Group calls for standardized data collection practices across cancer centers

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Group calls for standardized data collection practices across cancer centers

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Researchers have identified significant variations in how cancer centers gather data, particularly that pertaining to racial and ethnic minorities.

Although racial and ethnic categories were similar across the centers, those categories were defined differently.

And the centers’ definitions of “catchment area,” the geographic region they expect to influence with their programs, differed widely.

This research, published in Cancer, was part of a national effort to recruit more racial/ethnic minorities into clinical trials and, ultimately, reduce the disproportional incidence of many cancers among those populations.

Five National Cancer Institute-designated comprehensive cancer centers participated in the endeavor, known as EMPaCT—Enhancing Minority Participation in Clinical Trials. They were:

  • University of Minnesota, Minneapolis, which represents the Midwest and targets the accrual of Native Americans and African Americans
  • University of Alabama, Birmingham, representing the Southeast, targeting African Americans
  • Johns Hopkins University, representing the East, targeting African Americans
  • University of Texas MD Anderson, Houston, representing the Southwest, targeting Latinos
  • University of California, Davis, representing the West, targeting Asian Americans.

Ernest T. Hawk, MD, of the MD Anderson Cancer Center, and his colleagues reviewed the collection and reporting of patient data and other practices by these 5 centers.

This revealed significant variation in the centers’ methods of data collection. For example, patients’ insurance status was routinely documented at 2 centers, collected for non-research patients only at a third center, collected for billing of researcher enrollees at a fourth center, and not documented at all at a fifth center.

There were differences in data collection according to race/ethnicity as well. Racial/ethnic categories were generally similar across the centers—white, black/African American, Asian, Native American, Hispanic/Latino, and “other/unknown.”

However, the means of race/ethnicity data collection differed. Each center collected self-reported data on race/ethnicity, but 2 centers included data from staff observations.

Two centers compared the proportions of racial/ethnic groups enrolled in trials with those of their catchment area(s). But the others did not.

The centers also differed in how they defined their patient catchment area, in terms of their cancer patient-vs-general-population specificity, levels of specificity, and geographic coverage.

That merits notice, according to the researchers, because National Cancer Institute cancer centers are required to accrue women and minorities to clinical trials in rough proportion to the cancer patient population of the center’s primary catchment area.

Given these findings, the researchers recommended better standardization of data definition, collection, and reporting as an essential first step toward expanding minority participation in clinical trials.

The team also advised that cancer centers collect socioeconomic data, including a patient’s income and education levels, given past evidence of the strong link between socioeconomic status and cancer outcomes.

Finally, the group recommended collecting patient zip codes and insurance status to allow researchers to assess differences in access to clinical trials that may be related to geography and the availability of health insurance coverage.

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Doctor evaluating a patient

Credit: CDC

Researchers have identified significant variations in how cancer centers gather data, particularly that pertaining to racial and ethnic minorities.

Although racial and ethnic categories were similar across the centers, those categories were defined differently.

And the centers’ definitions of “catchment area,” the geographic region they expect to influence with their programs, differed widely.

This research, published in Cancer, was part of a national effort to recruit more racial/ethnic minorities into clinical trials and, ultimately, reduce the disproportional incidence of many cancers among those populations.

Five National Cancer Institute-designated comprehensive cancer centers participated in the endeavor, known as EMPaCT—Enhancing Minority Participation in Clinical Trials. They were:

  • University of Minnesota, Minneapolis, which represents the Midwest and targets the accrual of Native Americans and African Americans
  • University of Alabama, Birmingham, representing the Southeast, targeting African Americans
  • Johns Hopkins University, representing the East, targeting African Americans
  • University of Texas MD Anderson, Houston, representing the Southwest, targeting Latinos
  • University of California, Davis, representing the West, targeting Asian Americans.

Ernest T. Hawk, MD, of the MD Anderson Cancer Center, and his colleagues reviewed the collection and reporting of patient data and other practices by these 5 centers.

This revealed significant variation in the centers’ methods of data collection. For example, patients’ insurance status was routinely documented at 2 centers, collected for non-research patients only at a third center, collected for billing of researcher enrollees at a fourth center, and not documented at all at a fifth center.

There were differences in data collection according to race/ethnicity as well. Racial/ethnic categories were generally similar across the centers—white, black/African American, Asian, Native American, Hispanic/Latino, and “other/unknown.”

However, the means of race/ethnicity data collection differed. Each center collected self-reported data on race/ethnicity, but 2 centers included data from staff observations.

Two centers compared the proportions of racial/ethnic groups enrolled in trials with those of their catchment area(s). But the others did not.

The centers also differed in how they defined their patient catchment area, in terms of their cancer patient-vs-general-population specificity, levels of specificity, and geographic coverage.

That merits notice, according to the researchers, because National Cancer Institute cancer centers are required to accrue women and minorities to clinical trials in rough proportion to the cancer patient population of the center’s primary catchment area.

Given these findings, the researchers recommended better standardization of data definition, collection, and reporting as an essential first step toward expanding minority participation in clinical trials.

The team also advised that cancer centers collect socioeconomic data, including a patient’s income and education levels, given past evidence of the strong link between socioeconomic status and cancer outcomes.

Finally, the group recommended collecting patient zip codes and insurance status to allow researchers to assess differences in access to clinical trials that may be related to geography and the availability of health insurance coverage.

Doctor evaluating a patient

Credit: CDC

Researchers have identified significant variations in how cancer centers gather data, particularly that pertaining to racial and ethnic minorities.

Although racial and ethnic categories were similar across the centers, those categories were defined differently.

And the centers’ definitions of “catchment area,” the geographic region they expect to influence with their programs, differed widely.

This research, published in Cancer, was part of a national effort to recruit more racial/ethnic minorities into clinical trials and, ultimately, reduce the disproportional incidence of many cancers among those populations.

Five National Cancer Institute-designated comprehensive cancer centers participated in the endeavor, known as EMPaCT—Enhancing Minority Participation in Clinical Trials. They were:

  • University of Minnesota, Minneapolis, which represents the Midwest and targets the accrual of Native Americans and African Americans
  • University of Alabama, Birmingham, representing the Southeast, targeting African Americans
  • Johns Hopkins University, representing the East, targeting African Americans
  • University of Texas MD Anderson, Houston, representing the Southwest, targeting Latinos
  • University of California, Davis, representing the West, targeting Asian Americans.

Ernest T. Hawk, MD, of the MD Anderson Cancer Center, and his colleagues reviewed the collection and reporting of patient data and other practices by these 5 centers.

This revealed significant variation in the centers’ methods of data collection. For example, patients’ insurance status was routinely documented at 2 centers, collected for non-research patients only at a third center, collected for billing of researcher enrollees at a fourth center, and not documented at all at a fifth center.

There were differences in data collection according to race/ethnicity as well. Racial/ethnic categories were generally similar across the centers—white, black/African American, Asian, Native American, Hispanic/Latino, and “other/unknown.”

However, the means of race/ethnicity data collection differed. Each center collected self-reported data on race/ethnicity, but 2 centers included data from staff observations.

Two centers compared the proportions of racial/ethnic groups enrolled in trials with those of their catchment area(s). But the others did not.

The centers also differed in how they defined their patient catchment area, in terms of their cancer patient-vs-general-population specificity, levels of specificity, and geographic coverage.

That merits notice, according to the researchers, because National Cancer Institute cancer centers are required to accrue women and minorities to clinical trials in rough proportion to the cancer patient population of the center’s primary catchment area.

Given these findings, the researchers recommended better standardization of data definition, collection, and reporting as an essential first step toward expanding minority participation in clinical trials.

The team also advised that cancer centers collect socioeconomic data, including a patient’s income and education levels, given past evidence of the strong link between socioeconomic status and cancer outcomes.

Finally, the group recommended collecting patient zip codes and insurance status to allow researchers to assess differences in access to clinical trials that may be related to geography and the availability of health insurance coverage.

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Cancer survivors’ risk of health problems increases with age

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Cancer survivors’ risk of health problems increases with age

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cancer patient and her father

Credit: Rhoda Baer

The “health gap” between childhood cancer survivors and their siblings widens with age, according to a study published in the Journal of Clinical Oncology.

Cancer survivors aged 20 to 34 years old were 3.8 times more likely than siblings of the same age to develop new cancers and other serious health conditions.

By age 35 and beyond, survivors had a 5-fold greater risk.

“Survivors remain at risk for serious health problems into their 40s and 50s, decades after they have completed treatment for childhood cancer,” said study author Gregory Armstrong, MD, of the St Jude Children’s Research Hospital in Memphis, Tennessee.

“In fact, for survivors, the risk of illness and death increases significantly beyond the age of 35. Their siblings don’t share these same risks.”

Dr Armstrong and his colleagues uncovered these results by analyzing data from the Childhood Cancer Survivor Study, which included 14,359 survivors and 4301 healthy siblings.

The patients had been diagnosed with leukemias, lymphomas, and other pediatric cancers before age 21 and were followed for a median of 24.5 years (range, 5 to 39.3 years).

The researchers compared survivors to age-matched siblings, evaluating the incidence of severe, disabling, life-threatening, or fatal health conditions. This included new malignancies as well as diseases of the heart, lungs, liver, kidneys, and hormones.

The team found a heightened risk of these health conditions among cancer survivors. And that risk increased as the survivors aged.

At 20 years of age, 16% of survivors had serious health conditions, compared to 3.3% of siblings. But by age 50, the incidence had increased to 53.6% among survivors and 19.8% among siblings. At 50, 22.5% of survivors had at least 2 serious health problems, and 10.1% had 3 or more.

In a multivariate analysis, the hazard ratio for developing serious health conditions was significantly higher among survivors aged 35 and older than for those aged 20 to 34 (P=0.03).

Among survivors who reached age 35 without serious health problems, 25.9% developed a significant health problem in the next decade. In comparison, 6% of siblings developed their first serious health condition between the ages of 35 and 45.

In addition to showing a health gap between childhood cancer survivors and their siblings, this research adds to evidence that survivors experience accelerated aging. The 24-year-old cancer survivors had roughly the same cumulative incidence of grade 3 to 5 health conditions (19.6%) as the 50-year-old siblings (19.8%).

Overall, these findings highlight the importance of lifelong, risk-based healthcare for childhood cancer survivors, Dr Armstrong said. Depending on their cancer treatment and other risk factors, follow-up care may include performing health checks at a younger age than is recommended for the general public.

This study involved survivors whose cancer was diagnosed between 1970 and 1986. The researchers are now studying the health of adult cancer survivors from a more recent treatment era.

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Doctor consulting with a

cancer patient and her father

Credit: Rhoda Baer

The “health gap” between childhood cancer survivors and their siblings widens with age, according to a study published in the Journal of Clinical Oncology.

Cancer survivors aged 20 to 34 years old were 3.8 times more likely than siblings of the same age to develop new cancers and other serious health conditions.

By age 35 and beyond, survivors had a 5-fold greater risk.

“Survivors remain at risk for serious health problems into their 40s and 50s, decades after they have completed treatment for childhood cancer,” said study author Gregory Armstrong, MD, of the St Jude Children’s Research Hospital in Memphis, Tennessee.

“In fact, for survivors, the risk of illness and death increases significantly beyond the age of 35. Their siblings don’t share these same risks.”

Dr Armstrong and his colleagues uncovered these results by analyzing data from the Childhood Cancer Survivor Study, which included 14,359 survivors and 4301 healthy siblings.

The patients had been diagnosed with leukemias, lymphomas, and other pediatric cancers before age 21 and were followed for a median of 24.5 years (range, 5 to 39.3 years).

The researchers compared survivors to age-matched siblings, evaluating the incidence of severe, disabling, life-threatening, or fatal health conditions. This included new malignancies as well as diseases of the heart, lungs, liver, kidneys, and hormones.

The team found a heightened risk of these health conditions among cancer survivors. And that risk increased as the survivors aged.

At 20 years of age, 16% of survivors had serious health conditions, compared to 3.3% of siblings. But by age 50, the incidence had increased to 53.6% among survivors and 19.8% among siblings. At 50, 22.5% of survivors had at least 2 serious health problems, and 10.1% had 3 or more.

In a multivariate analysis, the hazard ratio for developing serious health conditions was significantly higher among survivors aged 35 and older than for those aged 20 to 34 (P=0.03).

Among survivors who reached age 35 without serious health problems, 25.9% developed a significant health problem in the next decade. In comparison, 6% of siblings developed their first serious health condition between the ages of 35 and 45.

In addition to showing a health gap between childhood cancer survivors and their siblings, this research adds to evidence that survivors experience accelerated aging. The 24-year-old cancer survivors had roughly the same cumulative incidence of grade 3 to 5 health conditions (19.6%) as the 50-year-old siblings (19.8%).

Overall, these findings highlight the importance of lifelong, risk-based healthcare for childhood cancer survivors, Dr Armstrong said. Depending on their cancer treatment and other risk factors, follow-up care may include performing health checks at a younger age than is recommended for the general public.

This study involved survivors whose cancer was diagnosed between 1970 and 1986. The researchers are now studying the health of adult cancer survivors from a more recent treatment era.

Doctor consulting with a

cancer patient and her father

Credit: Rhoda Baer

The “health gap” between childhood cancer survivors and their siblings widens with age, according to a study published in the Journal of Clinical Oncology.

Cancer survivors aged 20 to 34 years old were 3.8 times more likely than siblings of the same age to develop new cancers and other serious health conditions.

By age 35 and beyond, survivors had a 5-fold greater risk.

“Survivors remain at risk for serious health problems into their 40s and 50s, decades after they have completed treatment for childhood cancer,” said study author Gregory Armstrong, MD, of the St Jude Children’s Research Hospital in Memphis, Tennessee.

“In fact, for survivors, the risk of illness and death increases significantly beyond the age of 35. Their siblings don’t share these same risks.”

Dr Armstrong and his colleagues uncovered these results by analyzing data from the Childhood Cancer Survivor Study, which included 14,359 survivors and 4301 healthy siblings.

The patients had been diagnosed with leukemias, lymphomas, and other pediatric cancers before age 21 and were followed for a median of 24.5 years (range, 5 to 39.3 years).

The researchers compared survivors to age-matched siblings, evaluating the incidence of severe, disabling, life-threatening, or fatal health conditions. This included new malignancies as well as diseases of the heart, lungs, liver, kidneys, and hormones.

The team found a heightened risk of these health conditions among cancer survivors. And that risk increased as the survivors aged.

At 20 years of age, 16% of survivors had serious health conditions, compared to 3.3% of siblings. But by age 50, the incidence had increased to 53.6% among survivors and 19.8% among siblings. At 50, 22.5% of survivors had at least 2 serious health problems, and 10.1% had 3 or more.

In a multivariate analysis, the hazard ratio for developing serious health conditions was significantly higher among survivors aged 35 and older than for those aged 20 to 34 (P=0.03).

Among survivors who reached age 35 without serious health problems, 25.9% developed a significant health problem in the next decade. In comparison, 6% of siblings developed their first serious health condition between the ages of 35 and 45.

In addition to showing a health gap between childhood cancer survivors and their siblings, this research adds to evidence that survivors experience accelerated aging. The 24-year-old cancer survivors had roughly the same cumulative incidence of grade 3 to 5 health conditions (19.6%) as the 50-year-old siblings (19.8%).

Overall, these findings highlight the importance of lifelong, risk-based healthcare for childhood cancer survivors, Dr Armstrong said. Depending on their cancer treatment and other risk factors, follow-up care may include performing health checks at a younger age than is recommended for the general public.

This study involved survivors whose cancer was diagnosed between 1970 and 1986. The researchers are now studying the health of adult cancer survivors from a more recent treatment era.

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Adult minorities underrepresented in cancer trials

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Adult minorities underrepresented in cancer trials

Nurse treating a cancer patient

Credit: Rhoda Baer

New research indicates that less than 2% of trials funded by the National Cancer Institute focus on racial and ethnic minorities, and minority participation in adult cancer trials is not representative of the US population.

The researchers said these findings suggest we must do more to promote minority-focused research and clinical trial recruitment, beyond the National Institutes of Health (NIH) Revitalization Act of 1993, which mandated the appropriate inclusion of minorities in all NIH-funded research.

“What is needed is deliberate effort,” said study author Moon Chen, Jr, PhD, of the University of California, Davis. “Minorities are not hard to reach. They are hardly reached.”

To assess minority inclusion in clinical trials, Dr Chen and his colleagues searched ClinicalTrials.gov, looking for trials sponsored by the National Cancer Institute that were available in January 2013.

They searched using terms for different minority groups, then counted the number of clinical trials with a primary focus on a particular ethnic or minority population. Roughly 150 trials out of 10,000—or less than 2%—met the criteria.

The researchers also reviewed abstracts and articles accessed from January through March 2013 on PubMed to find those that specifically examined minority accrual in clinical trials.

Of the 42 citations found, 5 included reports explicitly discussing participation levels by race and ethnicity. Those reports revealed an “encouraging but less than optimal” increase in specification of race or ethnicity in published results of clinical trials.

Dr Chen and his colleagues also reported that participation of adult minorities is not proportional to their representation in the US population.

For example, African Americans experience the highest cancer incidence of any racial group (593.7 cases per 100,000), but they have the lowest rates of cancer trial participation (tied with Hispanics), at 1.3%. It’s important to note, however, that clinical trial participation is low for all adult cancer patients, at 3% to 5%.

In contrast, the researchers pointed out that 60% of all patients under age 15 are enrolled in clinical trials. And minority representation among children is excellent, either equal to or greater than their proportion of the population.

To put the adult population on par with the pediatric population, researchers should design trials to include and focus on specific populations, Dr Chen said. Furthermore, scientific journals should insist on appropriate representation and analyses of NIH research by race and ethnicity.

“Whatever happens in the laboratory or in the clinic needs to be applied to solving real-world problems,” Dr Chen said. “And those relate to the disproportionate effects of cancer and other diseases on racial and ethnic minorities.”

Dr Chen and his colleagues reported this research in Cancer.

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Nurse treating a cancer patient

Credit: Rhoda Baer

New research indicates that less than 2% of trials funded by the National Cancer Institute focus on racial and ethnic minorities, and minority participation in adult cancer trials is not representative of the US population.

The researchers said these findings suggest we must do more to promote minority-focused research and clinical trial recruitment, beyond the National Institutes of Health (NIH) Revitalization Act of 1993, which mandated the appropriate inclusion of minorities in all NIH-funded research.

“What is needed is deliberate effort,” said study author Moon Chen, Jr, PhD, of the University of California, Davis. “Minorities are not hard to reach. They are hardly reached.”

To assess minority inclusion in clinical trials, Dr Chen and his colleagues searched ClinicalTrials.gov, looking for trials sponsored by the National Cancer Institute that were available in January 2013.

They searched using terms for different minority groups, then counted the number of clinical trials with a primary focus on a particular ethnic or minority population. Roughly 150 trials out of 10,000—or less than 2%—met the criteria.

The researchers also reviewed abstracts and articles accessed from January through March 2013 on PubMed to find those that specifically examined minority accrual in clinical trials.

Of the 42 citations found, 5 included reports explicitly discussing participation levels by race and ethnicity. Those reports revealed an “encouraging but less than optimal” increase in specification of race or ethnicity in published results of clinical trials.

Dr Chen and his colleagues also reported that participation of adult minorities is not proportional to their representation in the US population.

For example, African Americans experience the highest cancer incidence of any racial group (593.7 cases per 100,000), but they have the lowest rates of cancer trial participation (tied with Hispanics), at 1.3%. It’s important to note, however, that clinical trial participation is low for all adult cancer patients, at 3% to 5%.

In contrast, the researchers pointed out that 60% of all patients under age 15 are enrolled in clinical trials. And minority representation among children is excellent, either equal to or greater than their proportion of the population.

To put the adult population on par with the pediatric population, researchers should design trials to include and focus on specific populations, Dr Chen said. Furthermore, scientific journals should insist on appropriate representation and analyses of NIH research by race and ethnicity.

“Whatever happens in the laboratory or in the clinic needs to be applied to solving real-world problems,” Dr Chen said. “And those relate to the disproportionate effects of cancer and other diseases on racial and ethnic minorities.”

Dr Chen and his colleagues reported this research in Cancer.

Nurse treating a cancer patient

Credit: Rhoda Baer

New research indicates that less than 2% of trials funded by the National Cancer Institute focus on racial and ethnic minorities, and minority participation in adult cancer trials is not representative of the US population.

The researchers said these findings suggest we must do more to promote minority-focused research and clinical trial recruitment, beyond the National Institutes of Health (NIH) Revitalization Act of 1993, which mandated the appropriate inclusion of minorities in all NIH-funded research.

“What is needed is deliberate effort,” said study author Moon Chen, Jr, PhD, of the University of California, Davis. “Minorities are not hard to reach. They are hardly reached.”

To assess minority inclusion in clinical trials, Dr Chen and his colleagues searched ClinicalTrials.gov, looking for trials sponsored by the National Cancer Institute that were available in January 2013.

They searched using terms for different minority groups, then counted the number of clinical trials with a primary focus on a particular ethnic or minority population. Roughly 150 trials out of 10,000—or less than 2%—met the criteria.

The researchers also reviewed abstracts and articles accessed from January through March 2013 on PubMed to find those that specifically examined minority accrual in clinical trials.

Of the 42 citations found, 5 included reports explicitly discussing participation levels by race and ethnicity. Those reports revealed an “encouraging but less than optimal” increase in specification of race or ethnicity in published results of clinical trials.

Dr Chen and his colleagues also reported that participation of adult minorities is not proportional to their representation in the US population.

For example, African Americans experience the highest cancer incidence of any racial group (593.7 cases per 100,000), but they have the lowest rates of cancer trial participation (tied with Hispanics), at 1.3%. It’s important to note, however, that clinical trial participation is low for all adult cancer patients, at 3% to 5%.

In contrast, the researchers pointed out that 60% of all patients under age 15 are enrolled in clinical trials. And minority representation among children is excellent, either equal to or greater than their proportion of the population.

To put the adult population on par with the pediatric population, researchers should design trials to include and focus on specific populations, Dr Chen said. Furthermore, scientific journals should insist on appropriate representation and analyses of NIH research by race and ethnicity.

“Whatever happens in the laboratory or in the clinic needs to be applied to solving real-world problems,” Dr Chen said. “And those relate to the disproportionate effects of cancer and other diseases on racial and ethnic minorities.”

Dr Chen and his colleagues reported this research in Cancer.

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Company issues nationwide recall of blood sets

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Company issues nationwide recall of blood sets

Blood for transfusion

Credit: Elise Amendola

Hospira, Inc. has announced a nationwide recall of 2 lots of Hemoset Dual Channel Plum Sets, which are used to administer blood products.

The affected lots—28005-5H and 34100-5H (list number 11241-03)—contain an incorrect component.

Using these sets, which were distributed across the US, could result in the over-delivery of blood products.

However, Hospira has not received any reports of adverse events associated with the sets. The recall is a precautionary measure.

Possible risk associated with the sets

The Hemostat Dual Channel Plum Set is designed to administer blood and blood products via the Plum infusion pump. If the Plum infusion pump is used with one of the sets being recalled, the blood product will be delivered at its intended dosage.

However, if one of the affected sets is removed from the Plum infusion pump and used in a gravity infusion, there is a risk of over-delivering blood products, due to the incorrect component—a lower lid.

In a gravity delivery, the correct lower lid dispenses 15 drops per mL. But the incorrect lower lid dispenses 10 drops per mL. If a caregiver does not realize that each drop contains more volume, over-delivery could occur.

Over-delivery of blood products in the populations at greatest risk (eg, neonates and patients with heart and/or kidney failure) may result in injuries that require medical intervention. These injuries are expected to fully resolve with medical intervention.

Steps to take

The sets impacted by the recall were distributed to US healthcare and veterinary facilities from May 2013 through December 2013.

Customers should check their inventory and immediately quarantine any affected sets. They should also inform individuals who might use the sets about the recall.

The affected sets should be returned to Stericycle. To do so, call 1-888-240-4282, Monday through Friday between 8 am and 5 pm Eastern Time.

For medical inquiries, contact Hospira Medical Communications at 1-800-615-0187.

Adverse reactions or quality problems associated with the use of these sets can be reported to the US Food and Drug Administration’s MedWatch Adverse Event Reporting Program.

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Blood for transfusion

Credit: Elise Amendola

Hospira, Inc. has announced a nationwide recall of 2 lots of Hemoset Dual Channel Plum Sets, which are used to administer blood products.

The affected lots—28005-5H and 34100-5H (list number 11241-03)—contain an incorrect component.

Using these sets, which were distributed across the US, could result in the over-delivery of blood products.

However, Hospira has not received any reports of adverse events associated with the sets. The recall is a precautionary measure.

Possible risk associated with the sets

The Hemostat Dual Channel Plum Set is designed to administer blood and blood products via the Plum infusion pump. If the Plum infusion pump is used with one of the sets being recalled, the blood product will be delivered at its intended dosage.

However, if one of the affected sets is removed from the Plum infusion pump and used in a gravity infusion, there is a risk of over-delivering blood products, due to the incorrect component—a lower lid.

In a gravity delivery, the correct lower lid dispenses 15 drops per mL. But the incorrect lower lid dispenses 10 drops per mL. If a caregiver does not realize that each drop contains more volume, over-delivery could occur.

Over-delivery of blood products in the populations at greatest risk (eg, neonates and patients with heart and/or kidney failure) may result in injuries that require medical intervention. These injuries are expected to fully resolve with medical intervention.

Steps to take

The sets impacted by the recall were distributed to US healthcare and veterinary facilities from May 2013 through December 2013.

Customers should check their inventory and immediately quarantine any affected sets. They should also inform individuals who might use the sets about the recall.

The affected sets should be returned to Stericycle. To do so, call 1-888-240-4282, Monday through Friday between 8 am and 5 pm Eastern Time.

For medical inquiries, contact Hospira Medical Communications at 1-800-615-0187.

Adverse reactions or quality problems associated with the use of these sets can be reported to the US Food and Drug Administration’s MedWatch Adverse Event Reporting Program.

Blood for transfusion

Credit: Elise Amendola

Hospira, Inc. has announced a nationwide recall of 2 lots of Hemoset Dual Channel Plum Sets, which are used to administer blood products.

The affected lots—28005-5H and 34100-5H (list number 11241-03)—contain an incorrect component.

Using these sets, which were distributed across the US, could result in the over-delivery of blood products.

However, Hospira has not received any reports of adverse events associated with the sets. The recall is a precautionary measure.

Possible risk associated with the sets

The Hemostat Dual Channel Plum Set is designed to administer blood and blood products via the Plum infusion pump. If the Plum infusion pump is used with one of the sets being recalled, the blood product will be delivered at its intended dosage.

However, if one of the affected sets is removed from the Plum infusion pump and used in a gravity infusion, there is a risk of over-delivering blood products, due to the incorrect component—a lower lid.

In a gravity delivery, the correct lower lid dispenses 15 drops per mL. But the incorrect lower lid dispenses 10 drops per mL. If a caregiver does not realize that each drop contains more volume, over-delivery could occur.

Over-delivery of blood products in the populations at greatest risk (eg, neonates and patients with heart and/or kidney failure) may result in injuries that require medical intervention. These injuries are expected to fully resolve with medical intervention.

Steps to take

The sets impacted by the recall were distributed to US healthcare and veterinary facilities from May 2013 through December 2013.

Customers should check their inventory and immediately quarantine any affected sets. They should also inform individuals who might use the sets about the recall.

The affected sets should be returned to Stericycle. To do so, call 1-888-240-4282, Monday through Friday between 8 am and 5 pm Eastern Time.

For medical inquiries, contact Hospira Medical Communications at 1-800-615-0187.

Adverse reactions or quality problems associated with the use of these sets can be reported to the US Food and Drug Administration’s MedWatch Adverse Event Reporting Program.

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CDU is Associated with Decreased LOS

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Caring for patients in a hospitalist‐run clinical decision unit is associated with decreased length of stay without increasing revisit rates

Hospitalists play a crucial role in improving hospital throughput and length of stay (LOS). The clinical decision unit (CDU) or observation unit (OU) is a strategy that was developed to facilitate both aims. CDUs and OUs are units where patients can be managed in the hospital for up to 24 hours prior to a decision being made to admit or discharge. Observation care is provided to patients who require further treatment or monitoring beyond what is accomplished in the emergency department (ED), but who do not require inpatient admission. CDUs arose in the 1990s in response to a desire to decrease inpatient costs as well as changing Medicare guidelines, which recognized observation status. Initially, CDUs and OUs were located within the ED and run by emergency medicine physicians. However, at the turn of the 21st century, hospitalists became involved in observation medicine, and the Society of Hospital Medicine issued a white paper on the OU in 2007. [1] Today, up to 50% of CDUs and OUs nationally are managed by hospitalists and located physically outside of the ED.[2, 3]

Despite the fact that nearly half of all CDUs and OUs nationally are run by hospitalists, there has been little published regarding the impact of hospitalist‐driven units. This study demonstrates the effect of observation care delivered in a hospitalist‐run geographic CDU. The primary objective was to determine the impact on LOS for patients in observation status managed in a hospitalist‐run CDU compared with LOS for observation patients with the same diagnoses cared for on medicalsurgical units prior to the existence of the CDU. The secondary objective was to determine the effect on the 30‐day ED or hospital revisit rate, as well as ED LOS. This work will guide health systems, hospitalist groups, and physicians in their decision making regarding the future structure and process of CDUs.

METHODS

Study Design

The Cooper University Hospital institutional review board approved this study. The study took place at Cooper University Hospital, a large, urban, academic safety‐net hospital providing tertiary care located in Camden, New Jersey.

We performed a retrospective observational study of all adult observation encounters at the study hospital from July 2010 to January 2011, and July 2011 through January 2012. During the second time period, patients could have been managed in the CDU or on a medicalsurgical unit. We recorded the following demographic data: age, gender, race, principal diagnosis, and payer, as well as several outcomes of interest, including: LOS (defined as the time separating the admitting physician order from discharge), ED visits within 30 days of discharge, and hospital revisits (observation or inpatient) within 30 days.

Data Sources

Data were culled by the institution's performance improvement department from the electronic medical record, as well as cost accounting and claims‐based sources.

Clinical Decision Unit

The CDU at Cooper University Hospital opened in June 2011 and is a 20‐bed geographically distinct unit adjacent to the ED. During the study period, it was staffed 24 hours a day by a hospitalist and a nurse practitioner as well as dedicated nurses and critical care technicians. Patients meeting observation status in the ED were eligible for the CDU provided that they fulfilled the CDU placement guidelines including that they were more likely than not to be discharged within a period of 24 hours of CDU care, did not meet inpatient admission criteria, did not require new placement in a rehabilitation or extended‐care facility, and did not require one‐on‐one monitoring. Additional exclusion criteria included severe vital sign or laboratory abnormalities. The overall strategy of the guidelines was to facilitate a pull culture, where the majority of observation patients were brought from the ED to the CDU once it was determined that they did not require inpatient care. The CDU had order sets and protocols in place for many of the common diagnoses. All CDU patients received priority laboratory and radiologic testing as well as priority consultation from specialty services. Medication reconciliation was performed by a pharmacy technician for higher‐risk patients, identified by Project BOOST (Better Outcomes by Optimizing Safe Transitions) criteria.[4] Structured multidisciplinary rounds occurred daily including the hospitalist, nurse practitioner, registered nurses, case manager, and pharmacy technician. A discharge planner was available to schedule follow‐up appointments.

Although chest pain was the most common CDU diagnosis, the CDU was designed to care for the majority of the hospital's observation patients rather than focus specifically on chest pain. Patients with chest pain who met observation criteria were transferred from the ED to the CDU, rather than a medicalsurgical unit, provided they did not have: positive cardiac enzymes, an electrocardiogram indicative of ischemia, known coronary artery disease presenting with pain consistent with acute coronary syndrome, need for heparin or nitroglycerin continuous infusion, symptomatic or unresolved arrhythmia, congestive heart failure meeting inpatient criteria, hypertensive urgency or emergency, pacemaker malfunction, pericarditis, or toxicity from cardiac drugs. Cardiologist consultants were involved in the care of nearly all CDU patients with chest pain.

Observation Status Determination

During the study period, observation status was recommended by a case manager in the ED based on Milliman (Milliman Care Guidelines) or McKesson InterQual (McKesson Corporation) criteria, once it was determined by the ED physician that the patient had failed usual ED care and required hospitalization. Observation status was assigned by the admitting (non‐ED) physician, who placed the order for inpatient admission or observation. Other than the implementation of the CDU, there were no significant changes to the process or criteria for assigning observation status, admission order sets, or the hospital's electronic medical record during this time period.

Statistical Analysis

Continuous data are presented as mean ( standard deviation [SD]) or median (25%75% interquartile range) as specified, and differences were assessed using one‐way analysis of variance testing and Mann‐Whitney U testing. Categorical data are presented as count (percentage) and differences evaluated using [2] analysis. P values of 0.05 or less were considered statistically significant.

To account for differences in groups with regard to outcomes, we performed a multivariate regression analysis. The following variables were entered: age (years), gender, race (African American vs other), admission diagnosis (chest pain vs other), and insurance status (Medicare vs other). All variables were entered simultaneously without forcing. Statistical analyses were done using the SPSS 20.0 Software (SPSS Inc., Chicago, IL).

RESULTS

Demographics

There were a total of 3735 patients included in the study: 1650 in the pre‐CDU group, 1469 in the post‐CDU group, and 616 in the post‐CDU group on medicalsurgical units. The post‐CDU period had a total of 2085 patients. Patients in the CDU group were younger and were more likely to have chest pain as the admission diagnosis. Patient demographics are presented in Table 1.

Patient Demographics by Group
Variable Pre‐CDU, n=1,650 Post‐CDU, n=1,469 PostNon‐CDU, n=616 P, CDU vs Pre‐CDU P, Non‐CDU vs Pre‐CDU P, CDU vs Non‐CDU
  • NOTE: Abbreviations: CDU, clinical decision unit.

Age, y [range] 56 [4569] 53 [4364] 57 [44.370] <0.001 0.751 0.001
Female gender 918 (55.6%) 833(56.7%) 328 (53.2%) 0.563 0.319 0.148
African American race 574 (34.8%) 505 (34.4%) 174 (28.2%) 0.821 0.004 0.007
Admission diagnosis
Chest pain 462 (38%) 528 (35.9%) 132 (21.4%) <0.001 0.002 <0.001
Syncope 93 (5.6%) 56 (3.8%) 15 (2.4%) 0.018 0.001 0.145
Abdominal pain 46 (2.8%) 49 (3.3%) 20(3.2%) 0.404 0.575 1.0
Other 1,049 (63.6%) 836 (56.9%) 449 (72.9%) <0.001 <0.001 <0.001
Third‐party payer
Medicare 727 (44.1%) 491 (33.4%) 264(43.4%) <0.001 0.634 <0.001
Charity care 187 (11.3%) 238 (16.2%) 73 (11.9%) <0.001 0.767 0.010
Commercial 185 (11.1%) 214 (14.6%) 87 (14.1%) 0.005 0.059 0.838
Medicaid 292 (17.7%) 280 (19.1%) 100 (16.2%) 0.331 0.454 0.136
Other 153 (9.3%) 195 (13.3%) 60 (9.9%) <0.001 0.746 0.028
Self‐pay 106 (6.4%) 51(3.5%) 32 (5.2%) <0.001 0.323 0.085

Outcomes of Interest

There was a statistically significant association between LOS and CDU implementation (Table 2). Observation patients cared for in the CDU had a lower LOS than observation patients cared for on the medicalsurgical units during the same time period (17.6 vs 26.1 hours, P<0.0001).

Revisit Rates and Length of Stay Pre‐ and Post‐CDU Implementation
Outcome Pre‐CDU, n=1,650 Post‐CDU, n=1,469 PostNon‐CDU, n=616 P, CDU vs Pre‐CDU P, Non‐CDU vs Pre‐CDU P, CDU vs Non‐CDU
  • NOTE: Abbreviations: CDU, clinical decision unit; ED, emergency department; LOS, length of stay.

All patients, n=3,735
30‐day ED or hospital revisit 326 (19.8%) 268 (18.2%) 123 (17.2%) 0.294 0.906 0.357
Median LOS, h 27.1 [17.446.4] 17.6 [12.122.8] 26.1 [16.941.2] <0.001 0.004 <0.001
Chest‐pain patients, n=1,122
30‐day ED or hospital revisit 69 (14.9%) 82 (15.5%) 23 (17.4%) 0.859 0.496 0.596
Median LOS, h 22 [15.838.9] 17.3 [10.922.4] 23.2 [13.843.1] <0.001 0.995 <0.001
Other diagnoses, n=2,613
30‐day ED or hospital revisit 257 (21.6%) 186 (19.8%) 100 (18.4%) 0.307 0.693 0.727
Median LOS, h 30.4 [18.649.4] 17.8 [12.923] 26.7 [17.231.1] <0.001 <0.001 <0.001

In total, there were 717 total revisits including ED visits and hospital stays within 30 days of discharge (Table 2). Of all the observation encounters in the study, 19.2% were followed by a revisit within 30 days. There were no differences in the 30‐day post‐ED visit rates in between periods and between groups.

Mean ED LOS for hospitalized patients was examined for a sample of the pre‐ and post‐CDU periods, namely November 2010 to January 2011 and November 2011 to January 2012. The mean ED LOS decreased from 410 minutes (SD=61) to 393 minutes (SD=51) after implementation of the CDU (P=0.037).

To account for possible skewing of the data, we transformed LOS into ln (natural log) LOS and found the following means (SD): group 1 was 3.27 (0.94), group 2 was 2.78 (0.6), and group 3 was 3.1 (0.93). Using an independent t test, we found a significant difference between groups 1 and 2, 2 and 3, as well as 1 and 3 (P<0.001 for all).

Chest‐Pain Subgroup Analysis

We analyzed the data specifically for the 1122 patients discharged with a diagnosis of chest pain. LOS was significantly lower for patients in the CDU compared to either pre‐CDU or observation on floors (Table 2).

Multivariate Regression Analysis

We performed a linear regression analysis using the following variables: age, race, gender, diagnosis, insurance status, and study period (pre‐CDU, post‐CDU, and postnon‐CDU). We performed 3 different comparisons: pre‐CDU vs post‐CDU, postnon‐CDU vs post‐CDU, and postnon‐CDU vs pre‐CDU. After adjusting for other variables, the postnon‐CDU group was significantly associated with higher LOS (P<0.001). The pre‐CDU group was associated with higher LOS than both the post‐CDU and postnon‐CDU groups (P<0.001 for both).

DISCUSSION

In our study of a hospitalist‐run CDU for observation patients, we observed that the care in the CDU was associated with a lower median LOS, but no increase in ED or hospital revisits within 30 days.

Previous studies have reported the impact of clinical observation or clinical diagnosis units, particularly chest‐pain units.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] Studies of hospitalist‐run units suggest shorter LOS in the entire hospital,[16] or in the target unit.[17] Although one study suggested a lower 30‐day readmission rate,[18] most others did not describe this effect.[16, 17] Our study differs from previous research in that our program employed a pull‐culture aimed at accepting the majority of observation status patients without focusing on a particular diagnosis. We also implemented a structured multidisciplinary team focused on expediting care and utilized BOOST‐framed transitions, including targeted medication reconciliation and tools such as teach‐back.

The CDU in our hospital produced shorter LOS even compared to our non‐CDU units, but the revisit rate did not improve despite activities to reduce revisits. During the study period, efforts to decrease readmissions were implemented in various areas of our hospital, but not a comprehensive institution‐wide readmissions strategy. Lack of impact on revisits could be viewed as a positive finding, in that shorter LOS did not result in patients being discharged home before clinically stable. Alternatively, lack of impact could be due to the uncertain effectiveness of BOOST specifically[19, 20, 21] or inpatient‐targeted transitions interventions more generally.[22]

Our study has certain limitations. Findings in our single‐center study in an urban academic medical center may not apply to CDUs in other settings. As a prepost design, our study is subject to external trends for which our analyses may be unable to account. For example, during CDU implementation, there were hospital‐wide initiatives aimed at improving inpatient LOS, including complex case rounds, increased use of active bed management, and improved case management efforts to decrease LOS. These may have been a factor in the small decrease in observation LOS seen in the medicalsurgical patients during the post period. Additionally, though we have attempted to control for possible confounders, there could have been differences in the study groups for which we were unable to account, including code status or social variables such as homelessness, which played a role in our revisit outcomes. The decrease in LOS by 35%, or 9.5 hours, in CDU patients is clinically important, as it allows low‐risk patients to spend less time in the hospital where they may have been at risk of hospital‐acquired conditions; however, this study did not include patient satisfaction data. It would be important to measure the effect on patient experience of potentially spending 1 fewer night in the hospital. Finally, our CDU was designed with specific clinical criteria for inclusion and exclusion. Patients who were higher risk or expected to need more than 24 hours of care were not placed in the CDU. We were not able to adjust our analyses for factors that were not in our data, such as severe vital sign or laboratory abnormalities or a physician's clinical impression of a patient. It is possible, therefore, that referral bias may have occurred and influenced our results. The fact that non‐CDU chest‐pain patients in the post‐CDU period did not experience any decrease in LOS, whereas other medicalsurgical observation patients did, may be an example of this bias. Patients were excluded from the CDU by virtue of being deemed higher risk as described in Methods section. We were unable to adjust for these differences.

Implementation of CDUs may be useful for health systems seeking to improve hospital throughput and improve utilization among common but low‐acuity patient groups. Although our initial results are promising, the concept of a CDU may require enhancements. For example, at our hospital we are addressing transitions of care by looking at models that address patient risk through a systematic process, and then target individuals for specific interventions to prevent revisits. Moreover, the study of CDUs should report impact on patient and referring physician satisfaction, and whether CDUs can reduce per‐case costs.

CONCLUSION

Caring for patients in a hospitalist‐run geographic CDU was associated with a 35% decrease in observation LOS for CDU patients compared with a 3.7% decrease for observation patients cared for elsewhere in the hospital. CDU patients' LOS was significantly decreased without increasing ED or hospital revisit rates.

Acknowledgments

The authors would like to thank Ken Travis for excellent data support.

Files
References
  1. The observation unit: an operational overview for the hospitalist. Society of Hospital Medicine website. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=White_Papers18(12):13711379.
  2. Venkatesh AK, Geisler BP, Gibson Chambers JJ, Baugh CW, Bohan JS, Schuur JD. Use of observation care in US emergency departments, 2001 to 2008. PLoS One. 2011;6(9):e24326.
  3. The Society of Hospital Medicine Project Boost (Better Outcomes by Optimizing Safe Transitions) Available at: http://www.hospitalmedicine.org/boost. Accessed on June 4, 2013.
  4. Gomez MA, Anderson JL, Karagounis LA, Muhlestein JB, Mooers FB. An emergency department‐based protocol for rapidly ruling out myocardial ischemia reduces hospital time and expense: results of a randomized study (ROMIO). J Am Coll Cardiol. 1996;28(1):2533.
  5. Siebens K, Miljoen H, Fieuws S, Drew B, De Geest S, Vrints C. Implementation of the guidelines for the management of patients with chest pain through a critical pathway approach improves length of stay and patient satisfaction but not anxiety. Crit Pathw Cardiol. 2010;9(1):3034.
  6. Roberts RR, Zalenski RJ, Mensah EK, et al. Costs of an emergency department‐based accelerated diagnostic protocol vs hospitalization in patients with chest pain: a randomized controlled trial. JAMA. 1997;278(20):16701676.
  7. Hoekstra JW, Gibler WB, Levy RC, et al. Emergency‐department diagnosis of acute myocardial infarction and ischemia: a cost analysis of two diagnostic protocols. Acad Emerg Med. 1994;1(2):103110.
  8. Graff LG, Dallara J, Ross MA, et al. Impact on the care of the emergency department chest pain patient from the chest pain evaluation registry (CHEPER) study. Am J Cardiol. 1997;80(5):563568.
  9. Gaspoz JM, Lee TH, Weinstein MC, et al. Cost‐effectiveness of a new short‐stay unit to “rule out” acute myocardial infarction in low risk patients. J Am Coll Cardiol. 1994;24(5):12491259.
  10. Rydman RJ, Isola ML, Roberts RR, et al. Emergency Department Observation Unit versus hospital inpatient care for a chronic asthmatic population: a randomized trial of health status outcome and cost. Med Care. 1998;36(4):599609.
  11. McDermott MF, Murphy DG, Zalenski RJ, et al. A comparison between emergency diagnostic and treatment unit and inpatient care in the management of acute asthma. Arch Intern Med. 1997;157(18):20552062.
  12. Tham KY, Kimura H, Nagurney T, Volinsky F. Retrospective review of emergency department patients with non‐variceal upper gastrointestinal hemorrhage for potential outpatient management. Acad Emerg Med. 1999;6(3):196201.
  13. Longstreth GF, Feitelberg SP. Outpatient care of selected patients with acute non‐variceal upper gastrointestinal haemorrhage. Lancet. 1995;345(8942):108111.
  14. Hostetler B, Leikin JB, Timmons JA, Hanashiro PK, Kissane K. Patterns of use of an emergency department‐based observation unit. Am J Ther. 2002;9(6):499502.
  15. Leykum LK, Huerta V, Mortensen E. Implementation of a hospitalist‐run observation unit and impact on length of stay (LOS): a brief report. J Hosp Med. 2010;5(9):E2E5.
  16. Myers JS, Bellini LM, Rohrbach J, Shofer FS, Hollander JE. Improving resource utilization in a teaching hospital: development of a nonteaching service for chest pain admissions. Acad Med. 2006;81(5):432435.
  17. Abenhaim HA, Kahn SR, Raffoul J, Becker MR. Program description: a hospitalist‐run, medical short‐stay unit in a teaching hospital. CMAJ. 2000;163(11):14771480.
  18. Hansen L, Greenwald JL, Budnitz T, et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8:421427.
  19. Auerbach A, Fang M, Glasheen J, et al. BOOST: evidence needing a lift. J Hosp Med. 2013;8:468469.
  20. Jha AK. BOOST and readmissions: thinking beyond the walls of the hospital. J Hosp Med. 2013;8:470471.
  21. Rennke S, Nguyen OK, Shoeb MH, et al. Hospital‐initiated transitional care interventions as a patient safety strategy. Ann Int Med. 2013;158:433440.
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Hospitalists play a crucial role in improving hospital throughput and length of stay (LOS). The clinical decision unit (CDU) or observation unit (OU) is a strategy that was developed to facilitate both aims. CDUs and OUs are units where patients can be managed in the hospital for up to 24 hours prior to a decision being made to admit or discharge. Observation care is provided to patients who require further treatment or monitoring beyond what is accomplished in the emergency department (ED), but who do not require inpatient admission. CDUs arose in the 1990s in response to a desire to decrease inpatient costs as well as changing Medicare guidelines, which recognized observation status. Initially, CDUs and OUs were located within the ED and run by emergency medicine physicians. However, at the turn of the 21st century, hospitalists became involved in observation medicine, and the Society of Hospital Medicine issued a white paper on the OU in 2007. [1] Today, up to 50% of CDUs and OUs nationally are managed by hospitalists and located physically outside of the ED.[2, 3]

Despite the fact that nearly half of all CDUs and OUs nationally are run by hospitalists, there has been little published regarding the impact of hospitalist‐driven units. This study demonstrates the effect of observation care delivered in a hospitalist‐run geographic CDU. The primary objective was to determine the impact on LOS for patients in observation status managed in a hospitalist‐run CDU compared with LOS for observation patients with the same diagnoses cared for on medicalsurgical units prior to the existence of the CDU. The secondary objective was to determine the effect on the 30‐day ED or hospital revisit rate, as well as ED LOS. This work will guide health systems, hospitalist groups, and physicians in their decision making regarding the future structure and process of CDUs.

METHODS

Study Design

The Cooper University Hospital institutional review board approved this study. The study took place at Cooper University Hospital, a large, urban, academic safety‐net hospital providing tertiary care located in Camden, New Jersey.

We performed a retrospective observational study of all adult observation encounters at the study hospital from July 2010 to January 2011, and July 2011 through January 2012. During the second time period, patients could have been managed in the CDU or on a medicalsurgical unit. We recorded the following demographic data: age, gender, race, principal diagnosis, and payer, as well as several outcomes of interest, including: LOS (defined as the time separating the admitting physician order from discharge), ED visits within 30 days of discharge, and hospital revisits (observation or inpatient) within 30 days.

Data Sources

Data were culled by the institution's performance improvement department from the electronic medical record, as well as cost accounting and claims‐based sources.

Clinical Decision Unit

The CDU at Cooper University Hospital opened in June 2011 and is a 20‐bed geographically distinct unit adjacent to the ED. During the study period, it was staffed 24 hours a day by a hospitalist and a nurse practitioner as well as dedicated nurses and critical care technicians. Patients meeting observation status in the ED were eligible for the CDU provided that they fulfilled the CDU placement guidelines including that they were more likely than not to be discharged within a period of 24 hours of CDU care, did not meet inpatient admission criteria, did not require new placement in a rehabilitation or extended‐care facility, and did not require one‐on‐one monitoring. Additional exclusion criteria included severe vital sign or laboratory abnormalities. The overall strategy of the guidelines was to facilitate a pull culture, where the majority of observation patients were brought from the ED to the CDU once it was determined that they did not require inpatient care. The CDU had order sets and protocols in place for many of the common diagnoses. All CDU patients received priority laboratory and radiologic testing as well as priority consultation from specialty services. Medication reconciliation was performed by a pharmacy technician for higher‐risk patients, identified by Project BOOST (Better Outcomes by Optimizing Safe Transitions) criteria.[4] Structured multidisciplinary rounds occurred daily including the hospitalist, nurse practitioner, registered nurses, case manager, and pharmacy technician. A discharge planner was available to schedule follow‐up appointments.

Although chest pain was the most common CDU diagnosis, the CDU was designed to care for the majority of the hospital's observation patients rather than focus specifically on chest pain. Patients with chest pain who met observation criteria were transferred from the ED to the CDU, rather than a medicalsurgical unit, provided they did not have: positive cardiac enzymes, an electrocardiogram indicative of ischemia, known coronary artery disease presenting with pain consistent with acute coronary syndrome, need for heparin or nitroglycerin continuous infusion, symptomatic or unresolved arrhythmia, congestive heart failure meeting inpatient criteria, hypertensive urgency or emergency, pacemaker malfunction, pericarditis, or toxicity from cardiac drugs. Cardiologist consultants were involved in the care of nearly all CDU patients with chest pain.

Observation Status Determination

During the study period, observation status was recommended by a case manager in the ED based on Milliman (Milliman Care Guidelines) or McKesson InterQual (McKesson Corporation) criteria, once it was determined by the ED physician that the patient had failed usual ED care and required hospitalization. Observation status was assigned by the admitting (non‐ED) physician, who placed the order for inpatient admission or observation. Other than the implementation of the CDU, there were no significant changes to the process or criteria for assigning observation status, admission order sets, or the hospital's electronic medical record during this time period.

Statistical Analysis

Continuous data are presented as mean ( standard deviation [SD]) or median (25%75% interquartile range) as specified, and differences were assessed using one‐way analysis of variance testing and Mann‐Whitney U testing. Categorical data are presented as count (percentage) and differences evaluated using [2] analysis. P values of 0.05 or less were considered statistically significant.

To account for differences in groups with regard to outcomes, we performed a multivariate regression analysis. The following variables were entered: age (years), gender, race (African American vs other), admission diagnosis (chest pain vs other), and insurance status (Medicare vs other). All variables were entered simultaneously without forcing. Statistical analyses were done using the SPSS 20.0 Software (SPSS Inc., Chicago, IL).

RESULTS

Demographics

There were a total of 3735 patients included in the study: 1650 in the pre‐CDU group, 1469 in the post‐CDU group, and 616 in the post‐CDU group on medicalsurgical units. The post‐CDU period had a total of 2085 patients. Patients in the CDU group were younger and were more likely to have chest pain as the admission diagnosis. Patient demographics are presented in Table 1.

Patient Demographics by Group
Variable Pre‐CDU, n=1,650 Post‐CDU, n=1,469 PostNon‐CDU, n=616 P, CDU vs Pre‐CDU P, Non‐CDU vs Pre‐CDU P, CDU vs Non‐CDU
  • NOTE: Abbreviations: CDU, clinical decision unit.

Age, y [range] 56 [4569] 53 [4364] 57 [44.370] <0.001 0.751 0.001
Female gender 918 (55.6%) 833(56.7%) 328 (53.2%) 0.563 0.319 0.148
African American race 574 (34.8%) 505 (34.4%) 174 (28.2%) 0.821 0.004 0.007
Admission diagnosis
Chest pain 462 (38%) 528 (35.9%) 132 (21.4%) <0.001 0.002 <0.001
Syncope 93 (5.6%) 56 (3.8%) 15 (2.4%) 0.018 0.001 0.145
Abdominal pain 46 (2.8%) 49 (3.3%) 20(3.2%) 0.404 0.575 1.0
Other 1,049 (63.6%) 836 (56.9%) 449 (72.9%) <0.001 <0.001 <0.001
Third‐party payer
Medicare 727 (44.1%) 491 (33.4%) 264(43.4%) <0.001 0.634 <0.001
Charity care 187 (11.3%) 238 (16.2%) 73 (11.9%) <0.001 0.767 0.010
Commercial 185 (11.1%) 214 (14.6%) 87 (14.1%) 0.005 0.059 0.838
Medicaid 292 (17.7%) 280 (19.1%) 100 (16.2%) 0.331 0.454 0.136
Other 153 (9.3%) 195 (13.3%) 60 (9.9%) <0.001 0.746 0.028
Self‐pay 106 (6.4%) 51(3.5%) 32 (5.2%) <0.001 0.323 0.085

Outcomes of Interest

There was a statistically significant association between LOS and CDU implementation (Table 2). Observation patients cared for in the CDU had a lower LOS than observation patients cared for on the medicalsurgical units during the same time period (17.6 vs 26.1 hours, P<0.0001).

Revisit Rates and Length of Stay Pre‐ and Post‐CDU Implementation
Outcome Pre‐CDU, n=1,650 Post‐CDU, n=1,469 PostNon‐CDU, n=616 P, CDU vs Pre‐CDU P, Non‐CDU vs Pre‐CDU P, CDU vs Non‐CDU
  • NOTE: Abbreviations: CDU, clinical decision unit; ED, emergency department; LOS, length of stay.

All patients, n=3,735
30‐day ED or hospital revisit 326 (19.8%) 268 (18.2%) 123 (17.2%) 0.294 0.906 0.357
Median LOS, h 27.1 [17.446.4] 17.6 [12.122.8] 26.1 [16.941.2] <0.001 0.004 <0.001
Chest‐pain patients, n=1,122
30‐day ED or hospital revisit 69 (14.9%) 82 (15.5%) 23 (17.4%) 0.859 0.496 0.596
Median LOS, h 22 [15.838.9] 17.3 [10.922.4] 23.2 [13.843.1] <0.001 0.995 <0.001
Other diagnoses, n=2,613
30‐day ED or hospital revisit 257 (21.6%) 186 (19.8%) 100 (18.4%) 0.307 0.693 0.727
Median LOS, h 30.4 [18.649.4] 17.8 [12.923] 26.7 [17.231.1] <0.001 <0.001 <0.001

In total, there were 717 total revisits including ED visits and hospital stays within 30 days of discharge (Table 2). Of all the observation encounters in the study, 19.2% were followed by a revisit within 30 days. There were no differences in the 30‐day post‐ED visit rates in between periods and between groups.

Mean ED LOS for hospitalized patients was examined for a sample of the pre‐ and post‐CDU periods, namely November 2010 to January 2011 and November 2011 to January 2012. The mean ED LOS decreased from 410 minutes (SD=61) to 393 minutes (SD=51) after implementation of the CDU (P=0.037).

To account for possible skewing of the data, we transformed LOS into ln (natural log) LOS and found the following means (SD): group 1 was 3.27 (0.94), group 2 was 2.78 (0.6), and group 3 was 3.1 (0.93). Using an independent t test, we found a significant difference between groups 1 and 2, 2 and 3, as well as 1 and 3 (P<0.001 for all).

Chest‐Pain Subgroup Analysis

We analyzed the data specifically for the 1122 patients discharged with a diagnosis of chest pain. LOS was significantly lower for patients in the CDU compared to either pre‐CDU or observation on floors (Table 2).

Multivariate Regression Analysis

We performed a linear regression analysis using the following variables: age, race, gender, diagnosis, insurance status, and study period (pre‐CDU, post‐CDU, and postnon‐CDU). We performed 3 different comparisons: pre‐CDU vs post‐CDU, postnon‐CDU vs post‐CDU, and postnon‐CDU vs pre‐CDU. After adjusting for other variables, the postnon‐CDU group was significantly associated with higher LOS (P<0.001). The pre‐CDU group was associated with higher LOS than both the post‐CDU and postnon‐CDU groups (P<0.001 for both).

DISCUSSION

In our study of a hospitalist‐run CDU for observation patients, we observed that the care in the CDU was associated with a lower median LOS, but no increase in ED or hospital revisits within 30 days.

Previous studies have reported the impact of clinical observation or clinical diagnosis units, particularly chest‐pain units.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] Studies of hospitalist‐run units suggest shorter LOS in the entire hospital,[16] or in the target unit.[17] Although one study suggested a lower 30‐day readmission rate,[18] most others did not describe this effect.[16, 17] Our study differs from previous research in that our program employed a pull‐culture aimed at accepting the majority of observation status patients without focusing on a particular diagnosis. We also implemented a structured multidisciplinary team focused on expediting care and utilized BOOST‐framed transitions, including targeted medication reconciliation and tools such as teach‐back.

The CDU in our hospital produced shorter LOS even compared to our non‐CDU units, but the revisit rate did not improve despite activities to reduce revisits. During the study period, efforts to decrease readmissions were implemented in various areas of our hospital, but not a comprehensive institution‐wide readmissions strategy. Lack of impact on revisits could be viewed as a positive finding, in that shorter LOS did not result in patients being discharged home before clinically stable. Alternatively, lack of impact could be due to the uncertain effectiveness of BOOST specifically[19, 20, 21] or inpatient‐targeted transitions interventions more generally.[22]

Our study has certain limitations. Findings in our single‐center study in an urban academic medical center may not apply to CDUs in other settings. As a prepost design, our study is subject to external trends for which our analyses may be unable to account. For example, during CDU implementation, there were hospital‐wide initiatives aimed at improving inpatient LOS, including complex case rounds, increased use of active bed management, and improved case management efforts to decrease LOS. These may have been a factor in the small decrease in observation LOS seen in the medicalsurgical patients during the post period. Additionally, though we have attempted to control for possible confounders, there could have been differences in the study groups for which we were unable to account, including code status or social variables such as homelessness, which played a role in our revisit outcomes. The decrease in LOS by 35%, or 9.5 hours, in CDU patients is clinically important, as it allows low‐risk patients to spend less time in the hospital where they may have been at risk of hospital‐acquired conditions; however, this study did not include patient satisfaction data. It would be important to measure the effect on patient experience of potentially spending 1 fewer night in the hospital. Finally, our CDU was designed with specific clinical criteria for inclusion and exclusion. Patients who were higher risk or expected to need more than 24 hours of care were not placed in the CDU. We were not able to adjust our analyses for factors that were not in our data, such as severe vital sign or laboratory abnormalities or a physician's clinical impression of a patient. It is possible, therefore, that referral bias may have occurred and influenced our results. The fact that non‐CDU chest‐pain patients in the post‐CDU period did not experience any decrease in LOS, whereas other medicalsurgical observation patients did, may be an example of this bias. Patients were excluded from the CDU by virtue of being deemed higher risk as described in Methods section. We were unable to adjust for these differences.

Implementation of CDUs may be useful for health systems seeking to improve hospital throughput and improve utilization among common but low‐acuity patient groups. Although our initial results are promising, the concept of a CDU may require enhancements. For example, at our hospital we are addressing transitions of care by looking at models that address patient risk through a systematic process, and then target individuals for specific interventions to prevent revisits. Moreover, the study of CDUs should report impact on patient and referring physician satisfaction, and whether CDUs can reduce per‐case costs.

CONCLUSION

Caring for patients in a hospitalist‐run geographic CDU was associated with a 35% decrease in observation LOS for CDU patients compared with a 3.7% decrease for observation patients cared for elsewhere in the hospital. CDU patients' LOS was significantly decreased without increasing ED or hospital revisit rates.

Acknowledgments

The authors would like to thank Ken Travis for excellent data support.

Hospitalists play a crucial role in improving hospital throughput and length of stay (LOS). The clinical decision unit (CDU) or observation unit (OU) is a strategy that was developed to facilitate both aims. CDUs and OUs are units where patients can be managed in the hospital for up to 24 hours prior to a decision being made to admit or discharge. Observation care is provided to patients who require further treatment or monitoring beyond what is accomplished in the emergency department (ED), but who do not require inpatient admission. CDUs arose in the 1990s in response to a desire to decrease inpatient costs as well as changing Medicare guidelines, which recognized observation status. Initially, CDUs and OUs were located within the ED and run by emergency medicine physicians. However, at the turn of the 21st century, hospitalists became involved in observation medicine, and the Society of Hospital Medicine issued a white paper on the OU in 2007. [1] Today, up to 50% of CDUs and OUs nationally are managed by hospitalists and located physically outside of the ED.[2, 3]

Despite the fact that nearly half of all CDUs and OUs nationally are run by hospitalists, there has been little published regarding the impact of hospitalist‐driven units. This study demonstrates the effect of observation care delivered in a hospitalist‐run geographic CDU. The primary objective was to determine the impact on LOS for patients in observation status managed in a hospitalist‐run CDU compared with LOS for observation patients with the same diagnoses cared for on medicalsurgical units prior to the existence of the CDU. The secondary objective was to determine the effect on the 30‐day ED or hospital revisit rate, as well as ED LOS. This work will guide health systems, hospitalist groups, and physicians in their decision making regarding the future structure and process of CDUs.

METHODS

Study Design

The Cooper University Hospital institutional review board approved this study. The study took place at Cooper University Hospital, a large, urban, academic safety‐net hospital providing tertiary care located in Camden, New Jersey.

We performed a retrospective observational study of all adult observation encounters at the study hospital from July 2010 to January 2011, and July 2011 through January 2012. During the second time period, patients could have been managed in the CDU or on a medicalsurgical unit. We recorded the following demographic data: age, gender, race, principal diagnosis, and payer, as well as several outcomes of interest, including: LOS (defined as the time separating the admitting physician order from discharge), ED visits within 30 days of discharge, and hospital revisits (observation or inpatient) within 30 days.

Data Sources

Data were culled by the institution's performance improvement department from the electronic medical record, as well as cost accounting and claims‐based sources.

Clinical Decision Unit

The CDU at Cooper University Hospital opened in June 2011 and is a 20‐bed geographically distinct unit adjacent to the ED. During the study period, it was staffed 24 hours a day by a hospitalist and a nurse practitioner as well as dedicated nurses and critical care technicians. Patients meeting observation status in the ED were eligible for the CDU provided that they fulfilled the CDU placement guidelines including that they were more likely than not to be discharged within a period of 24 hours of CDU care, did not meet inpatient admission criteria, did not require new placement in a rehabilitation or extended‐care facility, and did not require one‐on‐one monitoring. Additional exclusion criteria included severe vital sign or laboratory abnormalities. The overall strategy of the guidelines was to facilitate a pull culture, where the majority of observation patients were brought from the ED to the CDU once it was determined that they did not require inpatient care. The CDU had order sets and protocols in place for many of the common diagnoses. All CDU patients received priority laboratory and radiologic testing as well as priority consultation from specialty services. Medication reconciliation was performed by a pharmacy technician for higher‐risk patients, identified by Project BOOST (Better Outcomes by Optimizing Safe Transitions) criteria.[4] Structured multidisciplinary rounds occurred daily including the hospitalist, nurse practitioner, registered nurses, case manager, and pharmacy technician. A discharge planner was available to schedule follow‐up appointments.

Although chest pain was the most common CDU diagnosis, the CDU was designed to care for the majority of the hospital's observation patients rather than focus specifically on chest pain. Patients with chest pain who met observation criteria were transferred from the ED to the CDU, rather than a medicalsurgical unit, provided they did not have: positive cardiac enzymes, an electrocardiogram indicative of ischemia, known coronary artery disease presenting with pain consistent with acute coronary syndrome, need for heparin or nitroglycerin continuous infusion, symptomatic or unresolved arrhythmia, congestive heart failure meeting inpatient criteria, hypertensive urgency or emergency, pacemaker malfunction, pericarditis, or toxicity from cardiac drugs. Cardiologist consultants were involved in the care of nearly all CDU patients with chest pain.

Observation Status Determination

During the study period, observation status was recommended by a case manager in the ED based on Milliman (Milliman Care Guidelines) or McKesson InterQual (McKesson Corporation) criteria, once it was determined by the ED physician that the patient had failed usual ED care and required hospitalization. Observation status was assigned by the admitting (non‐ED) physician, who placed the order for inpatient admission or observation. Other than the implementation of the CDU, there were no significant changes to the process or criteria for assigning observation status, admission order sets, or the hospital's electronic medical record during this time period.

Statistical Analysis

Continuous data are presented as mean ( standard deviation [SD]) or median (25%75% interquartile range) as specified, and differences were assessed using one‐way analysis of variance testing and Mann‐Whitney U testing. Categorical data are presented as count (percentage) and differences evaluated using [2] analysis. P values of 0.05 or less were considered statistically significant.

To account for differences in groups with regard to outcomes, we performed a multivariate regression analysis. The following variables were entered: age (years), gender, race (African American vs other), admission diagnosis (chest pain vs other), and insurance status (Medicare vs other). All variables were entered simultaneously without forcing. Statistical analyses were done using the SPSS 20.0 Software (SPSS Inc., Chicago, IL).

RESULTS

Demographics

There were a total of 3735 patients included in the study: 1650 in the pre‐CDU group, 1469 in the post‐CDU group, and 616 in the post‐CDU group on medicalsurgical units. The post‐CDU period had a total of 2085 patients. Patients in the CDU group were younger and were more likely to have chest pain as the admission diagnosis. Patient demographics are presented in Table 1.

Patient Demographics by Group
Variable Pre‐CDU, n=1,650 Post‐CDU, n=1,469 PostNon‐CDU, n=616 P, CDU vs Pre‐CDU P, Non‐CDU vs Pre‐CDU P, CDU vs Non‐CDU
  • NOTE: Abbreviations: CDU, clinical decision unit.

Age, y [range] 56 [4569] 53 [4364] 57 [44.370] <0.001 0.751 0.001
Female gender 918 (55.6%) 833(56.7%) 328 (53.2%) 0.563 0.319 0.148
African American race 574 (34.8%) 505 (34.4%) 174 (28.2%) 0.821 0.004 0.007
Admission diagnosis
Chest pain 462 (38%) 528 (35.9%) 132 (21.4%) <0.001 0.002 <0.001
Syncope 93 (5.6%) 56 (3.8%) 15 (2.4%) 0.018 0.001 0.145
Abdominal pain 46 (2.8%) 49 (3.3%) 20(3.2%) 0.404 0.575 1.0
Other 1,049 (63.6%) 836 (56.9%) 449 (72.9%) <0.001 <0.001 <0.001
Third‐party payer
Medicare 727 (44.1%) 491 (33.4%) 264(43.4%) <0.001 0.634 <0.001
Charity care 187 (11.3%) 238 (16.2%) 73 (11.9%) <0.001 0.767 0.010
Commercial 185 (11.1%) 214 (14.6%) 87 (14.1%) 0.005 0.059 0.838
Medicaid 292 (17.7%) 280 (19.1%) 100 (16.2%) 0.331 0.454 0.136
Other 153 (9.3%) 195 (13.3%) 60 (9.9%) <0.001 0.746 0.028
Self‐pay 106 (6.4%) 51(3.5%) 32 (5.2%) <0.001 0.323 0.085

Outcomes of Interest

There was a statistically significant association between LOS and CDU implementation (Table 2). Observation patients cared for in the CDU had a lower LOS than observation patients cared for on the medicalsurgical units during the same time period (17.6 vs 26.1 hours, P<0.0001).

Revisit Rates and Length of Stay Pre‐ and Post‐CDU Implementation
Outcome Pre‐CDU, n=1,650 Post‐CDU, n=1,469 PostNon‐CDU, n=616 P, CDU vs Pre‐CDU P, Non‐CDU vs Pre‐CDU P, CDU vs Non‐CDU
  • NOTE: Abbreviations: CDU, clinical decision unit; ED, emergency department; LOS, length of stay.

All patients, n=3,735
30‐day ED or hospital revisit 326 (19.8%) 268 (18.2%) 123 (17.2%) 0.294 0.906 0.357
Median LOS, h 27.1 [17.446.4] 17.6 [12.122.8] 26.1 [16.941.2] <0.001 0.004 <0.001
Chest‐pain patients, n=1,122
30‐day ED or hospital revisit 69 (14.9%) 82 (15.5%) 23 (17.4%) 0.859 0.496 0.596
Median LOS, h 22 [15.838.9] 17.3 [10.922.4] 23.2 [13.843.1] <0.001 0.995 <0.001
Other diagnoses, n=2,613
30‐day ED or hospital revisit 257 (21.6%) 186 (19.8%) 100 (18.4%) 0.307 0.693 0.727
Median LOS, h 30.4 [18.649.4] 17.8 [12.923] 26.7 [17.231.1] <0.001 <0.001 <0.001

In total, there were 717 total revisits including ED visits and hospital stays within 30 days of discharge (Table 2). Of all the observation encounters in the study, 19.2% were followed by a revisit within 30 days. There were no differences in the 30‐day post‐ED visit rates in between periods and between groups.

Mean ED LOS for hospitalized patients was examined for a sample of the pre‐ and post‐CDU periods, namely November 2010 to January 2011 and November 2011 to January 2012. The mean ED LOS decreased from 410 minutes (SD=61) to 393 minutes (SD=51) after implementation of the CDU (P=0.037).

To account for possible skewing of the data, we transformed LOS into ln (natural log) LOS and found the following means (SD): group 1 was 3.27 (0.94), group 2 was 2.78 (0.6), and group 3 was 3.1 (0.93). Using an independent t test, we found a significant difference between groups 1 and 2, 2 and 3, as well as 1 and 3 (P<0.001 for all).

Chest‐Pain Subgroup Analysis

We analyzed the data specifically for the 1122 patients discharged with a diagnosis of chest pain. LOS was significantly lower for patients in the CDU compared to either pre‐CDU or observation on floors (Table 2).

Multivariate Regression Analysis

We performed a linear regression analysis using the following variables: age, race, gender, diagnosis, insurance status, and study period (pre‐CDU, post‐CDU, and postnon‐CDU). We performed 3 different comparisons: pre‐CDU vs post‐CDU, postnon‐CDU vs post‐CDU, and postnon‐CDU vs pre‐CDU. After adjusting for other variables, the postnon‐CDU group was significantly associated with higher LOS (P<0.001). The pre‐CDU group was associated with higher LOS than both the post‐CDU and postnon‐CDU groups (P<0.001 for both).

DISCUSSION

In our study of a hospitalist‐run CDU for observation patients, we observed that the care in the CDU was associated with a lower median LOS, but no increase in ED or hospital revisits within 30 days.

Previous studies have reported the impact of clinical observation or clinical diagnosis units, particularly chest‐pain units.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] Studies of hospitalist‐run units suggest shorter LOS in the entire hospital,[16] or in the target unit.[17] Although one study suggested a lower 30‐day readmission rate,[18] most others did not describe this effect.[16, 17] Our study differs from previous research in that our program employed a pull‐culture aimed at accepting the majority of observation status patients without focusing on a particular diagnosis. We also implemented a structured multidisciplinary team focused on expediting care and utilized BOOST‐framed transitions, including targeted medication reconciliation and tools such as teach‐back.

The CDU in our hospital produced shorter LOS even compared to our non‐CDU units, but the revisit rate did not improve despite activities to reduce revisits. During the study period, efforts to decrease readmissions were implemented in various areas of our hospital, but not a comprehensive institution‐wide readmissions strategy. Lack of impact on revisits could be viewed as a positive finding, in that shorter LOS did not result in patients being discharged home before clinically stable. Alternatively, lack of impact could be due to the uncertain effectiveness of BOOST specifically[19, 20, 21] or inpatient‐targeted transitions interventions more generally.[22]

Our study has certain limitations. Findings in our single‐center study in an urban academic medical center may not apply to CDUs in other settings. As a prepost design, our study is subject to external trends for which our analyses may be unable to account. For example, during CDU implementation, there were hospital‐wide initiatives aimed at improving inpatient LOS, including complex case rounds, increased use of active bed management, and improved case management efforts to decrease LOS. These may have been a factor in the small decrease in observation LOS seen in the medicalsurgical patients during the post period. Additionally, though we have attempted to control for possible confounders, there could have been differences in the study groups for which we were unable to account, including code status or social variables such as homelessness, which played a role in our revisit outcomes. The decrease in LOS by 35%, or 9.5 hours, in CDU patients is clinically important, as it allows low‐risk patients to spend less time in the hospital where they may have been at risk of hospital‐acquired conditions; however, this study did not include patient satisfaction data. It would be important to measure the effect on patient experience of potentially spending 1 fewer night in the hospital. Finally, our CDU was designed with specific clinical criteria for inclusion and exclusion. Patients who were higher risk or expected to need more than 24 hours of care were not placed in the CDU. We were not able to adjust our analyses for factors that were not in our data, such as severe vital sign or laboratory abnormalities or a physician's clinical impression of a patient. It is possible, therefore, that referral bias may have occurred and influenced our results. The fact that non‐CDU chest‐pain patients in the post‐CDU period did not experience any decrease in LOS, whereas other medicalsurgical observation patients did, may be an example of this bias. Patients were excluded from the CDU by virtue of being deemed higher risk as described in Methods section. We were unable to adjust for these differences.

Implementation of CDUs may be useful for health systems seeking to improve hospital throughput and improve utilization among common but low‐acuity patient groups. Although our initial results are promising, the concept of a CDU may require enhancements. For example, at our hospital we are addressing transitions of care by looking at models that address patient risk through a systematic process, and then target individuals for specific interventions to prevent revisits. Moreover, the study of CDUs should report impact on patient and referring physician satisfaction, and whether CDUs can reduce per‐case costs.

CONCLUSION

Caring for patients in a hospitalist‐run geographic CDU was associated with a 35% decrease in observation LOS for CDU patients compared with a 3.7% decrease for observation patients cared for elsewhere in the hospital. CDU patients' LOS was significantly decreased without increasing ED or hospital revisit rates.

Acknowledgments

The authors would like to thank Ken Travis for excellent data support.

References
  1. The observation unit: an operational overview for the hospitalist. Society of Hospital Medicine website. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=White_Papers18(12):13711379.
  2. Venkatesh AK, Geisler BP, Gibson Chambers JJ, Baugh CW, Bohan JS, Schuur JD. Use of observation care in US emergency departments, 2001 to 2008. PLoS One. 2011;6(9):e24326.
  3. The Society of Hospital Medicine Project Boost (Better Outcomes by Optimizing Safe Transitions) Available at: http://www.hospitalmedicine.org/boost. Accessed on June 4, 2013.
  4. Gomez MA, Anderson JL, Karagounis LA, Muhlestein JB, Mooers FB. An emergency department‐based protocol for rapidly ruling out myocardial ischemia reduces hospital time and expense: results of a randomized study (ROMIO). J Am Coll Cardiol. 1996;28(1):2533.
  5. Siebens K, Miljoen H, Fieuws S, Drew B, De Geest S, Vrints C. Implementation of the guidelines for the management of patients with chest pain through a critical pathway approach improves length of stay and patient satisfaction but not anxiety. Crit Pathw Cardiol. 2010;9(1):3034.
  6. Roberts RR, Zalenski RJ, Mensah EK, et al. Costs of an emergency department‐based accelerated diagnostic protocol vs hospitalization in patients with chest pain: a randomized controlled trial. JAMA. 1997;278(20):16701676.
  7. Hoekstra JW, Gibler WB, Levy RC, et al. Emergency‐department diagnosis of acute myocardial infarction and ischemia: a cost analysis of two diagnostic protocols. Acad Emerg Med. 1994;1(2):103110.
  8. Graff LG, Dallara J, Ross MA, et al. Impact on the care of the emergency department chest pain patient from the chest pain evaluation registry (CHEPER) study. Am J Cardiol. 1997;80(5):563568.
  9. Gaspoz JM, Lee TH, Weinstein MC, et al. Cost‐effectiveness of a new short‐stay unit to “rule out” acute myocardial infarction in low risk patients. J Am Coll Cardiol. 1994;24(5):12491259.
  10. Rydman RJ, Isola ML, Roberts RR, et al. Emergency Department Observation Unit versus hospital inpatient care for a chronic asthmatic population: a randomized trial of health status outcome and cost. Med Care. 1998;36(4):599609.
  11. McDermott MF, Murphy DG, Zalenski RJ, et al. A comparison between emergency diagnostic and treatment unit and inpatient care in the management of acute asthma. Arch Intern Med. 1997;157(18):20552062.
  12. Tham KY, Kimura H, Nagurney T, Volinsky F. Retrospective review of emergency department patients with non‐variceal upper gastrointestinal hemorrhage for potential outpatient management. Acad Emerg Med. 1999;6(3):196201.
  13. Longstreth GF, Feitelberg SP. Outpatient care of selected patients with acute non‐variceal upper gastrointestinal haemorrhage. Lancet. 1995;345(8942):108111.
  14. Hostetler B, Leikin JB, Timmons JA, Hanashiro PK, Kissane K. Patterns of use of an emergency department‐based observation unit. Am J Ther. 2002;9(6):499502.
  15. Leykum LK, Huerta V, Mortensen E. Implementation of a hospitalist‐run observation unit and impact on length of stay (LOS): a brief report. J Hosp Med. 2010;5(9):E2E5.
  16. Myers JS, Bellini LM, Rohrbach J, Shofer FS, Hollander JE. Improving resource utilization in a teaching hospital: development of a nonteaching service for chest pain admissions. Acad Med. 2006;81(5):432435.
  17. Abenhaim HA, Kahn SR, Raffoul J, Becker MR. Program description: a hospitalist‐run, medical short‐stay unit in a teaching hospital. CMAJ. 2000;163(11):14771480.
  18. Hansen L, Greenwald JL, Budnitz T, et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8:421427.
  19. Auerbach A, Fang M, Glasheen J, et al. BOOST: evidence needing a lift. J Hosp Med. 2013;8:468469.
  20. Jha AK. BOOST and readmissions: thinking beyond the walls of the hospital. J Hosp Med. 2013;8:470471.
  21. Rennke S, Nguyen OK, Shoeb MH, et al. Hospital‐initiated transitional care interventions as a patient safety strategy. Ann Int Med. 2013;158:433440.
References
  1. The observation unit: an operational overview for the hospitalist. Society of Hospital Medicine website. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=White_Papers18(12):13711379.
  2. Venkatesh AK, Geisler BP, Gibson Chambers JJ, Baugh CW, Bohan JS, Schuur JD. Use of observation care in US emergency departments, 2001 to 2008. PLoS One. 2011;6(9):e24326.
  3. The Society of Hospital Medicine Project Boost (Better Outcomes by Optimizing Safe Transitions) Available at: http://www.hospitalmedicine.org/boost. Accessed on June 4, 2013.
  4. Gomez MA, Anderson JL, Karagounis LA, Muhlestein JB, Mooers FB. An emergency department‐based protocol for rapidly ruling out myocardial ischemia reduces hospital time and expense: results of a randomized study (ROMIO). J Am Coll Cardiol. 1996;28(1):2533.
  5. Siebens K, Miljoen H, Fieuws S, Drew B, De Geest S, Vrints C. Implementation of the guidelines for the management of patients with chest pain through a critical pathway approach improves length of stay and patient satisfaction but not anxiety. Crit Pathw Cardiol. 2010;9(1):3034.
  6. Roberts RR, Zalenski RJ, Mensah EK, et al. Costs of an emergency department‐based accelerated diagnostic protocol vs hospitalization in patients with chest pain: a randomized controlled trial. JAMA. 1997;278(20):16701676.
  7. Hoekstra JW, Gibler WB, Levy RC, et al. Emergency‐department diagnosis of acute myocardial infarction and ischemia: a cost analysis of two diagnostic protocols. Acad Emerg Med. 1994;1(2):103110.
  8. Graff LG, Dallara J, Ross MA, et al. Impact on the care of the emergency department chest pain patient from the chest pain evaluation registry (CHEPER) study. Am J Cardiol. 1997;80(5):563568.
  9. Gaspoz JM, Lee TH, Weinstein MC, et al. Cost‐effectiveness of a new short‐stay unit to “rule out” acute myocardial infarction in low risk patients. J Am Coll Cardiol. 1994;24(5):12491259.
  10. Rydman RJ, Isola ML, Roberts RR, et al. Emergency Department Observation Unit versus hospital inpatient care for a chronic asthmatic population: a randomized trial of health status outcome and cost. Med Care. 1998;36(4):599609.
  11. McDermott MF, Murphy DG, Zalenski RJ, et al. A comparison between emergency diagnostic and treatment unit and inpatient care in the management of acute asthma. Arch Intern Med. 1997;157(18):20552062.
  12. Tham KY, Kimura H, Nagurney T, Volinsky F. Retrospective review of emergency department patients with non‐variceal upper gastrointestinal hemorrhage for potential outpatient management. Acad Emerg Med. 1999;6(3):196201.
  13. Longstreth GF, Feitelberg SP. Outpatient care of selected patients with acute non‐variceal upper gastrointestinal haemorrhage. Lancet. 1995;345(8942):108111.
  14. Hostetler B, Leikin JB, Timmons JA, Hanashiro PK, Kissane K. Patterns of use of an emergency department‐based observation unit. Am J Ther. 2002;9(6):499502.
  15. Leykum LK, Huerta V, Mortensen E. Implementation of a hospitalist‐run observation unit and impact on length of stay (LOS): a brief report. J Hosp Med. 2010;5(9):E2E5.
  16. Myers JS, Bellini LM, Rohrbach J, Shofer FS, Hollander JE. Improving resource utilization in a teaching hospital: development of a nonteaching service for chest pain admissions. Acad Med. 2006;81(5):432435.
  17. Abenhaim HA, Kahn SR, Raffoul J, Becker MR. Program description: a hospitalist‐run, medical short‐stay unit in a teaching hospital. CMAJ. 2000;163(11):14771480.
  18. Hansen L, Greenwald JL, Budnitz T, et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8:421427.
  19. Auerbach A, Fang M, Glasheen J, et al. BOOST: evidence needing a lift. J Hosp Med. 2013;8:468469.
  20. Jha AK. BOOST and readmissions: thinking beyond the walls of the hospital. J Hosp Med. 2013;8:470471.
  21. Rennke S, Nguyen OK, Shoeb MH, et al. Hospital‐initiated transitional care interventions as a patient safety strategy. Ann Int Med. 2013;158:433440.
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Address for correspondence and reprint requests: Kara S. Aplin, MD, Assistant Professor of Medicine, Cooper Medical School of Rowan University, Dorrance Building, Suite 222, One Cooper Plaza, Camden, NJ 08103; Telephone: 856‐342‐3150; Fax: 856‐968‐8418; E‐mail: [email protected]
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CDC Report Calls for Hospitalists to Focus on Antibiotic Stewardship

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CDC Report Calls for Hospitalists to Focus on Antibiotic Stewardship

A Centers for Disease Control and Prevention (CDC) report this month on antibiotic stewardship highlights the need for continued attention and improvement around the topic, says a hospitalist who has studied the issue.

The CDC announcement, "Antibiotic Rx in Hospitals: Proceed with Caution," circulated in its monthly report, CDC Vital Signs, urged hospital leaders to adopt at least a basic stewardship program and "work with other healthcare facilities to prevent infections, transmission, and resistance."

David Rosenberg, MD, MPH, FACP, SFHM, chief of the division of hospital medicine at North Shore University Hospital's department of medicine in Manhasset, N.Y., says the alert can serve as a spotlight.

"While we all agree that this is an important topic, there's a certain amount of inertia around it," Dr. Rosenberg says. "When the CDC comes out with statements like this, it really helps drive this forward. It really should be viewed as a call to action."

The CDC alert highlights the variability of antibiotic use. It notes that doctors in some hospitals prescribed three times as many antibiotics as doctors at others. The disparity in treatment standards makes stewardship a broad issue to tackle, Dr. Rosenberg says.

"It's not a simple fix," he adds. "You have to do it one piece at a time. How are you going to manage urinary-tract infections? How are you going to manage pneumonias? How are you going to manage bloodstream infections? We want ultimately to integrate the approach into the day-to-day practice of hospitalists, but there's a lot of data you need in a very organized format to inform those decisions. Stewardship programs organize the information in a way that can influence and change practice."

Visit our website for more information on antibiotic stewardship.


 

 

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A Centers for Disease Control and Prevention (CDC) report this month on antibiotic stewardship highlights the need for continued attention and improvement around the topic, says a hospitalist who has studied the issue.

The CDC announcement, "Antibiotic Rx in Hospitals: Proceed with Caution," circulated in its monthly report, CDC Vital Signs, urged hospital leaders to adopt at least a basic stewardship program and "work with other healthcare facilities to prevent infections, transmission, and resistance."

David Rosenberg, MD, MPH, FACP, SFHM, chief of the division of hospital medicine at North Shore University Hospital's department of medicine in Manhasset, N.Y., says the alert can serve as a spotlight.

"While we all agree that this is an important topic, there's a certain amount of inertia around it," Dr. Rosenberg says. "When the CDC comes out with statements like this, it really helps drive this forward. It really should be viewed as a call to action."

The CDC alert highlights the variability of antibiotic use. It notes that doctors in some hospitals prescribed three times as many antibiotics as doctors at others. The disparity in treatment standards makes stewardship a broad issue to tackle, Dr. Rosenberg says.

"It's not a simple fix," he adds. "You have to do it one piece at a time. How are you going to manage urinary-tract infections? How are you going to manage pneumonias? How are you going to manage bloodstream infections? We want ultimately to integrate the approach into the day-to-day practice of hospitalists, but there's a lot of data you need in a very organized format to inform those decisions. Stewardship programs organize the information in a way that can influence and change practice."

Visit our website for more information on antibiotic stewardship.


 

 

A Centers for Disease Control and Prevention (CDC) report this month on antibiotic stewardship highlights the need for continued attention and improvement around the topic, says a hospitalist who has studied the issue.

The CDC announcement, "Antibiotic Rx in Hospitals: Proceed with Caution," circulated in its monthly report, CDC Vital Signs, urged hospital leaders to adopt at least a basic stewardship program and "work with other healthcare facilities to prevent infections, transmission, and resistance."

David Rosenberg, MD, MPH, FACP, SFHM, chief of the division of hospital medicine at North Shore University Hospital's department of medicine in Manhasset, N.Y., says the alert can serve as a spotlight.

"While we all agree that this is an important topic, there's a certain amount of inertia around it," Dr. Rosenberg says. "When the CDC comes out with statements like this, it really helps drive this forward. It really should be viewed as a call to action."

The CDC alert highlights the variability of antibiotic use. It notes that doctors in some hospitals prescribed three times as many antibiotics as doctors at others. The disparity in treatment standards makes stewardship a broad issue to tackle, Dr. Rosenberg says.

"It's not a simple fix," he adds. "You have to do it one piece at a time. How are you going to manage urinary-tract infections? How are you going to manage pneumonias? How are you going to manage bloodstream infections? We want ultimately to integrate the approach into the day-to-day practice of hospitalists, but there's a lot of data you need in a very organized format to inform those decisions. Stewardship programs organize the information in a way that can influence and change practice."

Visit our website for more information on antibiotic stewardship.


 

 

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ICU Pressure Improves Patient Transfers to the Hospital Floor

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Clinical question: Does ICU strain negatively affect the outcomes of patients transferred to the floor?

Background: With healthcare costs increasing and critical care staff shortages projected, ICUs will have to operate under increasing strain. This may influence decisions on discharging patients from ICUs and could affect patient outcomes.

Study design: Retrospective cohort study.

Setting: One hundred fifty-five ICUs in the United States.

Synopsis: Using the Project IMPACT database, 200,730 adult patients from 107 different hospitals were evaluated in times of ICU strain, determined by the current census, new admissions, and acuity level. Outcomes measured were initial ICU length of stay (LOS), readmission within 72 hours, in-hospital mortality rates, and post-ICU discharge LOS.

Increases of the strain variables from the fifth to the 95th percentiles resulted in a 6.3-hour reduction in ICU LOS, a 2.0-hour decrease in post-ICU discharge LOS, and a 1.0% increase in probability of ICU readmission within 72 hours. Mortality rates during the hospital stay and odds of being discharged home showed no significant change. This study was limited because the ICUs participating were not randomly chosen, outcomes of patients transferred to other hospitals were not measured, and no post-hospital data was collected, so no long-term outcomes could be measured.

Bottom line: ICU bed pressures prompt physicians to allocate ICU resources more efficiently without changing short-term patient outcomes.

Citation: Wagner J, Gabler NB, Ratcliffe SJ, Brown SE, Strom BL, Halpern SD. Outcomes among patients discharged from busy intensive care units. Ann Intern Med. 2013;159(7):447-455.

 

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Clinical question: Does ICU strain negatively affect the outcomes of patients transferred to the floor?

Background: With healthcare costs increasing and critical care staff shortages projected, ICUs will have to operate under increasing strain. This may influence decisions on discharging patients from ICUs and could affect patient outcomes.

Study design: Retrospective cohort study.

Setting: One hundred fifty-five ICUs in the United States.

Synopsis: Using the Project IMPACT database, 200,730 adult patients from 107 different hospitals were evaluated in times of ICU strain, determined by the current census, new admissions, and acuity level. Outcomes measured were initial ICU length of stay (LOS), readmission within 72 hours, in-hospital mortality rates, and post-ICU discharge LOS.

Increases of the strain variables from the fifth to the 95th percentiles resulted in a 6.3-hour reduction in ICU LOS, a 2.0-hour decrease in post-ICU discharge LOS, and a 1.0% increase in probability of ICU readmission within 72 hours. Mortality rates during the hospital stay and odds of being discharged home showed no significant change. This study was limited because the ICUs participating were not randomly chosen, outcomes of patients transferred to other hospitals were not measured, and no post-hospital data was collected, so no long-term outcomes could be measured.

Bottom line: ICU bed pressures prompt physicians to allocate ICU resources more efficiently without changing short-term patient outcomes.

Citation: Wagner J, Gabler NB, Ratcliffe SJ, Brown SE, Strom BL, Halpern SD. Outcomes among patients discharged from busy intensive care units. Ann Intern Med. 2013;159(7):447-455.

 

Clinical question: Does ICU strain negatively affect the outcomes of patients transferred to the floor?

Background: With healthcare costs increasing and critical care staff shortages projected, ICUs will have to operate under increasing strain. This may influence decisions on discharging patients from ICUs and could affect patient outcomes.

Study design: Retrospective cohort study.

Setting: One hundred fifty-five ICUs in the United States.

Synopsis: Using the Project IMPACT database, 200,730 adult patients from 107 different hospitals were evaluated in times of ICU strain, determined by the current census, new admissions, and acuity level. Outcomes measured were initial ICU length of stay (LOS), readmission within 72 hours, in-hospital mortality rates, and post-ICU discharge LOS.

Increases of the strain variables from the fifth to the 95th percentiles resulted in a 6.3-hour reduction in ICU LOS, a 2.0-hour decrease in post-ICU discharge LOS, and a 1.0% increase in probability of ICU readmission within 72 hours. Mortality rates during the hospital stay and odds of being discharged home showed no significant change. This study was limited because the ICUs participating were not randomly chosen, outcomes of patients transferred to other hospitals were not measured, and no post-hospital data was collected, so no long-term outcomes could be measured.

Bottom line: ICU bed pressures prompt physicians to allocate ICU resources more efficiently without changing short-term patient outcomes.

Citation: Wagner J, Gabler NB, Ratcliffe SJ, Brown SE, Strom BL, Halpern SD. Outcomes among patients discharged from busy intensive care units. Ann Intern Med. 2013;159(7):447-455.

 

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Can coffee reduce weight?

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Caffeine in the form of tea and coffee is the most widely consumed, socially acceptable stimulant around the globe. More than 150 million people in the United States drink coffee daily, with an average intake of 2 cups (which contains about 280 mg of caffeine).

Caffeine results in the release of excitatory neurotransmitters. Caffeine may increase energy expenditure and has been associated with reduced body mass. Studies have observed lower body mass index (BMI) in coffee consumers, compared with individuals who don’t consume coffee. Coffee may reduce appetite and dietary intake.

Greek researchers at Harokopio University, Athens, conducted a cross-over study to evaluate the effects of caffeinated coffee on appetite and dietary intake (Obesity 2013;21:1127-32). Sixteen normal-weight and 17 overweight/obese habitual coffee consumers (at least 1 cup of coffee/day) were enrolled. Each participant took part in three trials at least 1 week apart. Participants were required to abstain from caffeine for 24 hours and then reported to the lab to consume a breakfast and 200 mL of one of three experimental beverages: instant coffee with 3 mg caffeine/kg body weight (Coffee 3); instant coffee with 6 mg caffeine/kg (Coffee 6); or water. Participants had to consume the breakfast and the beverage within 5 minutes.

During a 3-hour period following beverage consumption, appetite feelings and participants’ dietary intake the day before the experiment were assessed. After this 3-hour period, participants were offered an ad libitum lunch buffet. The following day, participants reported by telephone their food and fluid intake for the rest of the experiment day.

Normal-weight participants consumed comparable energy in the ad libitum meal and in their total daily intake in the three interventions. However, among overweight/obese individuals, Coffee 6 resulted in significantly reduced energy intake during the ad libitum meal, compared with Coffee 3, and in significantly reduced total day energy intake, compared with both water and Coffee 3.

Doses used in this study for participants were somewhat staggering. The average caffeine content of the beverage in the Coffee 6 group was 526 mg. This is the caffeine content of roughly four 8-ounce cups of brewed coffee. The authors acknowledged that the Coffee 6 beverage was not easily consumed by "most of the volunteers."

We need to be cautious about the use of this dosing in the clinical setting. But as part of comprehensive weight-management strategy, caffeinated coffee may be helpful for reducing energy intake.

Dr. Ebbert is professor of medicine, a general internist at the Mayo Clinic in Rochester, Minn., and a diplomate of the American Board of Addiction Medicine. The opinions expressed are those of the author. He reports no conflicts of interest.

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Caffeine in the form of tea and coffee is the most widely consumed, socially acceptable stimulant around the globe. More than 150 million people in the United States drink coffee daily, with an average intake of 2 cups (which contains about 280 mg of caffeine).

Caffeine results in the release of excitatory neurotransmitters. Caffeine may increase energy expenditure and has been associated with reduced body mass. Studies have observed lower body mass index (BMI) in coffee consumers, compared with individuals who don’t consume coffee. Coffee may reduce appetite and dietary intake.

Greek researchers at Harokopio University, Athens, conducted a cross-over study to evaluate the effects of caffeinated coffee on appetite and dietary intake (Obesity 2013;21:1127-32). Sixteen normal-weight and 17 overweight/obese habitual coffee consumers (at least 1 cup of coffee/day) were enrolled. Each participant took part in three trials at least 1 week apart. Participants were required to abstain from caffeine for 24 hours and then reported to the lab to consume a breakfast and 200 mL of one of three experimental beverages: instant coffee with 3 mg caffeine/kg body weight (Coffee 3); instant coffee with 6 mg caffeine/kg (Coffee 6); or water. Participants had to consume the breakfast and the beverage within 5 minutes.

During a 3-hour period following beverage consumption, appetite feelings and participants’ dietary intake the day before the experiment were assessed. After this 3-hour period, participants were offered an ad libitum lunch buffet. The following day, participants reported by telephone their food and fluid intake for the rest of the experiment day.

Normal-weight participants consumed comparable energy in the ad libitum meal and in their total daily intake in the three interventions. However, among overweight/obese individuals, Coffee 6 resulted in significantly reduced energy intake during the ad libitum meal, compared with Coffee 3, and in significantly reduced total day energy intake, compared with both water and Coffee 3.

Doses used in this study for participants were somewhat staggering. The average caffeine content of the beverage in the Coffee 6 group was 526 mg. This is the caffeine content of roughly four 8-ounce cups of brewed coffee. The authors acknowledged that the Coffee 6 beverage was not easily consumed by "most of the volunteers."

We need to be cautious about the use of this dosing in the clinical setting. But as part of comprehensive weight-management strategy, caffeinated coffee may be helpful for reducing energy intake.

Dr. Ebbert is professor of medicine, a general internist at the Mayo Clinic in Rochester, Minn., and a diplomate of the American Board of Addiction Medicine. The opinions expressed are those of the author. He reports no conflicts of interest.

Caffeine in the form of tea and coffee is the most widely consumed, socially acceptable stimulant around the globe. More than 150 million people in the United States drink coffee daily, with an average intake of 2 cups (which contains about 280 mg of caffeine).

Caffeine results in the release of excitatory neurotransmitters. Caffeine may increase energy expenditure and has been associated with reduced body mass. Studies have observed lower body mass index (BMI) in coffee consumers, compared with individuals who don’t consume coffee. Coffee may reduce appetite and dietary intake.

Greek researchers at Harokopio University, Athens, conducted a cross-over study to evaluate the effects of caffeinated coffee on appetite and dietary intake (Obesity 2013;21:1127-32). Sixteen normal-weight and 17 overweight/obese habitual coffee consumers (at least 1 cup of coffee/day) were enrolled. Each participant took part in three trials at least 1 week apart. Participants were required to abstain from caffeine for 24 hours and then reported to the lab to consume a breakfast and 200 mL of one of three experimental beverages: instant coffee with 3 mg caffeine/kg body weight (Coffee 3); instant coffee with 6 mg caffeine/kg (Coffee 6); or water. Participants had to consume the breakfast and the beverage within 5 minutes.

During a 3-hour period following beverage consumption, appetite feelings and participants’ dietary intake the day before the experiment were assessed. After this 3-hour period, participants were offered an ad libitum lunch buffet. The following day, participants reported by telephone their food and fluid intake for the rest of the experiment day.

Normal-weight participants consumed comparable energy in the ad libitum meal and in their total daily intake in the three interventions. However, among overweight/obese individuals, Coffee 6 resulted in significantly reduced energy intake during the ad libitum meal, compared with Coffee 3, and in significantly reduced total day energy intake, compared with both water and Coffee 3.

Doses used in this study for participants were somewhat staggering. The average caffeine content of the beverage in the Coffee 6 group was 526 mg. This is the caffeine content of roughly four 8-ounce cups of brewed coffee. The authors acknowledged that the Coffee 6 beverage was not easily consumed by "most of the volunteers."

We need to be cautious about the use of this dosing in the clinical setting. But as part of comprehensive weight-management strategy, caffeinated coffee may be helpful for reducing energy intake.

Dr. Ebbert is professor of medicine, a general internist at the Mayo Clinic in Rochester, Minn., and a diplomate of the American Board of Addiction Medicine. The opinions expressed are those of the author. He reports no conflicts of interest.

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Legislating kindness

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Late in February, I drove to Annapolis, Md., to listen to testimony being given to our state’s Senate Finance Committee on a number of proposed bills relating to involuntary treatments. But this article is not going to be about those bills or the fuss that played out around them, but rather about what happened when I arrived a bit early and heard testimony on another piece of legislation that had nothing to do with psychiatry and everything to do with being human.

Ten bills were scheduled for public testimony that afternoon, and I arrived shortly after 1 p.m. to a hearing room in which there was standing room only. There are 11 members of the Senate Finance Committee; 10 were present, and I’m going to guess there were more than100 people in the room waiting to testify. The only other time I had gone to listen to public testimony was last year for Gov. Martin O’Malley’s Firearm Safety Act, and for that piece of legislation, 1,300 people were there to testify, the large majority of them being Second Amendment activists who were there to oppose the bill.

The legislation in question (the one I did not come to hear about) was Senate Bill 654. The description of that bill reads:

"This bill requires the Department of Health and Mental Hygiene to identify up-to-date, evidence-based, written information about Down syndrome. This information must be provided to health care facilities and providers, who must provide the information to expectant parents who receive a prenatal test result for Down syndrome and parents of a child diagnosed with Down syndrome."

My first thought was this is a standard of care issue, something to be addressed by the specialties involved, not something that should be the subject of legislation. We don’t really want to have condition-specific laws mandating what physicians must tell their patients, do we? Where would it stop: Would legislation be written for every disorder? Shouldn’t the clinician who knows the family determine what information is best for each individual and with what timing? Never mind the nuance of figuring out exactly what is "up to date" and what is not. And it’s not as though the parents couldn’t search for the information themselves – certainly there must be resources online.

Then the testimony began. There were women whose children had been diagnosed, generally in utero, before the time when one could Google the condition and search for available resources. They told stories of asking for resources, only to be told that their clinicians didn’t know of any. And they told stories of being counseled to abort after they wanted to continue with the pregnancy. They discussed how shocked and alone they felt, and how insensitive the care they received was. In addition, they talked about the value their children added to the world. And then the adult children with Down syndrome also testified. In a way, I felt like it was testimony for the rights of these people to be here, more so perhaps than testimony to require clinicians to give out the appropriate pamphlets.

Representatives from MedChi (the Maryland State Medical Society) and the Department of Health and Mental Hygiene also testified. While neither was opposed to the creation of lists and websites of resources and educational information, they did object to the mandate that the clinician must give the information at a specific time. What if the clinician felt it was not in that patient’s best interest to distribute the information to a specific patient or at a specific time? Perhaps it wasn’t there, but I heard the unspoken concern that the clinician might be subject to sanctions or accusations of malpractice if they neglected to distribute the information as required by law if the legislation passed. The Down syndrome information supporters said they had already gone office to office to provide pamphlets and information to clinicians, which were then placed in a drawer and not distributed. They objected to language that would change "must" to "may" with the idea that it wasn’t strong enough to move clinicians to action.

Another bill also caught my attention this year, although I was not present for any public testimony. Delegate Robert A. Costa proposed House Bill 279, legislation that would forbid a physician from charging or filing an insurance claim for any appointment that began more than 30 minutes from the scheduled time. The legislation did allow for an exclusion for emergencies, if the doctor presented an "emergency services verification number" to the patient, but it did not take into account that an appointment might start late because the patient was late!

 

 

Regardless of whether the legislation proposed by these two bills makes sense as something for lawmakers to address, and regardless of how practical they are in terms of both their intended and unintended consequences, the message of such legislation is clear: They represent distress at insensitive care and an attempt to legislate kindness. The message comes through that clinicians can’t be trusted to do what’s right, and laws must be passed to make sure they practice in a thoughtful and considerate manner. It would be nice to have some reasonable conclusion about how to rectify these situations without having legislators tell medical professionals how to practice. I don’t have an answer here, but I believe that passing more laws will create its own negative fallout, and we need to find a better route to kindness.

Dr. Miller is a coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: the Johns Hopkins University Press, 2011).

[email protected]

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Late in February, I drove to Annapolis, Md., to listen to testimony being given to our state’s Senate Finance Committee on a number of proposed bills relating to involuntary treatments. But this article is not going to be about those bills or the fuss that played out around them, but rather about what happened when I arrived a bit early and heard testimony on another piece of legislation that had nothing to do with psychiatry and everything to do with being human.

Ten bills were scheduled for public testimony that afternoon, and I arrived shortly after 1 p.m. to a hearing room in which there was standing room only. There are 11 members of the Senate Finance Committee; 10 were present, and I’m going to guess there were more than100 people in the room waiting to testify. The only other time I had gone to listen to public testimony was last year for Gov. Martin O’Malley’s Firearm Safety Act, and for that piece of legislation, 1,300 people were there to testify, the large majority of them being Second Amendment activists who were there to oppose the bill.

The legislation in question (the one I did not come to hear about) was Senate Bill 654. The description of that bill reads:

"This bill requires the Department of Health and Mental Hygiene to identify up-to-date, evidence-based, written information about Down syndrome. This information must be provided to health care facilities and providers, who must provide the information to expectant parents who receive a prenatal test result for Down syndrome and parents of a child diagnosed with Down syndrome."

My first thought was this is a standard of care issue, something to be addressed by the specialties involved, not something that should be the subject of legislation. We don’t really want to have condition-specific laws mandating what physicians must tell their patients, do we? Where would it stop: Would legislation be written for every disorder? Shouldn’t the clinician who knows the family determine what information is best for each individual and with what timing? Never mind the nuance of figuring out exactly what is "up to date" and what is not. And it’s not as though the parents couldn’t search for the information themselves – certainly there must be resources online.

Then the testimony began. There were women whose children had been diagnosed, generally in utero, before the time when one could Google the condition and search for available resources. They told stories of asking for resources, only to be told that their clinicians didn’t know of any. And they told stories of being counseled to abort after they wanted to continue with the pregnancy. They discussed how shocked and alone they felt, and how insensitive the care they received was. In addition, they talked about the value their children added to the world. And then the adult children with Down syndrome also testified. In a way, I felt like it was testimony for the rights of these people to be here, more so perhaps than testimony to require clinicians to give out the appropriate pamphlets.

Representatives from MedChi (the Maryland State Medical Society) and the Department of Health and Mental Hygiene also testified. While neither was opposed to the creation of lists and websites of resources and educational information, they did object to the mandate that the clinician must give the information at a specific time. What if the clinician felt it was not in that patient’s best interest to distribute the information to a specific patient or at a specific time? Perhaps it wasn’t there, but I heard the unspoken concern that the clinician might be subject to sanctions or accusations of malpractice if they neglected to distribute the information as required by law if the legislation passed. The Down syndrome information supporters said they had already gone office to office to provide pamphlets and information to clinicians, which were then placed in a drawer and not distributed. They objected to language that would change "must" to "may" with the idea that it wasn’t strong enough to move clinicians to action.

Another bill also caught my attention this year, although I was not present for any public testimony. Delegate Robert A. Costa proposed House Bill 279, legislation that would forbid a physician from charging or filing an insurance claim for any appointment that began more than 30 minutes from the scheduled time. The legislation did allow for an exclusion for emergencies, if the doctor presented an "emergency services verification number" to the patient, but it did not take into account that an appointment might start late because the patient was late!

 

 

Regardless of whether the legislation proposed by these two bills makes sense as something for lawmakers to address, and regardless of how practical they are in terms of both their intended and unintended consequences, the message of such legislation is clear: They represent distress at insensitive care and an attempt to legislate kindness. The message comes through that clinicians can’t be trusted to do what’s right, and laws must be passed to make sure they practice in a thoughtful and considerate manner. It would be nice to have some reasonable conclusion about how to rectify these situations without having legislators tell medical professionals how to practice. I don’t have an answer here, but I believe that passing more laws will create its own negative fallout, and we need to find a better route to kindness.

Dr. Miller is a coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: the Johns Hopkins University Press, 2011).

[email protected]

Late in February, I drove to Annapolis, Md., to listen to testimony being given to our state’s Senate Finance Committee on a number of proposed bills relating to involuntary treatments. But this article is not going to be about those bills or the fuss that played out around them, but rather about what happened when I arrived a bit early and heard testimony on another piece of legislation that had nothing to do with psychiatry and everything to do with being human.

Ten bills were scheduled for public testimony that afternoon, and I arrived shortly after 1 p.m. to a hearing room in which there was standing room only. There are 11 members of the Senate Finance Committee; 10 were present, and I’m going to guess there were more than100 people in the room waiting to testify. The only other time I had gone to listen to public testimony was last year for Gov. Martin O’Malley’s Firearm Safety Act, and for that piece of legislation, 1,300 people were there to testify, the large majority of them being Second Amendment activists who were there to oppose the bill.

The legislation in question (the one I did not come to hear about) was Senate Bill 654. The description of that bill reads:

"This bill requires the Department of Health and Mental Hygiene to identify up-to-date, evidence-based, written information about Down syndrome. This information must be provided to health care facilities and providers, who must provide the information to expectant parents who receive a prenatal test result for Down syndrome and parents of a child diagnosed with Down syndrome."

My first thought was this is a standard of care issue, something to be addressed by the specialties involved, not something that should be the subject of legislation. We don’t really want to have condition-specific laws mandating what physicians must tell their patients, do we? Where would it stop: Would legislation be written for every disorder? Shouldn’t the clinician who knows the family determine what information is best for each individual and with what timing? Never mind the nuance of figuring out exactly what is "up to date" and what is not. And it’s not as though the parents couldn’t search for the information themselves – certainly there must be resources online.

Then the testimony began. There were women whose children had been diagnosed, generally in utero, before the time when one could Google the condition and search for available resources. They told stories of asking for resources, only to be told that their clinicians didn’t know of any. And they told stories of being counseled to abort after they wanted to continue with the pregnancy. They discussed how shocked and alone they felt, and how insensitive the care they received was. In addition, they talked about the value their children added to the world. And then the adult children with Down syndrome also testified. In a way, I felt like it was testimony for the rights of these people to be here, more so perhaps than testimony to require clinicians to give out the appropriate pamphlets.

Representatives from MedChi (the Maryland State Medical Society) and the Department of Health and Mental Hygiene also testified. While neither was opposed to the creation of lists and websites of resources and educational information, they did object to the mandate that the clinician must give the information at a specific time. What if the clinician felt it was not in that patient’s best interest to distribute the information to a specific patient or at a specific time? Perhaps it wasn’t there, but I heard the unspoken concern that the clinician might be subject to sanctions or accusations of malpractice if they neglected to distribute the information as required by law if the legislation passed. The Down syndrome information supporters said they had already gone office to office to provide pamphlets and information to clinicians, which were then placed in a drawer and not distributed. They objected to language that would change "must" to "may" with the idea that it wasn’t strong enough to move clinicians to action.

Another bill also caught my attention this year, although I was not present for any public testimony. Delegate Robert A. Costa proposed House Bill 279, legislation that would forbid a physician from charging or filing an insurance claim for any appointment that began more than 30 minutes from the scheduled time. The legislation did allow for an exclusion for emergencies, if the doctor presented an "emergency services verification number" to the patient, but it did not take into account that an appointment might start late because the patient was late!

 

 

Regardless of whether the legislation proposed by these two bills makes sense as something for lawmakers to address, and regardless of how practical they are in terms of both their intended and unintended consequences, the message of such legislation is clear: They represent distress at insensitive care and an attempt to legislate kindness. The message comes through that clinicians can’t be trusted to do what’s right, and laws must be passed to make sure they practice in a thoughtful and considerate manner. It would be nice to have some reasonable conclusion about how to rectify these situations without having legislators tell medical professionals how to practice. I don’t have an answer here, but I believe that passing more laws will create its own negative fallout, and we need to find a better route to kindness.

Dr. Miller is a coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: the Johns Hopkins University Press, 2011).

[email protected]

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IHS Takes Aim at HIV

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In 2010, American Indians and Alaska Natives (AIANs) accounted for < 1% of the estimated 47,500 new cases of human immunodeficiency virus (HIV) infection in the U.S. However, that proportion is misleading. When population size is taken into account for 2011, AIANs ranked fifth in rates of HIV/AIDS (acquired immunodeficiency syndrome) diagnoses. And the rate of AIDS diagnosis for this group has been higher than that for whites since 1995, according to the Centers for Disease Control and Prevention (CDC).

Also, AIANs diagnosed with HIV/AIDS die sooner: Between 2003 and 2007, only 81% lived longer than 36 months after being diagnosed. In 2010, HIV infection was the ninth leading cause of death among AIAN men and women aged 25 to 34 years.

Race and ethnicity are not, by themselves, risk factors for HIV infection, the CDC says. American Indian and Alaska Natives also have high rates of Chlamydia trachomatis infection, gonorrhea, and syphilis—sexually transmitted diseases are a warning signal of contracting or spreading HIV. Substance abuse is another risk factor: Current illicit drug use is higher among AIANs (12.8%) than in other races or ethnicities.

Lack of access to appropriate health care is another crucial factor, especially in the extremely poor AIAN communities, where between 2002 and 2004 about twice the national average was living in poverty. An estimated 1 in 5 AIAN adults living with HIV/AIDS at the end of 2009 were unaware of their infection. And while 75% of those who found out they were living with HIV in 2010 were linked to medical care within 3 months, this was the lowest proportion of any group surveyed.

Effective prevention interventions, the CDC says, must be tailored to the population. But the AIAN population comprises 562 federally recognized tribes and at least 50 state-recognized tribes, with different culture, beliefs, practices, and languages. Further, at the time of AIDS diagnosis, more AIANs lived in rural areas and may have been less likely to be tested for HIV because of limited access to testing. They also may have been less likely to seek testing because of concerns of confidentiality in a small and close-knit community. More than half of AIANs who responded to the Behavioral Risk Factor Surveillance System survey during 1997-2000 said they had never been tested for HIV.

The Indian Health Service (IHS) created a video to promote testing, “Facing HIV/AIDS in Native Communities,” available at http://www.ihs.gov/hivaids. Promotional materials include radio public service announcements, and training kits. Information on reaching out through social media and other emerging technology can be found at http://www.aids.gov/using-new-media.

“We have shown the positive impact of focused HIV/AIDS screening, education, treatment, and prevention in a group of IHS facilities,” says IHS Chief Clinical Consultant for Infectious Diseases Dr. Jonathan Iralu. “Now is the time to offer the opportunity to have an ‘AIDS Free Generation’ to all American Indian and Alaska Native communities that we serve.”

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In 2010, American Indians and Alaska Natives (AIANs) accounted for < 1% of the estimated 47,500 new cases of human immunodeficiency virus (HIV) infection in the U.S. However, that proportion is misleading. When population size is taken into account for 2011, AIANs ranked fifth in rates of HIV/AIDS (acquired immunodeficiency syndrome) diagnoses. And the rate of AIDS diagnosis for this group has been higher than that for whites since 1995, according to the Centers for Disease Control and Prevention (CDC).

Also, AIANs diagnosed with HIV/AIDS die sooner: Between 2003 and 2007, only 81% lived longer than 36 months after being diagnosed. In 2010, HIV infection was the ninth leading cause of death among AIAN men and women aged 25 to 34 years.

Race and ethnicity are not, by themselves, risk factors for HIV infection, the CDC says. American Indian and Alaska Natives also have high rates of Chlamydia trachomatis infection, gonorrhea, and syphilis—sexually transmitted diseases are a warning signal of contracting or spreading HIV. Substance abuse is another risk factor: Current illicit drug use is higher among AIANs (12.8%) than in other races or ethnicities.

Lack of access to appropriate health care is another crucial factor, especially in the extremely poor AIAN communities, where between 2002 and 2004 about twice the national average was living in poverty. An estimated 1 in 5 AIAN adults living with HIV/AIDS at the end of 2009 were unaware of their infection. And while 75% of those who found out they were living with HIV in 2010 were linked to medical care within 3 months, this was the lowest proportion of any group surveyed.

Effective prevention interventions, the CDC says, must be tailored to the population. But the AIAN population comprises 562 federally recognized tribes and at least 50 state-recognized tribes, with different culture, beliefs, practices, and languages. Further, at the time of AIDS diagnosis, more AIANs lived in rural areas and may have been less likely to be tested for HIV because of limited access to testing. They also may have been less likely to seek testing because of concerns of confidentiality in a small and close-knit community. More than half of AIANs who responded to the Behavioral Risk Factor Surveillance System survey during 1997-2000 said they had never been tested for HIV.

The Indian Health Service (IHS) created a video to promote testing, “Facing HIV/AIDS in Native Communities,” available at http://www.ihs.gov/hivaids. Promotional materials include radio public service announcements, and training kits. Information on reaching out through social media and other emerging technology can be found at http://www.aids.gov/using-new-media.

“We have shown the positive impact of focused HIV/AIDS screening, education, treatment, and prevention in a group of IHS facilities,” says IHS Chief Clinical Consultant for Infectious Diseases Dr. Jonathan Iralu. “Now is the time to offer the opportunity to have an ‘AIDS Free Generation’ to all American Indian and Alaska Native communities that we serve.”

In 2010, American Indians and Alaska Natives (AIANs) accounted for < 1% of the estimated 47,500 new cases of human immunodeficiency virus (HIV) infection in the U.S. However, that proportion is misleading. When population size is taken into account for 2011, AIANs ranked fifth in rates of HIV/AIDS (acquired immunodeficiency syndrome) diagnoses. And the rate of AIDS diagnosis for this group has been higher than that for whites since 1995, according to the Centers for Disease Control and Prevention (CDC).

Also, AIANs diagnosed with HIV/AIDS die sooner: Between 2003 and 2007, only 81% lived longer than 36 months after being diagnosed. In 2010, HIV infection was the ninth leading cause of death among AIAN men and women aged 25 to 34 years.

Race and ethnicity are not, by themselves, risk factors for HIV infection, the CDC says. American Indian and Alaska Natives also have high rates of Chlamydia trachomatis infection, gonorrhea, and syphilis—sexually transmitted diseases are a warning signal of contracting or spreading HIV. Substance abuse is another risk factor: Current illicit drug use is higher among AIANs (12.8%) than in other races or ethnicities.

Lack of access to appropriate health care is another crucial factor, especially in the extremely poor AIAN communities, where between 2002 and 2004 about twice the national average was living in poverty. An estimated 1 in 5 AIAN adults living with HIV/AIDS at the end of 2009 were unaware of their infection. And while 75% of those who found out they were living with HIV in 2010 were linked to medical care within 3 months, this was the lowest proportion of any group surveyed.

Effective prevention interventions, the CDC says, must be tailored to the population. But the AIAN population comprises 562 federally recognized tribes and at least 50 state-recognized tribes, with different culture, beliefs, practices, and languages. Further, at the time of AIDS diagnosis, more AIANs lived in rural areas and may have been less likely to be tested for HIV because of limited access to testing. They also may have been less likely to seek testing because of concerns of confidentiality in a small and close-knit community. More than half of AIANs who responded to the Behavioral Risk Factor Surveillance System survey during 1997-2000 said they had never been tested for HIV.

The Indian Health Service (IHS) created a video to promote testing, “Facing HIV/AIDS in Native Communities,” available at http://www.ihs.gov/hivaids. Promotional materials include radio public service announcements, and training kits. Information on reaching out through social media and other emerging technology can be found at http://www.aids.gov/using-new-media.

“We have shown the positive impact of focused HIV/AIDS screening, education, treatment, and prevention in a group of IHS facilities,” says IHS Chief Clinical Consultant for Infectious Diseases Dr. Jonathan Iralu. “Now is the time to offer the opportunity to have an ‘AIDS Free Generation’ to all American Indian and Alaska Native communities that we serve.”

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IHS Takes Aim at HIV
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