a significant barrier, requiring substantial investment in technology, training, and ongoing maintenance while ensuring resources are not diverted from direct patient care. Moreover, HCPs may resist adoption due to concerns about job security, loss of autonomy, or skepticism about AI capabilities while paradoxically facing risks of overreliance on AI systems that could lead to the degradation of human clinical skills.
Addressing these challenges requires a multifaceted approach that combines technical solutions with organizational and policy changes. Health care organizations must implement rigorous data validation processes and interoperability standards while developing hybrid models that balance sophisticated AI capabilities with interpretable techniques. Extensive research and iterative design processes, with direct input from HCPs, are essential for successful integration. Establishing independent ethics boards to oversee system development and deployment, conducting multicenter randomized controlled trials, and creating clear regulatory frameworks will ensure safe and effective implementation. Success will ultimately depend on ongoing collaboration between technology developers, HCPs, policymakers, and patients, maintaining a steady focus on improving patient care and outcomes while carefully navigating the complex challenges of AI integration in health care.33-35
As multiagent AI systems in health care evolve, several exciting directions emerge. These include the integration of IoT and wearable devices, the development of more sophisticated natural language interfaces, and applying these systems to predictive maintenance of medical equipment.
CONCLUSIONS
The advent of multiagent AI systems in health care represents a paradigm shift in the approach to patient care, clinical decision making, and health care management. While these systems offer immense potential to transform health care delivery, their development and implementation must be guided by rigorous scientific validation, ethical considerations, and a patient-centered approach. The ultimate goal remains clear: harnessing the power of AI to improve patient outcomes, enhance the efficiency of health care delivery, and ultimately advance the health and well-being of patients.
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Borkowski AA, Jakey CE, Thomas LB, Viswanadhan N, Mastorides SM. Establishing a hospital artificial intelligence committee to improve patient care. Fed Pract. 2022;39(8):334-336. doi:10.12788/fp.0299
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Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P. Randomized controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health. 2024;6(5):e367-e373. doi:10.1016/S2589-7500(24)00047-5
Artificial intelligence (AI) is rapidly evolving, with large language models (LLMs) marking a significant milestone in processing and generating human-like responses to natural language prompts. However, this advancement only signals the beginning of a more profound transformation in AI capabilities. The development of AI agents represents a new paradigm at the forefront of this evolution.
BACKGROUND
AI agents represent a leap forward from traditional LLM applications. While definitions may vary slightly among technology developers, the core concept remains: these agents are autonomous software entities designed to interact with their environment, make independent decisions, and execute tasks based on predefined goals.1-3 What sets AI agents apart is their combination of sophisticated components within structured architectures. At their core, AI agents incorporate an LLM for response generation, which is augmented by a suite of tools to optimize workflow and complete tasks, memory capabilities for personalized interactions, and autonomous reasoning. This combination allows AI agents to plan, create subtasks, gather information, and learn iteratively from their own experiences or other AI agents.
The true potential of this technology becomes apparent when multiple AI agents collaborate within multiagent AI systems. This concept introduces a new level of flexibility and capability in tackling complex tasks. Autogen, CrewAI, and LangChain offer various agent network configurations, including hierarchical, sequential, conditional, or even parallel task execution.4-6 This adaptability opens up a world of possibilities across various industries, but perhaps nowhere is the potential impact more exciting and profound than in health care.
AI agents in health care present an opportunity to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. This review examines 3 scenarios that illustrate the impact of AI agents in health care: a hypothetical sepsis management system, chronic disease management, and hospital patient flow optimization. This article will provide a detailed look at the technical implementation challenges, including the integration with existing health care IT systems, data privacy considerations, and the crucial role of explainable AI in maintaining trust and transparency.
It is challenging to implement AI agents in health care. Concerns include ensuring data quality and mitigating bias, seamlessly integrating these systems into existing clinical workflows, and navigating the complex ethical considerations that arise when deploying autonomous systems in health care. The integration with Internet of Things (IoT) devices for real-time patient data monitoring and the development of more sophisticated natural language interfaces to enhance future human-AI collaboration.
The adoption of AI agents in health care is only beginning, and it promises to be transformative. As AI continues to evolve, a comprehensive understanding of its applications, limitations, and ethical considerations is essential. This report provides a comprehensive overview of the current state, potential applications, and future directions of AI agents in health care, offering insights valuable to researchers, clinicians, and policymakers.
MultiAgent AI architecture
Sepsis Management
Despite advancements in broad-spectrum antibiotics, imaging, and life support systems, mortality rates associated with sepsis remain high. The complexity of optimizing care in clinical settings has hindered progress in managing sepsis. Previous attempts to develop predictive sepsis models have proven challenging.7 This report proposes a multiagent AI system designed to enhance comprehensive patient monitoring and care through coordinated AI-driven interventions.
Data Collection and Integration Agent. Powered by a controlled vocabulary to specify all data, the primary function for the data collection and integration agent is to clean, transform, and organize patient data from structured and unstructured sources. This agent prepares succinct summaries of consultant notes and formats data for human and machine consumption. All numerical data are presented graphically, including relevant historical data trends. The agent also digitally captures all orders in a structured format using a specified controlled vocabulary. This structured data feed supports the output of other agents, including documentation, treatment planning, and risk stratification, while also supplying the data structures for future training.
Diagnostic Agent. Critical illness is characterized by multiple abnormalities across a wide array of tests, ranging from plain chest X-ray, computed tomography (CT), blood cell composition, plasma chemistry, and microscopic evaluation of specimens. Additionally, life support parameters provide insights into disease severity and can inform management recommendations. These data offer a wide array of visual and numerical data to be used as input for computation, recommendation, and further training. For example, to evaluate fluid overload on chest X-rays or tissue histopathology slides, an AI agent can leverage deep learning models such as convolutional neural networks and vision transformers to analyze images like radiographs and histopathology slides.8,9 Recurrent neural networks or transformer models process sequential data like time-series vital signs. The agent also implements ensemble methods that combine multiple machine learning algorithms to enhance diagnostic accuracy.
Risk Stratification Agent. This assesses severity and predicts potential outcomes. Morbidity and mortality risks are calculated using an established scoring system and individualized based on the history of other agents’ conditional patients. These are presented graphically, with major risk factors highlighted for explainability.
Treatment Recommendation Agent. Using a reinforcement learning framework supplemented by up-to-date clinical guidelines, this system leverages historical data structured with standardized vocabulary to analyze patients with similar clinical features. Training is also conducted on the patient’s physiological data. All recommendations are presented via a dedicated user interface in a readable format, along with recommendations for editable, orderable items, references, and full-text snippets from previous research. Stop rules end computing if confidence in recommendations is too broad or no clear pathway can be computed with certainty, prompting human mitigation.
Resource Management Agent. This agent coordinates hospital resources using constraint programming techniques for optimal resource allocation, uses queueing theory models to predict and manage patient flow, and implements genetic algorithms for complex scheduling problems.10,11
Monitoring and Alert Agent. By tracking patients’ progress and alerting staff to changes, this agent uses anomaly detection algorithms to identify unusual patterns in patient data and implement time-series forecasting models, such as autoregressive integrated moving average and prophet, to predict future patient states. The agent also uses stream-processing techniques for real-time data analysis.12,13
Documentation and Reporting Agent. This agent maintains comprehensive medical records and generates reports. It employs advanced natural language processing techniques for automated report generation, uses advanced LLMs fine-tuned on medical corpora for narrative creation, and implements information-retrieval techniques to efficiently query patient records.
CLINICAL CASE STUDIES
To illustrate the functionality of a multiagent system, this report examines its application for managing sepsis. The data collection and integration agent continuously aggregates patient data from various sources, normalizing and timestamping it for consistent processing. The diagnostic agent analyzes this integrated data in real time, applying sepsis criteria and utilizing a deep learning model trained on a large sepsis dataset to detect subtle patterns.
The risk stratification agent calculates severity scores, such as the Sepsis-related Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Acute Physiology and Chronic Health Evaluation II, upon detecting a possible sepsis case.14 It predicts the likelihood of specific outcomes and estimates the potential trajectory of the patient’s condition for the next 24 to 48 hours. Based on this assessment, the treatment recommendation agent suggests an initial treatment plan, including appropriate antibiotics, fluid resuscitation protocols, and vasopressor recommendations, recommendations when indicated.
Concurrently, the resource management agent checks the availability of necessary resources and prioritizes allocation based on the severity. The monitoring agent tracks the patient’s response to interventions in real time, alerting the care team to any concerning changes or lack of expected improvement. Throughout this process, the documentation agent ensures that all actions, responses, and outcomes are meticulously recorded in a structured format and generates real-time updates for the patient’s electronic health record (EHR) and preparing summary reports for handoffs between care teams.
Administrative Workflow Support
Modern health care operations are resource-intensive, requiring coordination of advanced imaging, procedures, laboratory testing, and professional consultations.15 AI-powered health care administrative workflow systems are revolutionizing how medical facilities coordinate patient care. For patients with chronic cough, these systems seamlessly integrate scheduling, imaging, diagnostics, and follow-up care into a cohesive process that reduces administrative burden while improving patient outcomes. Through an intuitive interface and automated assistance, health care practitioners (HCPs) can track patient progress from initial consultation through diagnosis and treatment.
The process begins when an HCP enters a patient into the system, which triggers an automated CT scan scheduling system. The system considers factors like urgency, facility availability, and patient preferences to suggest optimal appointment times. Once imaging is complete, AI agents analyze the radiology reports, extract key findings, and generate structured summaries that highlight critical information such as “mild bronchial wall thickening with patchy ground-glass opacities” or “findings consistent with chronic bronchitis.”
Based on these findings, the system automatically generates evidence-based recommendations for follow-up care, such as pulmonology consultations or follow-up imaging in 3 months. These recommendations are presented to the ordering clinician, along with suggested appointment slots for specialist consultations. The system then manages the coordination of multiple appointments, ensuring each step in the patient’s care plan is properly sequenced and scheduled.
The entire process is monitored through a comprehensive dashboard that provides real-time updates on patient status, appointment schedules, and clinical recommendations. HCPs can track which patients require immediate attention, view upcoming appointments, and monitor the progress of ongoing care plans.
Multiagent AI Operation Optimization
Hospitals are complex entities that must function at different scales and respond in an agile, timely manner at all hours, deploying staff at various positions.16 A system of AI agents can receive signals from sensors monitoring foot traffic in the emergency department and trauma unit, as well as the availability of operating room staff, equipment, and intensive care unit beds. Smart sensors enable this monitoring through IoT networks. These networks benefit from advances in adaptive and consensus networking algorithms, along with recent advances in bioengineering and biocomputing.17
For example, in the case of imaging for suspected abdominal obstruction, an AI agent tasked with scheduling CTs could time the patient’s arrival based on acuity. Another AI agent could alert staff transporting the patient to the CT appointment, with the next location contingent on a clinical decision to proceed to the operating room. Yet another AI agent could summarize radiology interpretations and alert the surgery and anesthesia teams to a potential case, while others could notify operating room staff of equipment needs or reserve a bed. In this paradigm, AI agents facilitate more precise and timely communication between multiple staff members.
TECHNICAL IMPLEMENTATION
Large Language Models
Each agent uses a different LLM optimized for its specific task. For example, the diagnostic agent uses an LLM pretrained on a large corpus of biomedical literature and fine-tuned on a dataset of confirmed sepsis cases and their presentations.18 It implements few-shot learning techniques to adapt to rare or atypical presentations. The treatment recommendation agent also uses an LLM, employing a retrieval-augmented generation approach to access the latest clinical guidelines during inference. The documentation agent uses another advanced language model, fine-tuned on a large corpus of high-quality medical documentation, implementing controlled text generation techniques and utilizing a separate smaller model for real-time error checking and correction.
Interagent Quality Control
Agents learn from their own experience and the experience of other agents. They are equipped with user-defined rule-based and model-based systems for quality assurance, with clear stopping rules for human involvement and mitigation.
Sophisticated quality control measures bolster the system’s reliability, including ensemble techniques for result comparison, redundancy for critical tasks, and automatic human review for disagreements above a certain threshold. Each agent provides a calibrated confidence score with its output, used to weigh inputs in downstream tasks and trigger additional checks for low-confidence outputs.
A dedicated quality control agent monitors output from all agents, employing both supervised and unsupervised anomaly detection techniques. Feedback loops allow agents to evaluate the quality and utility of information received from other agents. The system implements a multiarmed bandit approach to dynamically adjust the influence of different agents based on their performance and periodically retrains agent models using federated learning techniques.19
Electronic Health Record Integration
Seamless EHR integration is crucial for practical implementation. The system has secure application programming interface access to various EHR platforms, implements OAuth 2.0 for authentication, and use HTTPS with perfect forward secrecy for all communications.20 It works with HL7 FHIR to ensure interoperability and uses SNOMED CT for clinical terminology to ensure semantic interoperability across different EHRs.21,22
The system implements a multilevel approval system for write-backs to EHRs, with different thresholds based on the information’s criticality. It uses digital signatures to ensure the integrity and nonrepudiation of AI-generated entries and implements blockchain technology to create an immutable and distributed ledger of all AI system actions.23
Decision Transparency
To ensure transparency in decision-making processes, the system applies techniques (eg, local interpretable model-agnostic explanations and Shapley additive explanations) to provide insights into agent decision-making processes.24-26 It provides customized visualizations for different stakeholders and allows users to explore alternative decision paths through what-if scenario modeling.27
The system provides calibrated confidence indicators for each recommendation or decision, implementing a novel confidence calibration agent that continuously monitors and adjusts confidence scores based on observed outcomes.
Continuous Learning and Adaptation
The system employs several techniques to remain current with evolving medical knowledge. Federated learning includes information from diverse datasets across multiple institutions without compromising patient privacy.28 A/B testing is used to safely deploy and compare new agent versions in controlled settings, implementing multiarmed bandit algorithms to efficiently explore new models while minimizing potential negative impacts. Human-in-the-loop learning and active learning techniques are used to incorporate feedback from HCPs and efficiently solicit expert input on the most informative data.29
CLINICAL IMPLICATIONS
The implementation of multiagent AI systems in health care has several potential benefits: enhanced diagnostic accuracy, personalized treatment, improved efficiency, continuous monitoring, and resource optimization. A recent review of AI sepsis predictive models exhibited superior results to standard clinical scoring methods like qSOFA.30 In oncology, such systems can result in more tailored treatments, enhancing outcomes.31 The implementation of an ambient dictation system can improve workflow and prevent HCP burnout.32
ETHICAL CONSIDERATIONS AND AI OVERSIGHT
Integrating AI agents into health care raises significant ethical considerations that must be carefully addressed to ensure equitable and effective care delivery. One primary concern involves cultural and linguistic competency, as AI systems may struggle with cultural nuances, idioms, and context-specific communication patterns. This becomes particularly challenging in regions with diverse ethnic populations or immigrant communities, where medical terminology may not have direct translations and cultural beliefs significantly influence health care decisions. AI systems also may inherit and amplify existing biases in health care delivery, whether through HCP bias reflected in training data, patient bias affecting acceptance of AI-assisted care, or demographic underrepresentation during system development.
AI agents present unique opportunities for improving health care access and outcomes through community engagement, though such initiatives require thoughtful implementation. Predictive analytics can identify high-risk individuals within communities who may benefit from preventive care, while analysis of social determinants of health can enable more targeted interventions. However, these capabilities must be balanced with privacy concerns and the risk of surveillance, particularly in communities that distrust health care institutions. The potential for AI to bridge health care gaps must be weighed against the need to maintain cultural sensitivity and community trust.
The governance and oversight of health care AI systems requires a multistakeholder approach with clear lines of responsibility and accountability. This includes involvement from government health care agencies, professional medical associations, ethics boards, and independent auditors, all working together to establish and enforce standards while monitoring system performance and addressing potential biases. Health care organizations must maintain transparent policies about AI use, implement regular monitoring and evaluation protocols, and establish precise mechanisms for patient feedback and grievance resolution. Ongoing assessment and adjustment of these systems, informed by community feedback and outcomes data, will be crucial for their ethical implementation, ensuring that AI agents complement, rather than replace, human judgment and cultural sensitivity.
FUTURE DIRECTIONS
Despite the potential benefits, implementing multiagent AI systems in health care faces significant challenges that require careful consideration. Beyond the fundamental issues such as data quality and bias mitigation, health care organizations struggle with fragmented systems, inconsistent data formats, and varying quality. Technical infrastructure requirements are substantial, particularly in rural or underserved areas that lack robust networks and cybersecurity. HCPs already face significant cognitive load and time pressures, making integrating AI agents into existing workflows particularly challenging. There is also the critical issue of transparency and interpretability, as health care decisions require clear reasoning and accountability that many black-box AI systems struggle to provide.
The legal landscape introduces another layer of complexity, particularly regarding liability, consent, and privacy questions. When AI agents contribute to medical decisions, establishing clear lines of responsibility becomes crucial. There are also serious concerns about algorithmic fairness and the potential for AI systems to perpetuate or amplify existing inequities. The cost of implementation remains a significant barrier, requiring substantial investment in technology, training, and ongoing maintenance while ensuring resources are not diverted from direct patient care. Moreover, HCPs may resist adoption due to concerns about job security, loss of autonomy, or skepticism about AI capabilities while paradoxically facing risks of overreliance on AI systems that could lead to the degradation of human clinical skills.
Addressing these challenges requires a multifaceted approach that combines technical solutions with organizational and policy changes. Health care organizations must implement rigorous data validation processes and interoperability standards while developing hybrid models that balance sophisticated AI capabilities with interpretable techniques. Extensive research and iterative design processes, with direct input from HCPs, are essential for successful integration. Establishing independent ethics boards to oversee system development and deployment, conducting multicenter randomized controlled trials, and creating clear regulatory frameworks will ensure safe and effective implementation. Success will ultimately depend on ongoing collaboration between technology developers, HCPs, policymakers, and patients, maintaining a steady focus on improving patient care and outcomes while carefully navigating the complex challenges of AI integration in health care.33-35
As multiagent AI systems in health care evolve, several exciting directions emerge. These include the integration of IoT and wearable devices, the development of more sophisticated natural language interfaces, and applying these systems to predictive maintenance of medical equipment.
CONCLUSIONS
The advent of multiagent AI systems in health care represents a paradigm shift in the approach to patient care, clinical decision making, and health care management. While these systems offer immense potential to transform health care delivery, their development and implementation must be guided by rigorous scientific validation, ethical considerations, and a patient-centered approach. The ultimate goal remains clear: harnessing the power of AI to improve patient outcomes, enhance the efficiency of health care delivery, and ultimately advance the health and well-being of patients.
Artificial intelligence (AI) is rapidly evolving, with large language models (LLMs) marking a significant milestone in processing and generating human-like responses to natural language prompts. However, this advancement only signals the beginning of a more profound transformation in AI capabilities. The development of AI agents represents a new paradigm at the forefront of this evolution.
BACKGROUND
AI agents represent a leap forward from traditional LLM applications. While definitions may vary slightly among technology developers, the core concept remains: these agents are autonomous software entities designed to interact with their environment, make independent decisions, and execute tasks based on predefined goals.1-3 What sets AI agents apart is their combination of sophisticated components within structured architectures. At their core, AI agents incorporate an LLM for response generation, which is augmented by a suite of tools to optimize workflow and complete tasks, memory capabilities for personalized interactions, and autonomous reasoning. This combination allows AI agents to plan, create subtasks, gather information, and learn iteratively from their own experiences or other AI agents.
The true potential of this technology becomes apparent when multiple AI agents collaborate within multiagent AI systems. This concept introduces a new level of flexibility and capability in tackling complex tasks. Autogen, CrewAI, and LangChain offer various agent network configurations, including hierarchical, sequential, conditional, or even parallel task execution.4-6 This adaptability opens up a world of possibilities across various industries, but perhaps nowhere is the potential impact more exciting and profound than in health care.
AI agents in health care present an opportunity to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. This review examines 3 scenarios that illustrate the impact of AI agents in health care: a hypothetical sepsis management system, chronic disease management, and hospital patient flow optimization. This article will provide a detailed look at the technical implementation challenges, including the integration with existing health care IT systems, data privacy considerations, and the crucial role of explainable AI in maintaining trust and transparency.
It is challenging to implement AI agents in health care. Concerns include ensuring data quality and mitigating bias, seamlessly integrating these systems into existing clinical workflows, and navigating the complex ethical considerations that arise when deploying autonomous systems in health care. The integration with Internet of Things (IoT) devices for real-time patient data monitoring and the development of more sophisticated natural language interfaces to enhance future human-AI collaboration.
The adoption of AI agents in health care is only beginning, and it promises to be transformative. As AI continues to evolve, a comprehensive understanding of its applications, limitations, and ethical considerations is essential. This report provides a comprehensive overview of the current state, potential applications, and future directions of AI agents in health care, offering insights valuable to researchers, clinicians, and policymakers.
MultiAgent AI architecture
Sepsis Management
Despite advancements in broad-spectrum antibiotics, imaging, and life support systems, mortality rates associated with sepsis remain high. The complexity of optimizing care in clinical settings has hindered progress in managing sepsis. Previous attempts to develop predictive sepsis models have proven challenging.7 This report proposes a multiagent AI system designed to enhance comprehensive patient monitoring and care through coordinated AI-driven interventions.
Data Collection and Integration Agent. Powered by a controlled vocabulary to specify all data, the primary function for the data collection and integration agent is to clean, transform, and organize patient data from structured and unstructured sources. This agent prepares succinct summaries of consultant notes and formats data for human and machine consumption. All numerical data are presented graphically, including relevant historical data trends. The agent also digitally captures all orders in a structured format using a specified controlled vocabulary. This structured data feed supports the output of other agents, including documentation, treatment planning, and risk stratification, while also supplying the data structures for future training.
Diagnostic Agent. Critical illness is characterized by multiple abnormalities across a wide array of tests, ranging from plain chest X-ray, computed tomography (CT), blood cell composition, plasma chemistry, and microscopic evaluation of specimens. Additionally, life support parameters provide insights into disease severity and can inform management recommendations. These data offer a wide array of visual and numerical data to be used as input for computation, recommendation, and further training. For example, to evaluate fluid overload on chest X-rays or tissue histopathology slides, an AI agent can leverage deep learning models such as convolutional neural networks and vision transformers to analyze images like radiographs and histopathology slides.8,9 Recurrent neural networks or transformer models process sequential data like time-series vital signs. The agent also implements ensemble methods that combine multiple machine learning algorithms to enhance diagnostic accuracy.
Risk Stratification Agent. This assesses severity and predicts potential outcomes. Morbidity and mortality risks are calculated using an established scoring system and individualized based on the history of other agents’ conditional patients. These are presented graphically, with major risk factors highlighted for explainability.
Treatment Recommendation Agent. Using a reinforcement learning framework supplemented by up-to-date clinical guidelines, this system leverages historical data structured with standardized vocabulary to analyze patients with similar clinical features. Training is also conducted on the patient’s physiological data. All recommendations are presented via a dedicated user interface in a readable format, along with recommendations for editable, orderable items, references, and full-text snippets from previous research. Stop rules end computing if confidence in recommendations is too broad or no clear pathway can be computed with certainty, prompting human mitigation.
Resource Management Agent. This agent coordinates hospital resources using constraint programming techniques for optimal resource allocation, uses queueing theory models to predict and manage patient flow, and implements genetic algorithms for complex scheduling problems.10,11
Monitoring and Alert Agent. By tracking patients’ progress and alerting staff to changes, this agent uses anomaly detection algorithms to identify unusual patterns in patient data and implement time-series forecasting models, such as autoregressive integrated moving average and prophet, to predict future patient states. The agent also uses stream-processing techniques for real-time data analysis.12,13
Documentation and Reporting Agent. This agent maintains comprehensive medical records and generates reports. It employs advanced natural language processing techniques for automated report generation, uses advanced LLMs fine-tuned on medical corpora for narrative creation, and implements information-retrieval techniques to efficiently query patient records.
CLINICAL CASE STUDIES
To illustrate the functionality of a multiagent system, this report examines its application for managing sepsis. The data collection and integration agent continuously aggregates patient data from various sources, normalizing and timestamping it for consistent processing. The diagnostic agent analyzes this integrated data in real time, applying sepsis criteria and utilizing a deep learning model trained on a large sepsis dataset to detect subtle patterns.
The risk stratification agent calculates severity scores, such as the Sepsis-related Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Acute Physiology and Chronic Health Evaluation II, upon detecting a possible sepsis case.14 It predicts the likelihood of specific outcomes and estimates the potential trajectory of the patient’s condition for the next 24 to 48 hours. Based on this assessment, the treatment recommendation agent suggests an initial treatment plan, including appropriate antibiotics, fluid resuscitation protocols, and vasopressor recommendations, recommendations when indicated.
Concurrently, the resource management agent checks the availability of necessary resources and prioritizes allocation based on the severity. The monitoring agent tracks the patient’s response to interventions in real time, alerting the care team to any concerning changes or lack of expected improvement. Throughout this process, the documentation agent ensures that all actions, responses, and outcomes are meticulously recorded in a structured format and generates real-time updates for the patient’s electronic health record (EHR) and preparing summary reports for handoffs between care teams.
Administrative Workflow Support
Modern health care operations are resource-intensive, requiring coordination of advanced imaging, procedures, laboratory testing, and professional consultations.15 AI-powered health care administrative workflow systems are revolutionizing how medical facilities coordinate patient care. For patients with chronic cough, these systems seamlessly integrate scheduling, imaging, diagnostics, and follow-up care into a cohesive process that reduces administrative burden while improving patient outcomes. Through an intuitive interface and automated assistance, health care practitioners (HCPs) can track patient progress from initial consultation through diagnosis and treatment.
The process begins when an HCP enters a patient into the system, which triggers an automated CT scan scheduling system. The system considers factors like urgency, facility availability, and patient preferences to suggest optimal appointment times. Once imaging is complete, AI agents analyze the radiology reports, extract key findings, and generate structured summaries that highlight critical information such as “mild bronchial wall thickening with patchy ground-glass opacities” or “findings consistent with chronic bronchitis.”
Based on these findings, the system automatically generates evidence-based recommendations for follow-up care, such as pulmonology consultations or follow-up imaging in 3 months. These recommendations are presented to the ordering clinician, along with suggested appointment slots for specialist consultations. The system then manages the coordination of multiple appointments, ensuring each step in the patient’s care plan is properly sequenced and scheduled.
The entire process is monitored through a comprehensive dashboard that provides real-time updates on patient status, appointment schedules, and clinical recommendations. HCPs can track which patients require immediate attention, view upcoming appointments, and monitor the progress of ongoing care plans.
Multiagent AI Operation Optimization
Hospitals are complex entities that must function at different scales and respond in an agile, timely manner at all hours, deploying staff at various positions.16 A system of AI agents can receive signals from sensors monitoring foot traffic in the emergency department and trauma unit, as well as the availability of operating room staff, equipment, and intensive care unit beds. Smart sensors enable this monitoring through IoT networks. These networks benefit from advances in adaptive and consensus networking algorithms, along with recent advances in bioengineering and biocomputing.17
For example, in the case of imaging for suspected abdominal obstruction, an AI agent tasked with scheduling CTs could time the patient’s arrival based on acuity. Another AI agent could alert staff transporting the patient to the CT appointment, with the next location contingent on a clinical decision to proceed to the operating room. Yet another AI agent could summarize radiology interpretations and alert the surgery and anesthesia teams to a potential case, while others could notify operating room staff of equipment needs or reserve a bed. In this paradigm, AI agents facilitate more precise and timely communication between multiple staff members.
TECHNICAL IMPLEMENTATION
Large Language Models
Each agent uses a different LLM optimized for its specific task. For example, the diagnostic agent uses an LLM pretrained on a large corpus of biomedical literature and fine-tuned on a dataset of confirmed sepsis cases and their presentations.18 It implements few-shot learning techniques to adapt to rare or atypical presentations. The treatment recommendation agent also uses an LLM, employing a retrieval-augmented generation approach to access the latest clinical guidelines during inference. The documentation agent uses another advanced language model, fine-tuned on a large corpus of high-quality medical documentation, implementing controlled text generation techniques and utilizing a separate smaller model for real-time error checking and correction.
Interagent Quality Control
Agents learn from their own experience and the experience of other agents. They are equipped with user-defined rule-based and model-based systems for quality assurance, with clear stopping rules for human involvement and mitigation.
Sophisticated quality control measures bolster the system’s reliability, including ensemble techniques for result comparison, redundancy for critical tasks, and automatic human review for disagreements above a certain threshold. Each agent provides a calibrated confidence score with its output, used to weigh inputs in downstream tasks and trigger additional checks for low-confidence outputs.
A dedicated quality control agent monitors output from all agents, employing both supervised and unsupervised anomaly detection techniques. Feedback loops allow agents to evaluate the quality and utility of information received from other agents. The system implements a multiarmed bandit approach to dynamically adjust the influence of different agents based on their performance and periodically retrains agent models using federated learning techniques.19
Electronic Health Record Integration
Seamless EHR integration is crucial for practical implementation. The system has secure application programming interface access to various EHR platforms, implements OAuth 2.0 for authentication, and use HTTPS with perfect forward secrecy for all communications.20 It works with HL7 FHIR to ensure interoperability and uses SNOMED CT for clinical terminology to ensure semantic interoperability across different EHRs.21,22
The system implements a multilevel approval system for write-backs to EHRs, with different thresholds based on the information’s criticality. It uses digital signatures to ensure the integrity and nonrepudiation of AI-generated entries and implements blockchain technology to create an immutable and distributed ledger of all AI system actions.23
Decision Transparency
To ensure transparency in decision-making processes, the system applies techniques (eg, local interpretable model-agnostic explanations and Shapley additive explanations) to provide insights into agent decision-making processes.24-26 It provides customized visualizations for different stakeholders and allows users to explore alternative decision paths through what-if scenario modeling.27
The system provides calibrated confidence indicators for each recommendation or decision, implementing a novel confidence calibration agent that continuously monitors and adjusts confidence scores based on observed outcomes.
Continuous Learning and Adaptation
The system employs several techniques to remain current with evolving medical knowledge. Federated learning includes information from diverse datasets across multiple institutions without compromising patient privacy.28 A/B testing is used to safely deploy and compare new agent versions in controlled settings, implementing multiarmed bandit algorithms to efficiently explore new models while minimizing potential negative impacts. Human-in-the-loop learning and active learning techniques are used to incorporate feedback from HCPs and efficiently solicit expert input on the most informative data.29
CLINICAL IMPLICATIONS
The implementation of multiagent AI systems in health care has several potential benefits: enhanced diagnostic accuracy, personalized treatment, improved efficiency, continuous monitoring, and resource optimization. A recent review of AI sepsis predictive models exhibited superior results to standard clinical scoring methods like qSOFA.30 In oncology, such systems can result in more tailored treatments, enhancing outcomes.31 The implementation of an ambient dictation system can improve workflow and prevent HCP burnout.32
ETHICAL CONSIDERATIONS AND AI OVERSIGHT
Integrating AI agents into health care raises significant ethical considerations that must be carefully addressed to ensure equitable and effective care delivery. One primary concern involves cultural and linguistic competency, as AI systems may struggle with cultural nuances, idioms, and context-specific communication patterns. This becomes particularly challenging in regions with diverse ethnic populations or immigrant communities, where medical terminology may not have direct translations and cultural beliefs significantly influence health care decisions. AI systems also may inherit and amplify existing biases in health care delivery, whether through HCP bias reflected in training data, patient bias affecting acceptance of AI-assisted care, or demographic underrepresentation during system development.
AI agents present unique opportunities for improving health care access and outcomes through community engagement, though such initiatives require thoughtful implementation. Predictive analytics can identify high-risk individuals within communities who may benefit from preventive care, while analysis of social determinants of health can enable more targeted interventions. However, these capabilities must be balanced with privacy concerns and the risk of surveillance, particularly in communities that distrust health care institutions. The potential for AI to bridge health care gaps must be weighed against the need to maintain cultural sensitivity and community trust.
The governance and oversight of health care AI systems requires a multistakeholder approach with clear lines of responsibility and accountability. This includes involvement from government health care agencies, professional medical associations, ethics boards, and independent auditors, all working together to establish and enforce standards while monitoring system performance and addressing potential biases. Health care organizations must maintain transparent policies about AI use, implement regular monitoring and evaluation protocols, and establish precise mechanisms for patient feedback and grievance resolution. Ongoing assessment and adjustment of these systems, informed by community feedback and outcomes data, will be crucial for their ethical implementation, ensuring that AI agents complement, rather than replace, human judgment and cultural sensitivity.
FUTURE DIRECTIONS
Despite the potential benefits, implementing multiagent AI systems in health care faces significant challenges that require careful consideration. Beyond the fundamental issues such as data quality and bias mitigation, health care organizations struggle with fragmented systems, inconsistent data formats, and varying quality. Technical infrastructure requirements are substantial, particularly in rural or underserved areas that lack robust networks and cybersecurity. HCPs already face significant cognitive load and time pressures, making integrating AI agents into existing workflows particularly challenging. There is also the critical issue of transparency and interpretability, as health care decisions require clear reasoning and accountability that many black-box AI systems struggle to provide.
The legal landscape introduces another layer of complexity, particularly regarding liability, consent, and privacy questions. When AI agents contribute to medical decisions, establishing clear lines of responsibility becomes crucial. There are also serious concerns about algorithmic fairness and the potential for AI systems to perpetuate or amplify existing inequities. The cost of implementation remains a significant barrier, requiring substantial investment in technology, training, and ongoing maintenance while ensuring resources are not diverted from direct patient care. Moreover, HCPs may resist adoption due to concerns about job security, loss of autonomy, or skepticism about AI capabilities while paradoxically facing risks of overreliance on AI systems that could lead to the degradation of human clinical skills.
Addressing these challenges requires a multifaceted approach that combines technical solutions with organizational and policy changes. Health care organizations must implement rigorous data validation processes and interoperability standards while developing hybrid models that balance sophisticated AI capabilities with interpretable techniques. Extensive research and iterative design processes, with direct input from HCPs, are essential for successful integration. Establishing independent ethics boards to oversee system development and deployment, conducting multicenter randomized controlled trials, and creating clear regulatory frameworks will ensure safe and effective implementation. Success will ultimately depend on ongoing collaboration between technology developers, HCPs, policymakers, and patients, maintaining a steady focus on improving patient care and outcomes while carefully navigating the complex challenges of AI integration in health care.33-35
As multiagent AI systems in health care evolve, several exciting directions emerge. These include the integration of IoT and wearable devices, the development of more sophisticated natural language interfaces, and applying these systems to predictive maintenance of medical equipment.
CONCLUSIONS
The advent of multiagent AI systems in health care represents a paradigm shift in the approach to patient care, clinical decision making, and health care management. While these systems offer immense potential to transform health care delivery, their development and implementation must be guided by rigorous scientific validation, ethical considerations, and a patient-centered approach. The ultimate goal remains clear: harnessing the power of AI to improve patient outcomes, enhance the efficiency of health care delivery, and ultimately advance the health and well-being of patients.
Amazon Web Services, Inc. What are AI agents? Agents in artificial intelligence explained. Accessed April 7, 2025. https://aws.amazon.com/what-is/ai-agents/
Gutowska A. What are AI agents? IBM. Accessed April 7, 2025. https://www.ibm.com/think/topics/ai-agents
Agent AI. Microsoft Research. Accessed April 7, 2025. https://www.microsoft.com/en-us/research/project/agent-ai
Microsoft. AutoGen. Accessed April 7, 2025. https://microsoft.github.io/autogen/
Crew AI. The Leading Multi-Agent Platform. CrewAI. Accessed April 7, 2025. https://www.crewai.com/
LangChain. Accessed April 7, 2025. https://www.langchain.com/
Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065-1070. doi:10.1001/jamainternmed.2021.2626
Willemink MJ, Roth HR, Sandfort V. Toward foundational deep learning models for medical imaging in the new era of transformer networks. Radiol Artif Intell. 2022;4(6):e210284. doi:10.1148/ryai.210284
Waqas A, Bui MM, Glassy EF, et al. Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models. Lab Invest. 2023;103(11):100255. doi:10.1016/j.labinv.2023.100255
Moreno-Carrillo A, Arenas LMÁ, Fonseca JA, Caicedo CA, Tovar SV, Muñoz-Velandia OM. Application of queuing theory to optimize the triage process in a tertiary emergency care (“ER”) department. J Emerg Trauma Shock. 2019;12(4):268-273. doi:10.4103/JETS.JETS_42_19
Pongcharoen P, Hicks C, Braiden PM, Stewardson DJ. Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products. Int J Prod Econ. 2002;78(3):311-322. doi:10.1016/S0925-5273(02)00104-4
Sardar I, Akbar MA, Leiva V, Alsanad A, Mishra P. Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries. Stoch Environ Res Risk Assess. 2023;37(1):345-359. doi:10.1007/s00477-022-02307-x
Samosir J, Indrawan-Santiago M, Haghighi PD. An evaluation of data stream processing systems for data driven applications. Procedia Comput Sci. 2016;80:439-449. doi:10.1016/j.procs.2016.05.322
Asmarawati TP, Suryantoro SD, Rosyid AN, et al. Predictive value of sequential organ failure assessment, quick sequential organ failure assessment, acute physiology and chronic health evaluation II, and new early warning signs scores estimate mortality of COVID-19 patients requiring intensive care unit. Indian J Crit Care Med. 2022;26(4):466-473. doi:10.5005/jp-journals-10071-24170
Khan S, Vandermorris A, Shepherd J, et al. Embracing uncertainty, managing complexity: applying complexity thinking principles to transformation efforts in healthcare systems. BMC Health Serv Res. 2018;18(1):192. doi:10.1186/s12913-018-2994-0
Plsek PE, Greenhalgh T. The challenge of complexity in health care. BMJ. 2001;323(7313):625-628. doi:10.1136/bmj.323.7313.625
Kouchaki S, Ding X, Sanei S. AI- and IoT-enabled solutions for healthcare. Sensors. 2024;24(8):2607. doi:10.3390/s24082607
Saab K, Tu T, Weng WH, et al. Capabilities of Gemini Models in Medicine. arXiv. doi:10.48550/arXiv.2404.18416
Villar SS, Bowden J, Wason J. Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges. Stat Sci. 2015;30(2):199-215. doi:10.1214/14-STS504
Auth0. What is OAuth 2.0. Accessed April 7, 2025. https://auth0.com/intro-to-iam/what-is-oauth-2
HL7. Welcome to FHIR. Updated March 26, 2025. Accessed April 7, 2025. https://www.hl7.org/fhir/
SNOMED International. Accessed April 7, 2025. https://www.snomed.org
Hasselgren A, Kralevska K, Gligoroski D, Pedersen SA, Faxvaag A. Blockchain in healthcare and health sciences—a scoping review. Int J Med Inf. 2020;134:104040. doi:10.1016/j.ijmedinf.2019.104040
Ribeiro MT, Singh S, Guestrin C. “Why Should I Trust You?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016:1135-1144. doi:10.1145/2939672.2939778
Ekanayake IU, Meddage DPP, Rathnayake U. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Stud Constr Mater. 2022;16:e01059. doi:10.1016/j.cscm.2022.e01059
Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep. 2023;13(1):8984. doi:10.1038/s41598-023-35795-0
Otto E, Culakova E, Meng S, et al. Overview of sankey flow diagrams: focusing on symptom trajectories in older adults with advanced cancer. J Geriatr Oncol. 2022;13(5):742-746. doi:10.1016/j.jgo.2021.12.017
Fereidooni H, Marchal S, Miettinen M, et al. SAFELearn: secure aggregation for private federated learning. In: 2021 IEEE security and privacy workshops (SPW). 2021:56-62. doi:10.1109/SPW53761.2021.00017
Linton DL, Pangle WM, Wyatt KH, Powell KN, Sherwood RE. Identifying key features of effective active learning: the effects of writing and peer discussion. Life Sci Educ. 2014;13(3):469-477. doi:10.1187/cbe.13-12-0242
Yang HS. Machine learning for sepsis prediction: prospects and challenges. Clin Chem. 2024;70(3):465-467. doi:10.1093/clinchem/hvae006
Liao J, Li X, Gan Y, et al. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol. 2023;12. doi:10.3389/fonc.2022.998222
Tierney AA, Gayre G, Hoberman B, et al. Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catal. 2024;5(3):CAT.23.0404. doi:10.1056/CAT.23.0404
Borkowski AA, Jakey CE, Thomas LB, Viswanadhan N, Mastorides SM. Establishing a hospital artificial intelligence committee to improve patient care. Fed Pract. 2022;39(8):334-336. doi:10.12788/fp.0299
Isaacks DB, Borkowski AA. Implementing trustworthy AI in VA high reliability health care organizations. Fed Pract.2024;41(2):40-43. doi:10.12788/fp.0454
Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P. Randomized controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health. 2024;6(5):e367-e373. doi:10.1016/S2589-7500(24)00047-5
Amazon Web Services, Inc. What are AI agents? Agents in artificial intelligence explained. Accessed April 7, 2025. https://aws.amazon.com/what-is/ai-agents/
Gutowska A. What are AI agents? IBM. Accessed April 7, 2025. https://www.ibm.com/think/topics/ai-agents
Agent AI. Microsoft Research. Accessed April 7, 2025. https://www.microsoft.com/en-us/research/project/agent-ai
Microsoft. AutoGen. Accessed April 7, 2025. https://microsoft.github.io/autogen/
Crew AI. The Leading Multi-Agent Platform. CrewAI. Accessed April 7, 2025. https://www.crewai.com/
LangChain. Accessed April 7, 2025. https://www.langchain.com/
Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065-1070. doi:10.1001/jamainternmed.2021.2626
Willemink MJ, Roth HR, Sandfort V. Toward foundational deep learning models for medical imaging in the new era of transformer networks. Radiol Artif Intell. 2022;4(6):e210284. doi:10.1148/ryai.210284
Waqas A, Bui MM, Glassy EF, et al. Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models. Lab Invest. 2023;103(11):100255. doi:10.1016/j.labinv.2023.100255
Moreno-Carrillo A, Arenas LMÁ, Fonseca JA, Caicedo CA, Tovar SV, Muñoz-Velandia OM. Application of queuing theory to optimize the triage process in a tertiary emergency care (“ER”) department. J Emerg Trauma Shock. 2019;12(4):268-273. doi:10.4103/JETS.JETS_42_19
Pongcharoen P, Hicks C, Braiden PM, Stewardson DJ. Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products. Int J Prod Econ. 2002;78(3):311-322. doi:10.1016/S0925-5273(02)00104-4
Sardar I, Akbar MA, Leiva V, Alsanad A, Mishra P. Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries. Stoch Environ Res Risk Assess. 2023;37(1):345-359. doi:10.1007/s00477-022-02307-x
Samosir J, Indrawan-Santiago M, Haghighi PD. An evaluation of data stream processing systems for data driven applications. Procedia Comput Sci. 2016;80:439-449. doi:10.1016/j.procs.2016.05.322
Asmarawati TP, Suryantoro SD, Rosyid AN, et al. Predictive value of sequential organ failure assessment, quick sequential organ failure assessment, acute physiology and chronic health evaluation II, and new early warning signs scores estimate mortality of COVID-19 patients requiring intensive care unit. Indian J Crit Care Med. 2022;26(4):466-473. doi:10.5005/jp-journals-10071-24170
Khan S, Vandermorris A, Shepherd J, et al. Embracing uncertainty, managing complexity: applying complexity thinking principles to transformation efforts in healthcare systems. BMC Health Serv Res. 2018;18(1):192. doi:10.1186/s12913-018-2994-0
Plsek PE, Greenhalgh T. The challenge of complexity in health care. BMJ. 2001;323(7313):625-628. doi:10.1136/bmj.323.7313.625
Kouchaki S, Ding X, Sanei S. AI- and IoT-enabled solutions for healthcare. Sensors. 2024;24(8):2607. doi:10.3390/s24082607
Saab K, Tu T, Weng WH, et al. Capabilities of Gemini Models in Medicine. arXiv. doi:10.48550/arXiv.2404.18416
Villar SS, Bowden J, Wason J. Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges. Stat Sci. 2015;30(2):199-215. doi:10.1214/14-STS504
Auth0. What is OAuth 2.0. Accessed April 7, 2025. https://auth0.com/intro-to-iam/what-is-oauth-2
HL7. Welcome to FHIR. Updated March 26, 2025. Accessed April 7, 2025. https://www.hl7.org/fhir/
SNOMED International. Accessed April 7, 2025. https://www.snomed.org
Hasselgren A, Kralevska K, Gligoroski D, Pedersen SA, Faxvaag A. Blockchain in healthcare and health sciences—a scoping review. Int J Med Inf. 2020;134:104040. doi:10.1016/j.ijmedinf.2019.104040
Ribeiro MT, Singh S, Guestrin C. “Why Should I Trust You?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016:1135-1144. doi:10.1145/2939672.2939778
Ekanayake IU, Meddage DPP, Rathnayake U. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Stud Constr Mater. 2022;16:e01059. doi:10.1016/j.cscm.2022.e01059
Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep. 2023;13(1):8984. doi:10.1038/s41598-023-35795-0
Otto E, Culakova E, Meng S, et al. Overview of sankey flow diagrams: focusing on symptom trajectories in older adults with advanced cancer. J Geriatr Oncol. 2022;13(5):742-746. doi:10.1016/j.jgo.2021.12.017
Fereidooni H, Marchal S, Miettinen M, et al. SAFELearn: secure aggregation for private federated learning. In: 2021 IEEE security and privacy workshops (SPW). 2021:56-62. doi:10.1109/SPW53761.2021.00017
Linton DL, Pangle WM, Wyatt KH, Powell KN, Sherwood RE. Identifying key features of effective active learning: the effects of writing and peer discussion. Life Sci Educ. 2014;13(3):469-477. doi:10.1187/cbe.13-12-0242
Yang HS. Machine learning for sepsis prediction: prospects and challenges. Clin Chem. 2024;70(3):465-467. doi:10.1093/clinchem/hvae006
Liao J, Li X, Gan Y, et al. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol. 2023;12. doi:10.3389/fonc.2022.998222
Tierney AA, Gayre G, Hoberman B, et al. Ambient artificial intelligence scribes to alleviate the burden of clinical documentation. NEJM Catal. 2024;5(3):CAT.23.0404. doi:10.1056/CAT.23.0404
Borkowski AA, Jakey CE, Thomas LB, Viswanadhan N, Mastorides SM. Establishing a hospital artificial intelligence committee to improve patient care. Fed Pract. 2022;39(8):334-336. doi:10.12788/fp.0299
Isaacks DB, Borkowski AA. Implementing trustworthy AI in VA high reliability health care organizations. Fed Pract.2024;41(2):40-43. doi:10.12788/fp.0454
Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P. Randomized controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health. 2024;6(5):e367-e373. doi:10.1016/S2589-7500(24)00047-5