Mixed Topics

For those of us who cannot sit in the sun and fish all day, the next best thing for preventing autoimmune diseases may be supplementation with vitamin D and fish oil-derived omega-3 fatty acids, results of a large prospective randomized trial suggest.

Ziga Plahutar

Among nearly 26,000 adults enrolled in a randomized trial designed primarily to study the effects of vitamin D and omega-3 supplementation on incident cancer and cardiovascular disease, 5 years of vitamin D supplementation was associated with a 22% reduction in risk for confirmed autoimmune diseases, and 5 years of omega-3 fatty acid supplementation was associated with an 18% reduction in confirmed and probable incident autoimmune diseases, reported Karen H. Costenbader, MD, MPH, of Brigham & Women’s Hospital in Boston.

“The clinical importance of these results is very high, given that these are nontoxic, well-tolerated supplements, and that there are no other known effective therapies to reduce the incidence of autoimmune diseases,” she said during the virtual annual meeting of the American College of Rheumatology.

“People do have to take the supplements a long time to start to see the reduction in risk, especially for vitamin D, but they make biological sense, and autoimmune diseases develop slowly over time, so taking it today isn’t going to reduce risk of developing something tomorrow,” Dr. Costenbader said in an interview.

“These supplements have other health benefits. Obviously, fish oil is anti-inflammatory, and vitamin D is good for osteoporosis prevention, especially in our patients who take glucocorticoids. People who are otherwise healthy and have a family history of autoimmune disease might also consider starting to take these supplements,” she said.

After watching her presentation, session co-moderator Gregg Silverman, MD, from the NYU Langone School of Medicine in New York, who was not involved in the study, commented “I’m going to [nutrition store] GNC to get some vitamins.”

When asked for comment, the other session moderator, Tracy Frech, MD, of Vanderbilt University, Nashville, said, “I think Dr. Costenbader’s work is very important and her presentation excellent. My current practice is replacement of vitamin D in all autoimmune disease patients with low levels and per bone health guidelines. Additionally, I discuss omega-3 supplementation with Sjögren’s [syndrome] patients as a consideration.”

Evidence base

Dr. Costenbader noted that in a 2013 observational study from France, vitamin D derived through ultraviolet (UV) light exposure was associated with a lower risk for incident Crohn’s disease but not ulcerative colitis, and in two analyses of data in 2014 from the Nurses’ Health Study, both high plasma levels of 25-OH vitamin D and geographic residence in areas of high UV exposure were associated with a decreased incidence of rheumatoid arthritis (RA).

Other observational studies have supported omega-3 fatty acids for their anti-inflammatory properties, including a 2005 Danish prospective cohort study showing a lower risk for RA in participants who reported higher levels of fatty fish intake. In a separate study conducted in 2017, healthy volunteers with higher omega-3 fatty acid/total lipid proportions in red blood cell membranes had a lower prevalence of anti-cyclic citrullinated peptide (anti-CCP) antibodies and rheumatoid factor and a lower incidence of progression to inflammatory arthritis, she said.

Ancillary study

Despite the evidence, however, there have been no prospective randomized trials to test the effects of either vitamin D or omega-3 fatty acid supplementation on the incidence of autoimmune disease over time.

To rectify this, Dr. Costenbader and colleagues piggybacked an ancillary study onto the Vitamin D and Omega-3 Trial (VITAL), which had primary outcomes of cancer and cardiovascular disease incidence.

A total of 25,871 participants were enrolled, including 12,786 men aged 50 and older, and 13,085 women aged 55 and older.

The study had a 2 x 2 factorial design, with patients randomly assigned to vitamin D 2,000 IU/day or placebo, and then further randomized to either 1 g/day omega-3 fatty acids or placebo in both the vitamin D and placebo primary randomization arms.

At baseline 16,956 participants were assayed for 25-OH vitamin D and plasma omega 3 index, the ratio of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) to total fatty acids. Participants self-reported baseline and all incident autoimmune diseases annually, with the reports confirmed by medical record review and disease criteria whenever possible.

Results

At 5 years of follow-up, confirmed incident autoimmune diseases had occurred in 123 patients in the active vitamin D group, compared with 155 in the placebo vitamin D group, translating into a hazard ratio (HR) for vitamin D of 0.78 (P = .045).

In the active omega-3 arm, 130 participants developed an autoimmune disease, compared with 148 in the placebo omega-3 arm, which translated into a nonsignificant HR of 0.85.

There was no statistical interaction between the two supplements. The investigators did observe an interaction between vitamin D and body mass index, with the effect stronger among participants with low BMI (P = .02). There also was an interaction between omega-3 fatty acids with a family history of autoimmune disease (P = .03).

In multivariate analysis adjusted for age, sex, race, and other supplement arm, vitamin D alone was associated with an HR for incident autoimmune disease of 0.68 (P = .02), omega-3 alone was associated with a nonsignificant HR of 0.74, and the combination was associated with an HR of 0.69 (P = .03).

Dr. Costenbader and colleagues acknowledged that the study was limited by the lack of a high-risk or nutritionally-deficient population, where the effects of supplementation might be larger; the restriction of the sample to older adults; and to the difficulty of confirming incident autoimmune thyroid disease from patient reports.

Cheryl Koehn, an arthritis patient advocate from Vancouver, Canada, who was not involved in the study, commented in the “chat” section of the presentation that her rheumatologist “has recommended vitamin D for years now. Says basically everyone north of Boston is vitamin D deficient. I take 1,000 IU per day. Been taking it for years.” Ms. Koehn is the founder and president of Arthritis Consumer Experts, a website that provides education to those with arthritis.

“Agreed. I tell every patient to take vitamin D supplement,” commented Fatma Dedeoglu, MD, a rheumatologist at Boston Children’s Hospital.



A version of this article first appeared on Medscape.com.

For those of us who cannot sit in the sun and fish all day, the next best thing for preventing autoimmune diseases may be supplementation with vitamin D and fish oil-derived omega-3 fatty acids, results of a large prospective randomized trial suggest.

Ziga Plahutar

Among nearly 26,000 adults enrolled in a randomized trial designed primarily to study the effects of vitamin D and omega-3 supplementation on incident cancer and cardiovascular disease, 5 years of vitamin D supplementation was associated with a 22% reduction in risk for confirmed autoimmune diseases, and 5 years of omega-3 fatty acid supplementation was associated with an 18% reduction in confirmed and probable incident autoimmune diseases, reported Karen H. Costenbader, MD, MPH, of Brigham & Women’s Hospital in Boston.

“The clinical importance of these results is very high, given that these are nontoxic, well-tolerated supplements, and that there are no other known effective therapies to reduce the incidence of autoimmune diseases,” she said during the virtual annual meeting of the American College of Rheumatology.

“People do have to take the supplements a long time to start to see the reduction in risk, especially for vitamin D, but they make biological sense, and autoimmune diseases develop slowly over time, so taking it today isn’t going to reduce risk of developing something tomorrow,” Dr. Costenbader said in an interview.

“These supplements have other health benefits. Obviously, fish oil is anti-inflammatory, and vitamin D is good for osteoporosis prevention, especially in our patients who take glucocorticoids. People who are otherwise healthy and have a family history of autoimmune disease might also consider starting to take these supplements,” she said.

After watching her presentation, session co-moderator Gregg Silverman, MD, from the NYU Langone School of Medicine in New York, who was not involved in the study, commented “I’m going to [nutrition store] GNC to get some vitamins.”

When asked for comment, the other session moderator, Tracy Frech, MD, of Vanderbilt University, Nashville, said, “I think Dr. Costenbader’s work is very important and her presentation excellent. My current practice is replacement of vitamin D in all autoimmune disease patients with low levels and per bone health guidelines. Additionally, I discuss omega-3 supplementation with Sjögren’s [syndrome] patients as a consideration.”

Evidence base

Dr. Costenbader noted that in a 2013 observational study from France, vitamin D derived through ultraviolet (UV) light exposure was associated with a lower risk for incident Crohn’s disease but not ulcerative colitis, and in two analyses of data in 2014 from the Nurses’ Health Study, both high plasma levels of 25-OH vitamin D and geographic residence in areas of high UV exposure were associated with a decreased incidence of rheumatoid arthritis (RA).

Other observational studies have supported omega-3 fatty acids for their anti-inflammatory properties, including a 2005 Danish prospective cohort study showing a lower risk for RA in participants who reported higher levels of fatty fish intake. In a separate study conducted in 2017, healthy volunteers with higher omega-3 fatty acid/total lipid proportions in red blood cell membranes had a lower prevalence of anti-cyclic citrullinated peptide (anti-CCP) antibodies and rheumatoid factor and a lower incidence of progression to inflammatory arthritis, she said.

Ancillary study

Despite the evidence, however, there have been no prospective randomized trials to test the effects of either vitamin D or omega-3 fatty acid supplementation on the incidence of autoimmune disease over time.

To rectify this, Dr. Costenbader and colleagues piggybacked an ancillary study onto the Vitamin D and Omega-3 Trial (VITAL), which had primary outcomes of cancer and cardiovascular disease incidence.

A total of 25,871 participants were enrolled, including 12,786 men aged 50 and older, and 13,085 women aged 55 and older.

The study had a 2 x 2 factorial design, with patients randomly assigned to vitamin D 2,000 IU/day or placebo, and then further randomized to either 1 g/day omega-3 fatty acids or placebo in both the vitamin D and placebo primary randomization arms.

At baseline 16,956 participants were assayed for 25-OH vitamin D and plasma omega 3 index, the ratio of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) to total fatty acids. Participants self-reported baseline and all incident autoimmune diseases annually, with the reports confirmed by medical record review and disease criteria whenever possible.

Results

At 5 years of follow-up, confirmed incident autoimmune diseases had occurred in 123 patients in the active vitamin D group, compared with 155 in the placebo vitamin D group, translating into a hazard ratio (HR) for vitamin D of 0.78 (P = .045).

In the active omega-3 arm, 130 participants developed an autoimmune disease, compared with 148 in the placebo omega-3 arm, which translated into a nonsignificant HR of 0.85.

There was no statistical interaction between the two supplements. The investigators did observe an interaction between vitamin D and body mass index, with the effect stronger among participants with low BMI (P = .02). There also was an interaction between omega-3 fatty acids with a family history of autoimmune disease (P = .03).

In multivariate analysis adjusted for age, sex, race, and other supplement arm, vitamin D alone was associated with an HR for incident autoimmune disease of 0.68 (P = .02), omega-3 alone was associated with a nonsignificant HR of 0.74, and the combination was associated with an HR of 0.69 (P = .03).

Dr. Costenbader and colleagues acknowledged that the study was limited by the lack of a high-risk or nutritionally-deficient population, where the effects of supplementation might be larger; the restriction of the sample to older adults; and to the difficulty of confirming incident autoimmune thyroid disease from patient reports.

Cheryl Koehn, an arthritis patient advocate from Vancouver, Canada, who was not involved in the study, commented in the “chat” section of the presentation that her rheumatologist “has recommended vitamin D for years now. Says basically everyone north of Boston is vitamin D deficient. I take 1,000 IU per day. Been taking it for years.” Ms. Koehn is the founder and president of Arthritis Consumer Experts, a website that provides education to those with arthritis.

“Agreed. I tell every patient to take vitamin D supplement,” commented Fatma Dedeoglu, MD, a rheumatologist at Boston Children’s Hospital.



A version of this article first appeared on Medscape.com.

For those of us who cannot sit in the sun and fish all day, the next best thing for preventing autoimmune diseases may be supplementation with vitamin D and fish oil-derived omega-3 fatty acids, results of a large prospective randomized trial suggest.

Ziga Plahutar

Among nearly 26,000 adults enrolled in a randomized trial designed primarily to study the effects of vitamin D and omega-3 supplementation on incident cancer and cardiovascular disease, 5 years of vitamin D supplementation was associated with a 22% reduction in risk for confirmed autoimmune diseases, and 5 years of omega-3 fatty acid supplementation was associated with an 18% reduction in confirmed and probable incident autoimmune diseases, reported Karen H. Costenbader, MD, MPH, of Brigham & Women’s Hospital in Boston.

“The clinical importance of these results is very high, given that these are nontoxic, well-tolerated supplements, and that there are no other known effective therapies to reduce the incidence of autoimmune diseases,” she said during the virtual annual meeting of the American College of Rheumatology.

“People do have to take the supplements a long time to start to see the reduction in risk, especially for vitamin D, but they make biological sense, and autoimmune diseases develop slowly over time, so taking it today isn’t going to reduce risk of developing something tomorrow,” Dr. Costenbader said in an interview.

“These supplements have other health benefits. Obviously, fish oil is anti-inflammatory, and vitamin D is good for osteoporosis prevention, especially in our patients who take glucocorticoids. People who are otherwise healthy and have a family history of autoimmune disease might also consider starting to take these supplements,” she said.

After watching her presentation, session co-moderator Gregg Silverman, MD, from the NYU Langone School of Medicine in New York, who was not involved in the study, commented “I’m going to [nutrition store] GNC to get some vitamins.”

When asked for comment, the other session moderator, Tracy Frech, MD, of Vanderbilt University, Nashville, said, “I think Dr. Costenbader’s work is very important and her presentation excellent. My current practice is replacement of vitamin D in all autoimmune disease patients with low levels and per bone health guidelines. Additionally, I discuss omega-3 supplementation with Sjögren’s [syndrome] patients as a consideration.”

Evidence base

Dr. Costenbader noted that in a 2013 observational study from France, vitamin D derived through ultraviolet (UV) light exposure was associated with a lower risk for incident Crohn’s disease but not ulcerative colitis, and in two analyses of data in 2014 from the Nurses’ Health Study, both high plasma levels of 25-OH vitamin D and geographic residence in areas of high UV exposure were associated with a decreased incidence of rheumatoid arthritis (RA).

Other observational studies have supported omega-3 fatty acids for their anti-inflammatory properties, including a 2005 Danish prospective cohort study showing a lower risk for RA in participants who reported higher levels of fatty fish intake. In a separate study conducted in 2017, healthy volunteers with higher omega-3 fatty acid/total lipid proportions in red blood cell membranes had a lower prevalence of anti-cyclic citrullinated peptide (anti-CCP) antibodies and rheumatoid factor and a lower incidence of progression to inflammatory arthritis, she said.

Ancillary study

Despite the evidence, however, there have been no prospective randomized trials to test the effects of either vitamin D or omega-3 fatty acid supplementation on the incidence of autoimmune disease over time.

To rectify this, Dr. Costenbader and colleagues piggybacked an ancillary study onto the Vitamin D and Omega-3 Trial (VITAL), which had primary outcomes of cancer and cardiovascular disease incidence.

A total of 25,871 participants were enrolled, including 12,786 men aged 50 and older, and 13,085 women aged 55 and older.

The study had a 2 x 2 factorial design, with patients randomly assigned to vitamin D 2,000 IU/day or placebo, and then further randomized to either 1 g/day omega-3 fatty acids or placebo in both the vitamin D and placebo primary randomization arms.

At baseline 16,956 participants were assayed for 25-OH vitamin D and plasma omega 3 index, the ratio of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) to total fatty acids. Participants self-reported baseline and all incident autoimmune diseases annually, with the reports confirmed by medical record review and disease criteria whenever possible.

Results

At 5 years of follow-up, confirmed incident autoimmune diseases had occurred in 123 patients in the active vitamin D group, compared with 155 in the placebo vitamin D group, translating into a hazard ratio (HR) for vitamin D of 0.78 (P = .045).

In the active omega-3 arm, 130 participants developed an autoimmune disease, compared with 148 in the placebo omega-3 arm, which translated into a nonsignificant HR of 0.85.

There was no statistical interaction between the two supplements. The investigators did observe an interaction between vitamin D and body mass index, with the effect stronger among participants with low BMI (P = .02). There also was an interaction between omega-3 fatty acids with a family history of autoimmune disease (P = .03).

In multivariate analysis adjusted for age, sex, race, and other supplement arm, vitamin D alone was associated with an HR for incident autoimmune disease of 0.68 (P = .02), omega-3 alone was associated with a nonsignificant HR of 0.74, and the combination was associated with an HR of 0.69 (P = .03).

Dr. Costenbader and colleagues acknowledged that the study was limited by the lack of a high-risk or nutritionally-deficient population, where the effects of supplementation might be larger; the restriction of the sample to older adults; and to the difficulty of confirming incident autoimmune thyroid disease from patient reports.

Cheryl Koehn, an arthritis patient advocate from Vancouver, Canada, who was not involved in the study, commented in the “chat” section of the presentation that her rheumatologist “has recommended vitamin D for years now. Says basically everyone north of Boston is vitamin D deficient. I take 1,000 IU per day. Been taking it for years.” Ms. Koehn is the founder and president of Arthritis Consumer Experts, a website that provides education to those with arthritis.

“Agreed. I tell every patient to take vitamin D supplement,” commented Fatma Dedeoglu, MD, a rheumatologist at Boston Children’s Hospital.



A version of this article first appeared on Medscape.com.

Artificial Intelligence (AI) was first described in 1956 and refers to machines having the ability to learn as they receive and process information, resulting in the ability to “think” like humans.¹ AI’s impact in medicine is increasing; currently, at least 29 AI medical devices and algorithms are approved by the US Food and Drug Administration (FDA) in a variety of areas, including radiograph interpretation, managing glucose levels in patients with diabetes mellitus, analyzing electrocardiograms (ECGs), and diagnosing sleep disorders among others.² Significantly, in 2020, the Centers for Medicare and Medicaid Services (CMS) announced the first reimbursement to hospitals for an AI platform, a model for early detection of strokes.³ AI is rapidly becoming an integral part of health care, and its role will only increase in the future (Table).

As knowledge in medicine is expanding exponentially, AI has great potential to assist with handling complex patient care data. The concept of exponential growth is not a natural one. As Bini described, with exponential growth the volume of knowledge amassed over the past 10 years will now occur in perhaps only 1 year.¹ Likewise, equivalent advances over the past year may take just a few months. This phenomenon is partly due to the law of accelerating returns, which states that advances feed on themselves, continually increasing the rate of further advances.⁴ The volume of medical data doubles every 2 to 5 years.⁵ Fortunately, the field of AI is growing exponentially as well and can help health care practitioners (HCPs) keep pace, allowing the continued delivery of effective health care.

In this report, we review common terminology, principles, and general applications of AI, followed by current and potential applications of AI for selected medical specialties. Finally, we discuss AI’s future in health care, along with potential risks and pitfalls.

AI Overview

AI refers to machine programs that can “learn” or think based on past experiences. This functionality contrasts with simple rules-based programming available to health care for years. An example of rules-based programming is the warfarindosing.org website developed by Barnes-Jewish Hospital at Washington University Medical Center, which guides initial warfarin dosing.^6,7 The prescriber inputs detailed patient information, including age, sex, height, weight, tobacco history, medications, laboratory results, and genotype if available. The application then calculates recommended warfarin dosing regimens to avoid over- or underanticoagulation. While the dosing algorithm may be complex, it depends entirely on preprogrammed rules. The program does not learn to reach its conclusions and recommendations from patient data.

In contrast, one of the most common subsets of AI is machine learning (ML). ML describes a program that “learns from experience and improves its performance as it learns.”¹ With ML, the computer is initially provided with a training data set—data with known outcomes or labels. Because the initial data are input from known samples, this type of AI is known as supervised learning.^8-10 As an example, we recently reported using ML to diagnose various types of cancer from pathology slides.¹¹ In one experiment, we captured images of colon adenocarcinoma and normal colon (these 2 groups represent the training data set). Unlike traditional programming, we did not define characteristics that would differentiate colon cancer from normal; rather, the machine learned these characteristics independently by assessing the labeled images provided. A second data set (the validation data set) was used to evaluate the program and fine-tune the ML training model’s parameters. Finally, the program was presented with new images of cancer and normal cases for final assessment of accuracy (test data set). Our program learned to recognize differences from the images provided and was able to differentiate normal and cancer images with > 95% accuracy.

Advances in computer processing have allowed for the development of artificial neural networks (ANNs). While there are several types of ANNs, the most common types used for image classification and segmentation are known as convolutional neural networks (CNNs).^9,12-14 The programs are designed to work similar to the human brain, specifically the visual cortex.^15,16 As data are acquired, they are processed by various layers in the program. Much like neurons in the brain, one layer decides whether to advance information to the next.^13,14 CNNs can be many layers deep, leading to the term deep learning: “computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.”^1,13,17

ANNs can process larger volumes of data. This advance has led to the development of unstructured or unsupervised learning. With this type of learning, imputing defined features (ie, predetermined answers) of the training data set described above is no longer required.^1,8,10,14 The advantage of unsupervised learning is that the program can be presented raw data and extract meaningful interpretation without human input, often with less bias than may exist with supervised learning.^1,18 If shown enough data, the program can extract relevant features to make conclusions independently without predefined definitions, potentially uncovering markers not previously known. For example, several studies have used unsupervised learning to search patient data to assess readmission risks of patients with congestive heart failure.^10,19,20 AI compiled features independently and not previously defined, predicting patients at greater risk for readmission superior to traditional methods.

Health Care Applications

Image analysis has seen the most AI health care applications.^8,15 AI has shown potential in interpreting many types of medical images, including pathology slides, radiographs of various types, retina and other eye scans, and photographs of skin lesions. Many studies have demonstrated that AI can interpret these images as accurately as or even better than experienced clinicians.^9,13,22-29 Studies have suggested AI interpretation of radiographs may better distinguish patients infected with COVID-19 from other causes of pneumonia, and AI interpretation of pathology slides may detect specific genetic mutations not previously identified without additional molecular tests.^{11,14,23,24,30-32}

The second area in which AI can impact health care is improving workflow and efficiency. AI has improved surgery scheduling, saving significant revenue, and decreased patient wait times for appointments.¹ AI can screen and triage radiographs, allowing attention to be directed to critical patients. This use would be valuable in many busy clinical settings, such as the recent COVID-19 pandemic.^8,23 Similarly, AI can screen retina images to prioritize urgent conditions.²⁵ AI has improved pathologists’ efficiency when used to detect breast metastases.³³ Finally, AI may reduce medical errors, thereby ensuring patient safety.^8,9,34

A third health care benefit of AI is in public health and epidemiology. AI can assist with clinical decision-making and diagnoses in low-income countries and areas with limited health care resources and personnel.^25,29 AI can improve identification of infectious outbreaks, such as tuberculosis, malaria, dengue fever, and influenza.^29,35-40 AI has been used to predict transmission patterns of the Zika virus and the current COVID-19 pandemic.^41,42 Applications can stratify the risk of outbreaks based on multiple factors, including age, income, race, atypical geographic clusters, and seasonal factors like rainfall and temperature.^35,36,38,43 AI has been used to assess morbidity and mortality, such as predicting disease severity with malaria and identifying treatment failures in tuberculosis.²⁹

Finally, AI can dramatically impact health care due to processing large data sets or disconnected volumes of patient information—so-called big data.^44-46 An example is the widespread use of electronic health records (EHRs) such as the Computerized Patient Record System used in Veteran Affairs medical centers (VAMCs). Much of patient information exists in written text: HCP notes, laboratory and radiology reports, medication records, etc. Natural language processing (NLP) allows platforms to sort through extensive volumes of data on complex patients at rates much faster than human capability, which has great potential to assist with diagnosis and treatment decisions.⁹

Medical literature is being produced at rates that exceed our ability to digest. More than 200,000 cancer-related articles were published in 2019 alone.¹⁴ NLP capabilities of AI have the potential to rapidly sort through this extensive medical literature and relate specific verbiage in patient records guiding therapy.⁴⁶ IBM Watson, a supercomputer based on ML and NLP, demonstrates this concept with many potential applications, only some of which relate to health care.^1,9 Watson has an oncology component to assimilate multiple aspects of patient care, including clinical notes, pathology results, radiograph findings, staging, and a tumor’s genetic profile. It coordinates these inputs from the EHR and mines medical literature and research databases to recommend treatment options.^1,46 AI can assess and compile far greater patient data and therapeutic options than would be feasible by individual clinicians, thus providing customized patient care.⁴⁷ Watson has partnered with numerous medical centers, including MD Anderson Cancer Center and Memorial Sloan Kettering Cancer Center, with variable success.^44,47-49 While the full potential of Watson appears not yet realized, these AI-driven approaches will likely play an important role in leveraging the hidden value in the expanding volume of health care information.

Medical Specialty Applications

Radiology

Currently > 70% of FDA-approved AI medical devices are in the field of radiology.² Most radiology departments have used AI-friendly digital imaging for years, such as the picture archiving and communication systems used by numerous health care systems, including VAMCs.^2,15 Gray-scale images common in radiology lend themselves to standardization, although AI is not limited to black-and- white image interpretation.¹⁵

An abundance of literature describes plain radiograph interpretation using AI. One FDA-approved platform improved X-ray diagnosis of wrist fractures when used by emergency medicine clinicians.^2,50 AI has been applied to chest X-ray (CXR) interpretation of many conditions, including pneumonia, tuberculosis, malignant lung lesions, and COVID-19.^{23,25,28,44,51-53} For example, Nam and colleagues suggested AI is better at diagnosing malignant pulmonary nodules from CXRs than are trained radiologists.²⁸

In addition to plain radiographs, AI has been applied to many other imaging technologies, including ultrasounds, positron emission tomography, mammograms, computed tomography (CT), and magnetic resonance imaging (MRI).^{15,26,44,48,54-56} A large study demonstrated that ML platforms significantly reduced the time to diagnose intracranial hemorrhages on CT and identified subtle hemorrhages missed by radiologists.⁵⁵ Other studies have claimed that AI programs may be better than radiologists in detecting cancer in screening mammograms, and 3 FDA-approved devices focus on mammogram interpretation.^2,15,54,57 There is also great interest in MRI applications to detect and predict prognosis for breast cancer based on imaging findings.^21,56

Aside from providing accurate diagnoses, other studies focus on AI radiograph interpretation to assist with patient screening, triage, improving time to final diagnosis, providing a rapid “second opinion,” and even monitoring disease progression and offering insights into prognosis.^{8,21,23,52,55,56,58} These features help in busy urban centers but may play an even greater role in areas with limited access to health care or trained specialists such as radiologists.⁵²

Cardiology

Cardiology has the second highest number of FDA-approved AI applications.² Many cardiology AI platforms involve image analysis, as described in several recent reviews.^45,59,60 AI has been applied to echocardiography to measure ejection fractions, detect valvular disease, and assess heart failure from hypertrophic and restrictive cardiomyopathy and amyloidosis.^45,48,59 Applications for cardiac CT scans and CT angiography have successfully quantified both calcified and noncalcified coronary artery plaques and lumen assessments, assessed myocardial perfusion, and performed coronary artery calcium scoring.^45,59,60 Likewise, AI applications for cardiac MRI have been used to quantitate ejection fraction, large vessel flow assessment, and cardiac scar burden.^45,59

For years ECG devices have provided interpretation with limited accuracy using preprogrammed parameters.⁴⁸ However, the application of AI allows ECG interpretation on par with trained cardiologists. Numerous such AI applications exist, and 2 FDA-approved devices perform ECG interpretation.^2,61-64 One of these devices incorporates an AI-powered stethoscope to detect atrial fibrillation and heart murmurs.⁶⁵

Pathology

The advancement of whole slide imaging, wherein entire slides can be scanned and digitized at high speed and resolution, creates great potential for AI applications in pathology.^{12,24,32,33,66} A landmark study demonstrating the potential of AI for assessing whole slide imaging examined sentinel lymph node metastases in patients with breast cancer.²² Multiple algorithms in the study demonstrated that AI was equivalent or better than pathologists in detecting metastases, especially when the pathologists were time-constrained consistent with a normal working environment. Significantly, the most accurate and efficient diagnoses were achieved when the pathologist and AI interpretations were used together.^22,33

AI has shown promise in diagnosing many other entities, including cancers of the prostate (including Gleason scoring), lung, colon, breast, and skin.^{11,12,24,27,32,67} In addition, AI has shown great potential in scoring biomarkers important for prognosis and treatment, such as immunohistochemistry (IHC) labeling of Ki-67 and PD-L1.³² Pathologists can have difficulty classifying certain tumors or determining the site of origin for metastases, often having to rely on IHC with limited success. The unique features of image analysis with AI have the potential to assist in classifying difficult tumors and identifying sites of origin for metastatic disease based on morphology alone.¹¹

Oncology depends heavily on molecular pathology testing to dictate treatment options and determine prognosis. Preliminary studies suggest that AI interpretation alone has the potential to delineate whether certain molecular mutations are present in tumors from various sites.^11,14,24,32 One study combined histology and genomic results for AI interpretation that improved prognostic predictions.⁶⁸ In addition, AI analysis may have potential in predicting tumor recurrence or prognosis based on cellular features, as demonstrated for lung cancer and melanoma.^67,69,70

Ophthalmology

AI applications for ophthalmology have focused on diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related and congenital cataracts, and retinal vein occlusion.^71-73 Diabetic retinopathy is a leading cause of blindness and has been studied by numerous platforms with good success, most having used color fundus photography.^71,72 One study showed AI could diagnose diabetic retinopathy and diabetic macular edema with specificities similar to ophthalmologists.⁷⁴ In 2018, the FDA approved the AI platform IDx-DR. This diagnostic system classifies retinal images and recommends referral for patients determined to have “more than mild diabetic retinopathy” and reexamination within a year for other patients.^8,75 Significantly, the platform recommendations do not require confirmation by a clinician.⁸

AI has been applied to other modalities in ophthalmology such as optical coherence tomography (OCT) to diagnose retinal disease and to predict appropriate management of congenital cataracts.^25,73,76 For example, an AI application using OCT has been demonstrated to match or exceed the accuracy of retinal experts in diagnosing and triaging patients with a variety of retinal pathologies, including patients needing urgent referrals.⁷⁷

Dermatology

Multiple studies demonstrate AI performs at least equal to experienced dermatologists in differentiating selected skin lesions.^78-81 For example, Esteva and colleagues demonstrated AI could differentiate keratinocyte carcinomas from benign seborrheic keratoses and malignant melanomas from benign nevi with accuracy equal to 21 board-certified dermatologists.⁷⁸

AI is applicable to various imaging procedures common to dermatology, such as dermoscopy, very high-frequency ultrasound, and reflectance confocal microscopy.⁸² Several studies have demonstrated that AI interpretation compared favorably to dermatologists evaluating dermoscopy to assess melanocytic lesions.^78-81,83

A limitation in these studies is that they differentiate only a few diagnoses.⁸² Furthermore, dermatologists have sensory input such as touch and visual examination under various conditions, something AI has yet to replicate.^15,34,84 Also, most AI devices use no or limited clinical information.⁸¹ Dermatologists can recognize rarer conditions for which AI models may have had limited or no training.³⁴ Nevertheless, a recent study assessed AI for the diagnosis of 134 separate skin disorders with promising results, including providing diagnoses with accuracy comparable to that of dermatologists and providing accurate treatment strategies.⁸⁴ As Topol points out, most skin lesions are diagnosed in the primary care setting where AI can have a greater impact when used in conjunction with the clinical impression, especially where specialists are in limited supply.^48,78

Finally, dermatology lends itself to using portable or smartphone applications (apps) wherein the user can photograph a lesion for analysis by AI algorithms to assess the need for further evaluation or make treatment recommendations.^34,84,85 Although results from currently available apps are not encouraging, they may play a greater role as the technology advances.^34,85

Oncology

Applications of AI in oncology include predicting prognosis for patients with cancer based on histologic and/or genetic information.^14,68,86 Programs can predict the risk of complications before and recurrence risks after surgery for malignancies.^44,87-89 AI can also assist in treatment planning and predict treatment failure with radiation therapy.^90,91

AI has great potential in processing the large volumes of patient data in cancer genomics. Next-generation sequencing has allowed for the identification of millions of DNA sequences in a single tumor to detect genetic anomalies.⁹² Thousands of mutations can be found in individual tumor samples, and processing this information and determining its significance can be beyond human capability.¹⁴ We know little about the effects of various mutation combinations, and most tumors have a heterogeneous molecular profile among different cell populations.^14,93 The presence or absence of various mutations can have diagnostic, prognostic, and therapeutic implications.⁹³ AI has great potential to sort through these complex data and identify actionable findings.

More than 200,000 cancer-related articles were published in 2019, and publications in the field of cancer genomics are increasing exponentially.^14,92,93 Patel and colleagues assessed the utility of IBM Watson for Genomics against results from a molecular tumor board.⁹³ Watson for Genomics identified potentially significant mutations not identified by the tumor board in 32% of patients. Most mutations were related to new clinical trials not yet added to the tumor board watch list, demonstrating the role AI will have in processing the large volume of genetic data required to deliver personalized medicine moving forward.

Gastroenterology

AI has shown promise in predicting risk or outcomes based on clinical parameters in various common gastroenterology problems, including gastric reflux, acute pancreatitis, gastrointestinal bleeding, celiac disease, and inflammatory bowel disease.^94,95 AI endoscopic analysis has demonstrated potential in assessing Barrett’s esophagus, gastric Helicobacter pylori infections, gastric atrophy, and gastric intestinal metaplasia.⁹⁵ Applications have been used to assess esophageal, gastric, and colonic malignancies, including depth of invasion based on endoscopic images.⁹⁵ Finally, studies have evaluated AI to assess small colon polyps during colonoscopy, including differentiating benign and premalignant polyps with success comparable to gastroenterologists.^94,95 AI has been shown to increase the speed and accuracy of gastroenterologists in detecting small polyps during colonoscopy.⁴⁸ In a prospective randomized study, colonoscopies performed using an AI device identified significantly more small adenomatous polyps than colonoscopies without AI.⁹⁶

Neurology

It has been suggested that AI technologies are well suited for application in neurology due to the subtle presentation of many neurologic diseases.¹⁶ Viz LVO, the first CMS-approved AI reimbursement for the diagnosis of strokes, analyzes CTs to detect early ischemic strokes and alerts the medical team, thus shortening time to treatment.^3,97 Many other AI platforms are in use or development that use CT and MRI for the early detection of strokes as well as for treatment and prognosis.^9,97

AI technologies have been applied to neurodegenerative diseases, such as Alzheimer and Parkinson diseases.^16,98 For example, several studies have evaluated patient movements in Parkinson disease for both early diagnosis and to assess response to treatment.⁹⁸ These evaluations included assessment with both external cameras as well as wearable devices and smartphone apps.

Mental Health

Due to the interactive nature of mental health care, the field has been slower to develop AI applications.¹⁸ With heavy reliance on textual information (eg, clinic notes, mood rating scales, and documentation of conversations), successful AI applications in this field will likely rely heavily on NLP.¹⁸ However, studies investigating the application of AI to mental health have also incorporated data such as brain imaging, smartphone monitoring, and social media platforms, such as Facebook and Twitter.^18,103,104

The risk of suicide is higher in veteran patients, and ML algorithms have had limited success in predicting suicide risk in both veteran and nonveteran populations.^104-106 While early models have low positive predictive values and low sensitivities, they still promise to be a useful tool in conjunction with traditional risk assessments.¹⁰⁶ Kessler and colleagues suggest that combining multiple rather than single ML algorithms might lead to greater success.^105,106

AI may assist in diagnosing other mental health disorders, including major depressive disorder, attention deficit hyperactivity disorder (ADHD), schizophrenia, posttraumatic stress disorder, and Alzheimer disease.^103,104,107 These investigations are in the early stages with limited clinical applicability. However, 2 AI applications awaiting FDA approval relate to ADHD and opioid use.² Furthermore, potential exists for AI to not only assist with prevention and diagnosis of ADHD, but also to identify optimal treatment options.^2,103

General and Personalized Medicine

Additional AI applications include diagnosing patients with suspected sepsis, measuring liver iron concentrations, predicting hospital mortality at the time of admission, and more.^2,108,109 AI can guide end-of-life decisions such as resuscitation status or whether to initiate mechanical ventilation.⁴⁸

AI-driven smartphone apps can be beneficial to both patients and clinicians. Examples include predicting nonadherence to anticoagulation therapy, monitoring heart rhythms for atrial fibrillation or signs of hyperkalemia in patients with renal failure, and improving outcomes for patients with diabetes mellitus by decreasing glycemic variability and reducing hypoglycemia.^8,48,110,111 The potential for AI applications to health care and personalized medicine are almost limitless.

Discussion

With ever-increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has the potential to have numerous impacts. AI can improve diagnostic accuracy while limiting errors and impact patient safety such as assisting with prescription delivery.^8,9,34 It can screen and triage patients, alerting clinicians to those needing more urgent evaluation.^8,23,77,97 AI also may increase a clinician’s efficiency and speed to render a diagnosis.^12,13,55,97 AI can provide a rapid second opinion, an ability especially beneficial in underserved areas with shortages of specialists.^{23,25,26,29,34} Similarly, AI may decrease the inter- and intraobserver variability common in many medical specialties.^12,27,45 AI applications can also monitor disease progression, identifying patients at greatest risk, and provide information for prognosis.^21,23,56,58 Finally, as described with applications using IBM Watson, AI can allow for an integrated approach to health care that is currently lacking.

We have described many reports suggesting AI can render diagnoses as well as or better than experienced clinicians, and speculation exists that AI will replace many roles currently performed by health care practitioners.^9,26 However, most studies demonstrate that AI’s diagnostic benefits are best realized when used to supplement a clinician’s impression.^{8,22,30,33,52,54,56,69,84} AI is not likely to replace humans in health care in the foreseeable future. The technology can be likened to the impact of CT scans developed in the 1970s in neurology. Prior to such detailed imaging, neurologists spent extensive time performing detailed physicals to render diagnoses and locate lesions before surgery. There was mistrust of this new technology and concern that CT scans would eliminate the need for neurologists.¹¹² On the contrary, neurology is alive and well, frequently being augmented by the technologies once speculated to replace it.

Commercial AI health care platforms represented a $2 billion industry in 2018 and are growing rapidly each year.^13,32 Many AI products are offered ready for implementation for various tasks, including diagnostics, patient management, and improved efficiency. Others will likely be provided as templates suitable for modification to meet the specific needs of the facility, practice, or specialty for its patient population.

AI Risks and Limitations

AI has several risks and limitations. Although there is progress in explainable AI, at times we still struggle to understand how the output provided by machine learning algorithms was created.^44,48 The many layers associated with deep learning self-determine the criteria to reach its conclusion, and these criteria can continually evolve. The parameters of deep learning are not preprogrammed, and there are too many individual data points to be extrapolated or deconvoluted for evaluation at our current level of knowledge.^26,51 These apparent lack of constraints cause concern for patient safety and suggest that greater validation and continued scrutiny of validity is required.^8,48 Efforts are underway to create explainable AI programs to make their processes more transparent, but such clarification is limited presently.^14,26,48,77

Another challenge of AI is determining the amount of training data required to function optimally. Also, if the output describes multiple variables or diagnoses, are each equally valid?¹¹³ Furthermore, many AI applications look for a specific process, such as cancer diagnoses on CXRs. However, how coexisting conditions like cardiomegaly, emphysema, pneumonia, etc, seen on CXRs will affect the diagnosis needs to be considered.^51,52 Zech and colleagues provide the example that diagnoses for pneumothorax are frequently rendered on CXRs with chest tubes in place.⁵¹ They suggest that CNNs may develop a bias toward diagnosing pneumothorax when chest tubes are present. Many current studies approach an issue in isolation, a situation not realistic in real-world clinical practice.²⁶

Most studies on AI have been retrospective, and frequently data used to train the program are preselected.^13,26 The data are typically validated on available databases rather than actual patients in the clinical setting, limiting confidence in the validity of the AI output when applied to real-world situations. Currently, fewer than 12 prospective trials had been published comparing AI with traditional clinical care.^13,114 Randomized prospective clinical trials are even fewer, with none currently reported from the United States.^13,114 The results from several studies have been shown to diminish when repeated prospectively.¹¹⁴

The FDA has created a new category known as Software as a Medical Device and has a Digital Health Innovation Action Plan to regulate AI platforms. Still, the process of AI regulation is of necessity different from traditional approval processes and is continually evolving.⁸ The FDA approval process cannot account for the fact that the program’s parameters may continually evolve or adapt.²

Guidelines for investigating and reporting AI research with its unique attributes are being developed. Examples include the TRIPOD-ML statement and others.^49,115 In September 2020, 2 publications addressed the paucity of gold-standard randomized clinical trials in clinical AI applications.^116,117 The SPIRIT-AI statement expands on the original SPIRIT statement published in 2013 to guide minimal reporting standards for AI clinical trial protocols to promote transparency of design and methodology.¹¹⁶ Similarly, the CONSORT-AI extension, stemming from the original CONSORT statement in 1996, aims to ensure quality reporting of randomized controlled trials in AI.¹¹⁷

Another risk with AI is that while an individual physician making a mistake may adversely affect 1 patient, a single mistake in an AI algorithm could potentially affect thousands of patients.⁴⁸ Also, AI programs developed for patient populations at a facility may not translate to another. Referred to as overfitting, this phenomenon relates to selection bias in training data sets.^{15,34,49,51,52} Studies have shown that programs that underrepresent certain group characteristics such as age, sex, or race may be less effective when applied to a population in which these characteristics have differing representations.^8,48,49 This problem of underrepresentation has been demonstrated in programs interpreting pathology slides, radiographs, and skin lesions.^15,32,51

Admittedly, most of these challenges are not specific to AI and existed in health care previously. Physicians make mistakes, treatments are sometimes used without adequate prospective studies, and medications are given without understanding their mechanism of action, much like AI-facilitated processes reach a conclusion that cannot be fully explained.⁴⁸

Conclusions

The view that AI will dramatically impact health care in the coming years will likely prove true. However, much work is needed, especially because of the paucity of prospective clinical trials as has been historically required in medical research. Any concern that AI will replace HCPs seems unwarranted. Early studies suggest that even AI programs that appear to exceed human interpretation perform best when working in cooperation with and oversight from clinicians. AI’s greatest potential appears to be its ability to augment care from health professionals, improving efficiency and accuracy, and should be anticipated with enthusiasm as the field moves forward at an exponential rate.

Acknowledgments

The authors thank Makenna G. Thomas for proofreading and review of the manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital. This research has been approved by the James A. Haley Veteran’s Hospital Office of Communications and Media.

Artificial Intelligence (AI) was first described in 1956 and refers to machines having the ability to learn as they receive and process information, resulting in the ability to “think” like humans.¹ AI’s impact in medicine is increasing; currently, at least 29 AI medical devices and algorithms are approved by the US Food and Drug Administration (FDA) in a variety of areas, including radiograph interpretation, managing glucose levels in patients with diabetes mellitus, analyzing electrocardiograms (ECGs), and diagnosing sleep disorders among others.² Significantly, in 2020, the Centers for Medicare and Medicaid Services (CMS) announced the first reimbursement to hospitals for an AI platform, a model for early detection of strokes.³ AI is rapidly becoming an integral part of health care, and its role will only increase in the future (Table).

As knowledge in medicine is expanding exponentially, AI has great potential to assist with handling complex patient care data. The concept of exponential growth is not a natural one. As Bini described, with exponential growth the volume of knowledge amassed over the past 10 years will now occur in perhaps only 1 year.¹ Likewise, equivalent advances over the past year may take just a few months. This phenomenon is partly due to the law of accelerating returns, which states that advances feed on themselves, continually increasing the rate of further advances.⁴ The volume of medical data doubles every 2 to 5 years.⁵ Fortunately, the field of AI is growing exponentially as well and can help health care practitioners (HCPs) keep pace, allowing the continued delivery of effective health care.

In this report, we review common terminology, principles, and general applications of AI, followed by current and potential applications of AI for selected medical specialties. Finally, we discuss AI’s future in health care, along with potential risks and pitfalls.

AI Overview

AI refers to machine programs that can “learn” or think based on past experiences. This functionality contrasts with simple rules-based programming available to health care for years. An example of rules-based programming is the warfarindosing.org website developed by Barnes-Jewish Hospital at Washington University Medical Center, which guides initial warfarin dosing.^6,7 The prescriber inputs detailed patient information, including age, sex, height, weight, tobacco history, medications, laboratory results, and genotype if available. The application then calculates recommended warfarin dosing regimens to avoid over- or underanticoagulation. While the dosing algorithm may be complex, it depends entirely on preprogrammed rules. The program does not learn to reach its conclusions and recommendations from patient data.

In contrast, one of the most common subsets of AI is machine learning (ML). ML describes a program that “learns from experience and improves its performance as it learns.”¹ With ML, the computer is initially provided with a training data set—data with known outcomes or labels. Because the initial data are input from known samples, this type of AI is known as supervised learning.^8-10 As an example, we recently reported using ML to diagnose various types of cancer from pathology slides.¹¹ In one experiment, we captured images of colon adenocarcinoma and normal colon (these 2 groups represent the training data set). Unlike traditional programming, we did not define characteristics that would differentiate colon cancer from normal; rather, the machine learned these characteristics independently by assessing the labeled images provided. A second data set (the validation data set) was used to evaluate the program and fine-tune the ML training model’s parameters. Finally, the program was presented with new images of cancer and normal cases for final assessment of accuracy (test data set). Our program learned to recognize differences from the images provided and was able to differentiate normal and cancer images with > 95% accuracy.

Advances in computer processing have allowed for the development of artificial neural networks (ANNs). While there are several types of ANNs, the most common types used for image classification and segmentation are known as convolutional neural networks (CNNs).^9,12-14 The programs are designed to work similar to the human brain, specifically the visual cortex.^15,16 As data are acquired, they are processed by various layers in the program. Much like neurons in the brain, one layer decides whether to advance information to the next.^13,14 CNNs can be many layers deep, leading to the term deep learning: “computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.”^1,13,17

ANNs can process larger volumes of data. This advance has led to the development of unstructured or unsupervised learning. With this type of learning, imputing defined features (ie, predetermined answers) of the training data set described above is no longer required.^1,8,10,14 The advantage of unsupervised learning is that the program can be presented raw data and extract meaningful interpretation without human input, often with less bias than may exist with supervised learning.^1,18 If shown enough data, the program can extract relevant features to make conclusions independently without predefined definitions, potentially uncovering markers not previously known. For example, several studies have used unsupervised learning to search patient data to assess readmission risks of patients with congestive heart failure.^10,19,20 AI compiled features independently and not previously defined, predicting patients at greater risk for readmission superior to traditional methods.

Health Care Applications

Image analysis has seen the most AI health care applications.^8,15 AI has shown potential in interpreting many types of medical images, including pathology slides, radiographs of various types, retina and other eye scans, and photographs of skin lesions. Many studies have demonstrated that AI can interpret these images as accurately as or even better than experienced clinicians.^9,13,22-29 Studies have suggested AI interpretation of radiographs may better distinguish patients infected with COVID-19 from other causes of pneumonia, and AI interpretation of pathology slides may detect specific genetic mutations not previously identified without additional molecular tests.^{11,14,23,24,30-32}

The second area in which AI can impact health care is improving workflow and efficiency. AI has improved surgery scheduling, saving significant revenue, and decreased patient wait times for appointments.¹ AI can screen and triage radiographs, allowing attention to be directed to critical patients. This use would be valuable in many busy clinical settings, such as the recent COVID-19 pandemic.^8,23 Similarly, AI can screen retina images to prioritize urgent conditions.²⁵ AI has improved pathologists’ efficiency when used to detect breast metastases.³³ Finally, AI may reduce medical errors, thereby ensuring patient safety.^8,9,34

A third health care benefit of AI is in public health and epidemiology. AI can assist with clinical decision-making and diagnoses in low-income countries and areas with limited health care resources and personnel.^25,29 AI can improve identification of infectious outbreaks, such as tuberculosis, malaria, dengue fever, and influenza.^29,35-40 AI has been used to predict transmission patterns of the Zika virus and the current COVID-19 pandemic.^41,42 Applications can stratify the risk of outbreaks based on multiple factors, including age, income, race, atypical geographic clusters, and seasonal factors like rainfall and temperature.^35,36,38,43 AI has been used to assess morbidity and mortality, such as predicting disease severity with malaria and identifying treatment failures in tuberculosis.²⁹

Finally, AI can dramatically impact health care due to processing large data sets or disconnected volumes of patient information—so-called big data.^44-46 An example is the widespread use of electronic health records (EHRs) such as the Computerized Patient Record System used in Veteran Affairs medical centers (VAMCs). Much of patient information exists in written text: HCP notes, laboratory and radiology reports, medication records, etc. Natural language processing (NLP) allows platforms to sort through extensive volumes of data on complex patients at rates much faster than human capability, which has great potential to assist with diagnosis and treatment decisions.⁹

Medical literature is being produced at rates that exceed our ability to digest. More than 200,000 cancer-related articles were published in 2019 alone.¹⁴ NLP capabilities of AI have the potential to rapidly sort through this extensive medical literature and relate specific verbiage in patient records guiding therapy.⁴⁶ IBM Watson, a supercomputer based on ML and NLP, demonstrates this concept with many potential applications, only some of which relate to health care.^1,9 Watson has an oncology component to assimilate multiple aspects of patient care, including clinical notes, pathology results, radiograph findings, staging, and a tumor’s genetic profile. It coordinates these inputs from the EHR and mines medical literature and research databases to recommend treatment options.^1,46 AI can assess and compile far greater patient data and therapeutic options than would be feasible by individual clinicians, thus providing customized patient care.⁴⁷ Watson has partnered with numerous medical centers, including MD Anderson Cancer Center and Memorial Sloan Kettering Cancer Center, with variable success.^44,47-49 While the full potential of Watson appears not yet realized, these AI-driven approaches will likely play an important role in leveraging the hidden value in the expanding volume of health care information.

Medical Specialty Applications

Radiology

Currently > 70% of FDA-approved AI medical devices are in the field of radiology.² Most radiology departments have used AI-friendly digital imaging for years, such as the picture archiving and communication systems used by numerous health care systems, including VAMCs.^2,15 Gray-scale images common in radiology lend themselves to standardization, although AI is not limited to black-and- white image interpretation.¹⁵

An abundance of literature describes plain radiograph interpretation using AI. One FDA-approved platform improved X-ray diagnosis of wrist fractures when used by emergency medicine clinicians.^2,50 AI has been applied to chest X-ray (CXR) interpretation of many conditions, including pneumonia, tuberculosis, malignant lung lesions, and COVID-19.^{23,25,28,44,51-53} For example, Nam and colleagues suggested AI is better at diagnosing malignant pulmonary nodules from CXRs than are trained radiologists.²⁸

In addition to plain radiographs, AI has been applied to many other imaging technologies, including ultrasounds, positron emission tomography, mammograms, computed tomography (CT), and magnetic resonance imaging (MRI).^{15,26,44,48,54-56} A large study demonstrated that ML platforms significantly reduced the time to diagnose intracranial hemorrhages on CT and identified subtle hemorrhages missed by radiologists.⁵⁵ Other studies have claimed that AI programs may be better than radiologists in detecting cancer in screening mammograms, and 3 FDA-approved devices focus on mammogram interpretation.^2,15,54,57 There is also great interest in MRI applications to detect and predict prognosis for breast cancer based on imaging findings.^21,56

Aside from providing accurate diagnoses, other studies focus on AI radiograph interpretation to assist with patient screening, triage, improving time to final diagnosis, providing a rapid “second opinion,” and even monitoring disease progression and offering insights into prognosis.^{8,21,23,52,55,56,58} These features help in busy urban centers but may play an even greater role in areas with limited access to health care or trained specialists such as radiologists.⁵²

Cardiology

Cardiology has the second highest number of FDA-approved AI applications.² Many cardiology AI platforms involve image analysis, as described in several recent reviews.^45,59,60 AI has been applied to echocardiography to measure ejection fractions, detect valvular disease, and assess heart failure from hypertrophic and restrictive cardiomyopathy and amyloidosis.^45,48,59 Applications for cardiac CT scans and CT angiography have successfully quantified both calcified and noncalcified coronary artery plaques and lumen assessments, assessed myocardial perfusion, and performed coronary artery calcium scoring.^45,59,60 Likewise, AI applications for cardiac MRI have been used to quantitate ejection fraction, large vessel flow assessment, and cardiac scar burden.^45,59

For years ECG devices have provided interpretation with limited accuracy using preprogrammed parameters.⁴⁸ However, the application of AI allows ECG interpretation on par with trained cardiologists. Numerous such AI applications exist, and 2 FDA-approved devices perform ECG interpretation.^2,61-64 One of these devices incorporates an AI-powered stethoscope to detect atrial fibrillation and heart murmurs.⁶⁵

Pathology

The advancement of whole slide imaging, wherein entire slides can be scanned and digitized at high speed and resolution, creates great potential for AI applications in pathology.^{12,24,32,33,66} A landmark study demonstrating the potential of AI for assessing whole slide imaging examined sentinel lymph node metastases in patients with breast cancer.²² Multiple algorithms in the study demonstrated that AI was equivalent or better than pathologists in detecting metastases, especially when the pathologists were time-constrained consistent with a normal working environment. Significantly, the most accurate and efficient diagnoses were achieved when the pathologist and AI interpretations were used together.^22,33

AI has shown promise in diagnosing many other entities, including cancers of the prostate (including Gleason scoring), lung, colon, breast, and skin.^{11,12,24,27,32,67} In addition, AI has shown great potential in scoring biomarkers important for prognosis and treatment, such as immunohistochemistry (IHC) labeling of Ki-67 and PD-L1.³² Pathologists can have difficulty classifying certain tumors or determining the site of origin for metastases, often having to rely on IHC with limited success. The unique features of image analysis with AI have the potential to assist in classifying difficult tumors and identifying sites of origin for metastatic disease based on morphology alone.¹¹

Oncology depends heavily on molecular pathology testing to dictate treatment options and determine prognosis. Preliminary studies suggest that AI interpretation alone has the potential to delineate whether certain molecular mutations are present in tumors from various sites.^11,14,24,32 One study combined histology and genomic results for AI interpretation that improved prognostic predictions.⁶⁸ In addition, AI analysis may have potential in predicting tumor recurrence or prognosis based on cellular features, as demonstrated for lung cancer and melanoma.^67,69,70

Ophthalmology

AI applications for ophthalmology have focused on diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related and congenital cataracts, and retinal vein occlusion.^71-73 Diabetic retinopathy is a leading cause of blindness and has been studied by numerous platforms with good success, most having used color fundus photography.^71,72 One study showed AI could diagnose diabetic retinopathy and diabetic macular edema with specificities similar to ophthalmologists.⁷⁴ In 2018, the FDA approved the AI platform IDx-DR. This diagnostic system classifies retinal images and recommends referral for patients determined to have “more than mild diabetic retinopathy” and reexamination within a year for other patients.^8,75 Significantly, the platform recommendations do not require confirmation by a clinician.⁸

AI has been applied to other modalities in ophthalmology such as optical coherence tomography (OCT) to diagnose retinal disease and to predict appropriate management of congenital cataracts.^25,73,76 For example, an AI application using OCT has been demonstrated to match or exceed the accuracy of retinal experts in diagnosing and triaging patients with a variety of retinal pathologies, including patients needing urgent referrals.⁷⁷

Dermatology

Multiple studies demonstrate AI performs at least equal to experienced dermatologists in differentiating selected skin lesions.^78-81 For example, Esteva and colleagues demonstrated AI could differentiate keratinocyte carcinomas from benign seborrheic keratoses and malignant melanomas from benign nevi with accuracy equal to 21 board-certified dermatologists.⁷⁸

AI is applicable to various imaging procedures common to dermatology, such as dermoscopy, very high-frequency ultrasound, and reflectance confocal microscopy.⁸² Several studies have demonstrated that AI interpretation compared favorably to dermatologists evaluating dermoscopy to assess melanocytic lesions.^78-81,83

A limitation in these studies is that they differentiate only a few diagnoses.⁸² Furthermore, dermatologists have sensory input such as touch and visual examination under various conditions, something AI has yet to replicate.^15,34,84 Also, most AI devices use no or limited clinical information.⁸¹ Dermatologists can recognize rarer conditions for which AI models may have had limited or no training.³⁴ Nevertheless, a recent study assessed AI for the diagnosis of 134 separate skin disorders with promising results, including providing diagnoses with accuracy comparable to that of dermatologists and providing accurate treatment strategies.⁸⁴ As Topol points out, most skin lesions are diagnosed in the primary care setting where AI can have a greater impact when used in conjunction with the clinical impression, especially where specialists are in limited supply.^48,78

Finally, dermatology lends itself to using portable or smartphone applications (apps) wherein the user can photograph a lesion for analysis by AI algorithms to assess the need for further evaluation or make treatment recommendations.^34,84,85 Although results from currently available apps are not encouraging, they may play a greater role as the technology advances.^34,85

Oncology

Applications of AI in oncology include predicting prognosis for patients with cancer based on histologic and/or genetic information.^14,68,86 Programs can predict the risk of complications before and recurrence risks after surgery for malignancies.^44,87-89 AI can also assist in treatment planning and predict treatment failure with radiation therapy.^90,91

AI has great potential in processing the large volumes of patient data in cancer genomics. Next-generation sequencing has allowed for the identification of millions of DNA sequences in a single tumor to detect genetic anomalies.⁹² Thousands of mutations can be found in individual tumor samples, and processing this information and determining its significance can be beyond human capability.¹⁴ We know little about the effects of various mutation combinations, and most tumors have a heterogeneous molecular profile among different cell populations.^14,93 The presence or absence of various mutations can have diagnostic, prognostic, and therapeutic implications.⁹³ AI has great potential to sort through these complex data and identify actionable findings.

More than 200,000 cancer-related articles were published in 2019, and publications in the field of cancer genomics are increasing exponentially.^14,92,93 Patel and colleagues assessed the utility of IBM Watson for Genomics against results from a molecular tumor board.⁹³ Watson for Genomics identified potentially significant mutations not identified by the tumor board in 32% of patients. Most mutations were related to new clinical trials not yet added to the tumor board watch list, demonstrating the role AI will have in processing the large volume of genetic data required to deliver personalized medicine moving forward.

Gastroenterology

AI has shown promise in predicting risk or outcomes based on clinical parameters in various common gastroenterology problems, including gastric reflux, acute pancreatitis, gastrointestinal bleeding, celiac disease, and inflammatory bowel disease.^94,95 AI endoscopic analysis has demonstrated potential in assessing Barrett’s esophagus, gastric Helicobacter pylori infections, gastric atrophy, and gastric intestinal metaplasia.⁹⁵ Applications have been used to assess esophageal, gastric, and colonic malignancies, including depth of invasion based on endoscopic images.⁹⁵ Finally, studies have evaluated AI to assess small colon polyps during colonoscopy, including differentiating benign and premalignant polyps with success comparable to gastroenterologists.^94,95 AI has been shown to increase the speed and accuracy of gastroenterologists in detecting small polyps during colonoscopy.⁴⁸ In a prospective randomized study, colonoscopies performed using an AI device identified significantly more small adenomatous polyps than colonoscopies without AI.⁹⁶

Neurology

It has been suggested that AI technologies are well suited for application in neurology due to the subtle presentation of many neurologic diseases.¹⁶ Viz LVO, the first CMS-approved AI reimbursement for the diagnosis of strokes, analyzes CTs to detect early ischemic strokes and alerts the medical team, thus shortening time to treatment.^3,97 Many other AI platforms are in use or development that use CT and MRI for the early detection of strokes as well as for treatment and prognosis.^9,97

AI technologies have been applied to neurodegenerative diseases, such as Alzheimer and Parkinson diseases.^16,98 For example, several studies have evaluated patient movements in Parkinson disease for both early diagnosis and to assess response to treatment.⁹⁸ These evaluations included assessment with both external cameras as well as wearable devices and smartphone apps.

Mental Health

Due to the interactive nature of mental health care, the field has been slower to develop AI applications.¹⁸ With heavy reliance on textual information (eg, clinic notes, mood rating scales, and documentation of conversations), successful AI applications in this field will likely rely heavily on NLP.¹⁸ However, studies investigating the application of AI to mental health have also incorporated data such as brain imaging, smartphone monitoring, and social media platforms, such as Facebook and Twitter.^18,103,104

The risk of suicide is higher in veteran patients, and ML algorithms have had limited success in predicting suicide risk in both veteran and nonveteran populations.^104-106 While early models have low positive predictive values and low sensitivities, they still promise to be a useful tool in conjunction with traditional risk assessments.¹⁰⁶ Kessler and colleagues suggest that combining multiple rather than single ML algorithms might lead to greater success.^105,106

AI may assist in diagnosing other mental health disorders, including major depressive disorder, attention deficit hyperactivity disorder (ADHD), schizophrenia, posttraumatic stress disorder, and Alzheimer disease.^103,104,107 These investigations are in the early stages with limited clinical applicability. However, 2 AI applications awaiting FDA approval relate to ADHD and opioid use.² Furthermore, potential exists for AI to not only assist with prevention and diagnosis of ADHD, but also to identify optimal treatment options.^2,103

General and Personalized Medicine

Additional AI applications include diagnosing patients with suspected sepsis, measuring liver iron concentrations, predicting hospital mortality at the time of admission, and more.^2,108,109 AI can guide end-of-life decisions such as resuscitation status or whether to initiate mechanical ventilation.⁴⁸

AI-driven smartphone apps can be beneficial to both patients and clinicians. Examples include predicting nonadherence to anticoagulation therapy, monitoring heart rhythms for atrial fibrillation or signs of hyperkalemia in patients with renal failure, and improving outcomes for patients with diabetes mellitus by decreasing glycemic variability and reducing hypoglycemia.^8,48,110,111 The potential for AI applications to health care and personalized medicine are almost limitless.

Discussion

With ever-increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has the potential to have numerous impacts. AI can improve diagnostic accuracy while limiting errors and impact patient safety such as assisting with prescription delivery.^8,9,34 It can screen and triage patients, alerting clinicians to those needing more urgent evaluation.^8,23,77,97 AI also may increase a clinician’s efficiency and speed to render a diagnosis.^12,13,55,97 AI can provide a rapid second opinion, an ability especially beneficial in underserved areas with shortages of specialists.^{23,25,26,29,34} Similarly, AI may decrease the inter- and intraobserver variability common in many medical specialties.^12,27,45 AI applications can also monitor disease progression, identifying patients at greatest risk, and provide information for prognosis.^21,23,56,58 Finally, as described with applications using IBM Watson, AI can allow for an integrated approach to health care that is currently lacking.

We have described many reports suggesting AI can render diagnoses as well as or better than experienced clinicians, and speculation exists that AI will replace many roles currently performed by health care practitioners.^9,26 However, most studies demonstrate that AI’s diagnostic benefits are best realized when used to supplement a clinician’s impression.^{8,22,30,33,52,54,56,69,84} AI is not likely to replace humans in health care in the foreseeable future. The technology can be likened to the impact of CT scans developed in the 1970s in neurology. Prior to such detailed imaging, neurologists spent extensive time performing detailed physicals to render diagnoses and locate lesions before surgery. There was mistrust of this new technology and concern that CT scans would eliminate the need for neurologists.¹¹² On the contrary, neurology is alive and well, frequently being augmented by the technologies once speculated to replace it.

Commercial AI health care platforms represented a $2 billion industry in 2018 and are growing rapidly each year.^13,32 Many AI products are offered ready for implementation for various tasks, including diagnostics, patient management, and improved efficiency. Others will likely be provided as templates suitable for modification to meet the specific needs of the facility, practice, or specialty for its patient population.

AI Risks and Limitations

AI has several risks and limitations. Although there is progress in explainable AI, at times we still struggle to understand how the output provided by machine learning algorithms was created.^44,48 The many layers associated with deep learning self-determine the criteria to reach its conclusion, and these criteria can continually evolve. The parameters of deep learning are not preprogrammed, and there are too many individual data points to be extrapolated or deconvoluted for evaluation at our current level of knowledge.^26,51 These apparent lack of constraints cause concern for patient safety and suggest that greater validation and continued scrutiny of validity is required.^8,48 Efforts are underway to create explainable AI programs to make their processes more transparent, but such clarification is limited presently.^14,26,48,77

Another challenge of AI is determining the amount of training data required to function optimally. Also, if the output describes multiple variables or diagnoses, are each equally valid?¹¹³ Furthermore, many AI applications look for a specific process, such as cancer diagnoses on CXRs. However, how coexisting conditions like cardiomegaly, emphysema, pneumonia, etc, seen on CXRs will affect the diagnosis needs to be considered.^51,52 Zech and colleagues provide the example that diagnoses for pneumothorax are frequently rendered on CXRs with chest tubes in place.⁵¹ They suggest that CNNs may develop a bias toward diagnosing pneumothorax when chest tubes are present. Many current studies approach an issue in isolation, a situation not realistic in real-world clinical practice.²⁶

Most studies on AI have been retrospective, and frequently data used to train the program are preselected.^13,26 The data are typically validated on available databases rather than actual patients in the clinical setting, limiting confidence in the validity of the AI output when applied to real-world situations. Currently, fewer than 12 prospective trials had been published comparing AI with traditional clinical care.^13,114 Randomized prospective clinical trials are even fewer, with none currently reported from the United States.^13,114 The results from several studies have been shown to diminish when repeated prospectively.¹¹⁴

The FDA has created a new category known as Software as a Medical Device and has a Digital Health Innovation Action Plan to regulate AI platforms. Still, the process of AI regulation is of necessity different from traditional approval processes and is continually evolving.⁸ The FDA approval process cannot account for the fact that the program’s parameters may continually evolve or adapt.²

Guidelines for investigating and reporting AI research with its unique attributes are being developed. Examples include the TRIPOD-ML statement and others.^49,115 In September 2020, 2 publications addressed the paucity of gold-standard randomized clinical trials in clinical AI applications.^116,117 The SPIRIT-AI statement expands on the original SPIRIT statement published in 2013 to guide minimal reporting standards for AI clinical trial protocols to promote transparency of design and methodology.¹¹⁶ Similarly, the CONSORT-AI extension, stemming from the original CONSORT statement in 1996, aims to ensure quality reporting of randomized controlled trials in AI.¹¹⁷

Another risk with AI is that while an individual physician making a mistake may adversely affect 1 patient, a single mistake in an AI algorithm could potentially affect thousands of patients.⁴⁸ Also, AI programs developed for patient populations at a facility may not translate to another. Referred to as overfitting, this phenomenon relates to selection bias in training data sets.^{15,34,49,51,52} Studies have shown that programs that underrepresent certain group characteristics such as age, sex, or race may be less effective when applied to a population in which these characteristics have differing representations.^8,48,49 This problem of underrepresentation has been demonstrated in programs interpreting pathology slides, radiographs, and skin lesions.^15,32,51

Admittedly, most of these challenges are not specific to AI and existed in health care previously. Physicians make mistakes, treatments are sometimes used without adequate prospective studies, and medications are given without understanding their mechanism of action, much like AI-facilitated processes reach a conclusion that cannot be fully explained.⁴⁸

Conclusions

The view that AI will dramatically impact health care in the coming years will likely prove true. However, much work is needed, especially because of the paucity of prospective clinical trials as has been historically required in medical research. Any concern that AI will replace HCPs seems unwarranted. Early studies suggest that even AI programs that appear to exceed human interpretation perform best when working in cooperation with and oversight from clinicians. AI’s greatest potential appears to be its ability to augment care from health professionals, improving efficiency and accuracy, and should be anticipated with enthusiasm as the field moves forward at an exponential rate.

Acknowledgments

The authors thank Makenna G. Thomas for proofreading and review of the manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital. This research has been approved by the James A. Haley Veteran’s Hospital Office of Communications and Media.

Artificial Intelligence (AI) was first described in 1956 and refers to machines having the ability to learn as they receive and process information, resulting in the ability to “think” like humans.¹ AI’s impact in medicine is increasing; currently, at least 29 AI medical devices and algorithms are approved by the US Food and Drug Administration (FDA) in a variety of areas, including radiograph interpretation, managing glucose levels in patients with diabetes mellitus, analyzing electrocardiograms (ECGs), and diagnosing sleep disorders among others.² Significantly, in 2020, the Centers for Medicare and Medicaid Services (CMS) announced the first reimbursement to hospitals for an AI platform, a model for early detection of strokes.³ AI is rapidly becoming an integral part of health care, and its role will only increase in the future (Table).

As knowledge in medicine is expanding exponentially, AI has great potential to assist with handling complex patient care data. The concept of exponential growth is not a natural one. As Bini described, with exponential growth the volume of knowledge amassed over the past 10 years will now occur in perhaps only 1 year.¹ Likewise, equivalent advances over the past year may take just a few months. This phenomenon is partly due to the law of accelerating returns, which states that advances feed on themselves, continually increasing the rate of further advances.⁴ The volume of medical data doubles every 2 to 5 years.⁵ Fortunately, the field of AI is growing exponentially as well and can help health care practitioners (HCPs) keep pace, allowing the continued delivery of effective health care.

In this report, we review common terminology, principles, and general applications of AI, followed by current and potential applications of AI for selected medical specialties. Finally, we discuss AI’s future in health care, along with potential risks and pitfalls.

AI Overview

AI refers to machine programs that can “learn” or think based on past experiences. This functionality contrasts with simple rules-based programming available to health care for years. An example of rules-based programming is the warfarindosing.org website developed by Barnes-Jewish Hospital at Washington University Medical Center, which guides initial warfarin dosing.^6,7 The prescriber inputs detailed patient information, including age, sex, height, weight, tobacco history, medications, laboratory results, and genotype if available. The application then calculates recommended warfarin dosing regimens to avoid over- or underanticoagulation. While the dosing algorithm may be complex, it depends entirely on preprogrammed rules. The program does not learn to reach its conclusions and recommendations from patient data.

In contrast, one of the most common subsets of AI is machine learning (ML). ML describes a program that “learns from experience and improves its performance as it learns.”¹ With ML, the computer is initially provided with a training data set—data with known outcomes or labels. Because the initial data are input from known samples, this type of AI is known as supervised learning.^8-10 As an example, we recently reported using ML to diagnose various types of cancer from pathology slides.¹¹ In one experiment, we captured images of colon adenocarcinoma and normal colon (these 2 groups represent the training data set). Unlike traditional programming, we did not define characteristics that would differentiate colon cancer from normal; rather, the machine learned these characteristics independently by assessing the labeled images provided. A second data set (the validation data set) was used to evaluate the program and fine-tune the ML training model’s parameters. Finally, the program was presented with new images of cancer and normal cases for final assessment of accuracy (test data set). Our program learned to recognize differences from the images provided and was able to differentiate normal and cancer images with > 95% accuracy.

Advances in computer processing have allowed for the development of artificial neural networks (ANNs). While there are several types of ANNs, the most common types used for image classification and segmentation are known as convolutional neural networks (CNNs).^9,12-14 The programs are designed to work similar to the human brain, specifically the visual cortex.^15,16 As data are acquired, they are processed by various layers in the program. Much like neurons in the brain, one layer decides whether to advance information to the next.^13,14 CNNs can be many layers deep, leading to the term deep learning: “computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.”^1,13,17

ANNs can process larger volumes of data. This advance has led to the development of unstructured or unsupervised learning. With this type of learning, imputing defined features (ie, predetermined answers) of the training data set described above is no longer required.^1,8,10,14 The advantage of unsupervised learning is that the program can be presented raw data and extract meaningful interpretation without human input, often with less bias than may exist with supervised learning.^1,18 If shown enough data, the program can extract relevant features to make conclusions independently without predefined definitions, potentially uncovering markers not previously known. For example, several studies have used unsupervised learning to search patient data to assess readmission risks of patients with congestive heart failure.^10,19,20 AI compiled features independently and not previously defined, predicting patients at greater risk for readmission superior to traditional methods.

Health Care Applications

Image analysis has seen the most AI health care applications.^8,15 AI has shown potential in interpreting many types of medical images, including pathology slides, radiographs of various types, retina and other eye scans, and photographs of skin lesions. Many studies have demonstrated that AI can interpret these images as accurately as or even better than experienced clinicians.^9,13,22-29 Studies have suggested AI interpretation of radiographs may better distinguish patients infected with COVID-19 from other causes of pneumonia, and AI interpretation of pathology slides may detect specific genetic mutations not previously identified without additional molecular tests.^{11,14,23,24,30-32}

The second area in which AI can impact health care is improving workflow and efficiency. AI has improved surgery scheduling, saving significant revenue, and decreased patient wait times for appointments.¹ AI can screen and triage radiographs, allowing attention to be directed to critical patients. This use would be valuable in many busy clinical settings, such as the recent COVID-19 pandemic.^8,23 Similarly, AI can screen retina images to prioritize urgent conditions.²⁵ AI has improved pathologists’ efficiency when used to detect breast metastases.³³ Finally, AI may reduce medical errors, thereby ensuring patient safety.^8,9,34

A third health care benefit of AI is in public health and epidemiology. AI can assist with clinical decision-making and diagnoses in low-income countries and areas with limited health care resources and personnel.^25,29 AI can improve identification of infectious outbreaks, such as tuberculosis, malaria, dengue fever, and influenza.^29,35-40 AI has been used to predict transmission patterns of the Zika virus and the current COVID-19 pandemic.^41,42 Applications can stratify the risk of outbreaks based on multiple factors, including age, income, race, atypical geographic clusters, and seasonal factors like rainfall and temperature.^35,36,38,43 AI has been used to assess morbidity and mortality, such as predicting disease severity with malaria and identifying treatment failures in tuberculosis.²⁹

Finally, AI can dramatically impact health care due to processing large data sets or disconnected volumes of patient information—so-called big data.^44-46 An example is the widespread use of electronic health records (EHRs) such as the Computerized Patient Record System used in Veteran Affairs medical centers (VAMCs). Much of patient information exists in written text: HCP notes, laboratory and radiology reports, medication records, etc. Natural language processing (NLP) allows platforms to sort through extensive volumes of data on complex patients at rates much faster than human capability, which has great potential to assist with diagnosis and treatment decisions.⁹

Medical literature is being produced at rates that exceed our ability to digest. More than 200,000 cancer-related articles were published in 2019 alone.¹⁴ NLP capabilities of AI have the potential to rapidly sort through this extensive medical literature and relate specific verbiage in patient records guiding therapy.⁴⁶ IBM Watson, a supercomputer based on ML and NLP, demonstrates this concept with many potential applications, only some of which relate to health care.^1,9 Watson has an oncology component to assimilate multiple aspects of patient care, including clinical notes, pathology results, radiograph findings, staging, and a tumor’s genetic profile. It coordinates these inputs from the EHR and mines medical literature and research databases to recommend treatment options.^1,46 AI can assess and compile far greater patient data and therapeutic options than would be feasible by individual clinicians, thus providing customized patient care.⁴⁷ Watson has partnered with numerous medical centers, including MD Anderson Cancer Center and Memorial Sloan Kettering Cancer Center, with variable success.^44,47-49 While the full potential of Watson appears not yet realized, these AI-driven approaches will likely play an important role in leveraging the hidden value in the expanding volume of health care information.

Medical Specialty Applications

Radiology

Currently > 70% of FDA-approved AI medical devices are in the field of radiology.² Most radiology departments have used AI-friendly digital imaging for years, such as the picture archiving and communication systems used by numerous health care systems, including VAMCs.^2,15 Gray-scale images common in radiology lend themselves to standardization, although AI is not limited to black-and- white image interpretation.¹⁵

An abundance of literature describes plain radiograph interpretation using AI. One FDA-approved platform improved X-ray diagnosis of wrist fractures when used by emergency medicine clinicians.^2,50 AI has been applied to chest X-ray (CXR) interpretation of many conditions, including pneumonia, tuberculosis, malignant lung lesions, and COVID-19.^{23,25,28,44,51-53} For example, Nam and colleagues suggested AI is better at diagnosing malignant pulmonary nodules from CXRs than are trained radiologists.²⁸

In addition to plain radiographs, AI has been applied to many other imaging technologies, including ultrasounds, positron emission tomography, mammograms, computed tomography (CT), and magnetic resonance imaging (MRI).^{15,26,44,48,54-56} A large study demonstrated that ML platforms significantly reduced the time to diagnose intracranial hemorrhages on CT and identified subtle hemorrhages missed by radiologists.⁵⁵ Other studies have claimed that AI programs may be better than radiologists in detecting cancer in screening mammograms, and 3 FDA-approved devices focus on mammogram interpretation.^2,15,54,57 There is also great interest in MRI applications to detect and predict prognosis for breast cancer based on imaging findings.^21,56

Aside from providing accurate diagnoses, other studies focus on AI radiograph interpretation to assist with patient screening, triage, improving time to final diagnosis, providing a rapid “second opinion,” and even monitoring disease progression and offering insights into prognosis.^{8,21,23,52,55,56,58} These features help in busy urban centers but may play an even greater role in areas with limited access to health care or trained specialists such as radiologists.⁵²

Cardiology

Cardiology has the second highest number of FDA-approved AI applications.² Many cardiology AI platforms involve image analysis, as described in several recent reviews.^45,59,60 AI has been applied to echocardiography to measure ejection fractions, detect valvular disease, and assess heart failure from hypertrophic and restrictive cardiomyopathy and amyloidosis.^45,48,59 Applications for cardiac CT scans and CT angiography have successfully quantified both calcified and noncalcified coronary artery plaques and lumen assessments, assessed myocardial perfusion, and performed coronary artery calcium scoring.^45,59,60 Likewise, AI applications for cardiac MRI have been used to quantitate ejection fraction, large vessel flow assessment, and cardiac scar burden.^45,59

For years ECG devices have provided interpretation with limited accuracy using preprogrammed parameters.⁴⁸ However, the application of AI allows ECG interpretation on par with trained cardiologists. Numerous such AI applications exist, and 2 FDA-approved devices perform ECG interpretation.^2,61-64 One of these devices incorporates an AI-powered stethoscope to detect atrial fibrillation and heart murmurs.⁶⁵

Pathology

The advancement of whole slide imaging, wherein entire slides can be scanned and digitized at high speed and resolution, creates great potential for AI applications in pathology.^{12,24,32,33,66} A landmark study demonstrating the potential of AI for assessing whole slide imaging examined sentinel lymph node metastases in patients with breast cancer.²² Multiple algorithms in the study demonstrated that AI was equivalent or better than pathologists in detecting metastases, especially when the pathologists were time-constrained consistent with a normal working environment. Significantly, the most accurate and efficient diagnoses were achieved when the pathologist and AI interpretations were used together.^22,33

AI has shown promise in diagnosing many other entities, including cancers of the prostate (including Gleason scoring), lung, colon, breast, and skin.^{11,12,24,27,32,67} In addition, AI has shown great potential in scoring biomarkers important for prognosis and treatment, such as immunohistochemistry (IHC) labeling of Ki-67 and PD-L1.³² Pathologists can have difficulty classifying certain tumors or determining the site of origin for metastases, often having to rely on IHC with limited success. The unique features of image analysis with AI have the potential to assist in classifying difficult tumors and identifying sites of origin for metastatic disease based on morphology alone.¹¹

Oncology depends heavily on molecular pathology testing to dictate treatment options and determine prognosis. Preliminary studies suggest that AI interpretation alone has the potential to delineate whether certain molecular mutations are present in tumors from various sites.^11,14,24,32 One study combined histology and genomic results for AI interpretation that improved prognostic predictions.⁶⁸ In addition, AI analysis may have potential in predicting tumor recurrence or prognosis based on cellular features, as demonstrated for lung cancer and melanoma.^67,69,70

Ophthalmology

AI applications for ophthalmology have focused on diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related and congenital cataracts, and retinal vein occlusion.^71-73 Diabetic retinopathy is a leading cause of blindness and has been studied by numerous platforms with good success, most having used color fundus photography.^71,72 One study showed AI could diagnose diabetic retinopathy and diabetic macular edema with specificities similar to ophthalmologists.⁷⁴ In 2018, the FDA approved the AI platform IDx-DR. This diagnostic system classifies retinal images and recommends referral for patients determined to have “more than mild diabetic retinopathy” and reexamination within a year for other patients.^8,75 Significantly, the platform recommendations do not require confirmation by a clinician.⁸

AI has been applied to other modalities in ophthalmology such as optical coherence tomography (OCT) to diagnose retinal disease and to predict appropriate management of congenital cataracts.^25,73,76 For example, an AI application using OCT has been demonstrated to match or exceed the accuracy of retinal experts in diagnosing and triaging patients with a variety of retinal pathologies, including patients needing urgent referrals.⁷⁷

Dermatology

Multiple studies demonstrate AI performs at least equal to experienced dermatologists in differentiating selected skin lesions.^78-81 For example, Esteva and colleagues demonstrated AI could differentiate keratinocyte carcinomas from benign seborrheic keratoses and malignant melanomas from benign nevi with accuracy equal to 21 board-certified dermatologists.⁷⁸

AI is applicable to various imaging procedures common to dermatology, such as dermoscopy, very high-frequency ultrasound, and reflectance confocal microscopy.⁸² Several studies have demonstrated that AI interpretation compared favorably to dermatologists evaluating dermoscopy to assess melanocytic lesions.^78-81,83

A limitation in these studies is that they differentiate only a few diagnoses.⁸² Furthermore, dermatologists have sensory input such as touch and visual examination under various conditions, something AI has yet to replicate.^15,34,84 Also, most AI devices use no or limited clinical information.⁸¹ Dermatologists can recognize rarer conditions for which AI models may have had limited or no training.³⁴ Nevertheless, a recent study assessed AI for the diagnosis of 134 separate skin disorders with promising results, including providing diagnoses with accuracy comparable to that of dermatologists and providing accurate treatment strategies.⁸⁴ As Topol points out, most skin lesions are diagnosed in the primary care setting where AI can have a greater impact when used in conjunction with the clinical impression, especially where specialists are in limited supply.^48,78

Finally, dermatology lends itself to using portable or smartphone applications (apps) wherein the user can photograph a lesion for analysis by AI algorithms to assess the need for further evaluation or make treatment recommendations.^34,84,85 Although results from currently available apps are not encouraging, they may play a greater role as the technology advances.^34,85

Oncology

Applications of AI in oncology include predicting prognosis for patients with cancer based on histologic and/or genetic information.^14,68,86 Programs can predict the risk of complications before and recurrence risks after surgery for malignancies.^44,87-89 AI can also assist in treatment planning and predict treatment failure with radiation therapy.^90,91

AI has great potential in processing the large volumes of patient data in cancer genomics. Next-generation sequencing has allowed for the identification of millions of DNA sequences in a single tumor to detect genetic anomalies.⁹² Thousands of mutations can be found in individual tumor samples, and processing this information and determining its significance can be beyond human capability.¹⁴ We know little about the effects of various mutation combinations, and most tumors have a heterogeneous molecular profile among different cell populations.^14,93 The presence or absence of various mutations can have diagnostic, prognostic, and therapeutic implications.⁹³ AI has great potential to sort through these complex data and identify actionable findings.

More than 200,000 cancer-related articles were published in 2019, and publications in the field of cancer genomics are increasing exponentially.^14,92,93 Patel and colleagues assessed the utility of IBM Watson for Genomics against results from a molecular tumor board.⁹³ Watson for Genomics identified potentially significant mutations not identified by the tumor board in 32% of patients. Most mutations were related to new clinical trials not yet added to the tumor board watch list, demonstrating the role AI will have in processing the large volume of genetic data required to deliver personalized medicine moving forward.

Gastroenterology

AI has shown promise in predicting risk or outcomes based on clinical parameters in various common gastroenterology problems, including gastric reflux, acute pancreatitis, gastrointestinal bleeding, celiac disease, and inflammatory bowel disease.^94,95 AI endoscopic analysis has demonstrated potential in assessing Barrett’s esophagus, gastric Helicobacter pylori infections, gastric atrophy, and gastric intestinal metaplasia.⁹⁵ Applications have been used to assess esophageal, gastric, and colonic malignancies, including depth of invasion based on endoscopic images.⁹⁵ Finally, studies have evaluated AI to assess small colon polyps during colonoscopy, including differentiating benign and premalignant polyps with success comparable to gastroenterologists.^94,95 AI has been shown to increase the speed and accuracy of gastroenterologists in detecting small polyps during colonoscopy.⁴⁸ In a prospective randomized study, colonoscopies performed using an AI device identified significantly more small adenomatous polyps than colonoscopies without AI.⁹⁶

Neurology

It has been suggested that AI technologies are well suited for application in neurology due to the subtle presentation of many neurologic diseases.¹⁶ Viz LVO, the first CMS-approved AI reimbursement for the diagnosis of strokes, analyzes CTs to detect early ischemic strokes and alerts the medical team, thus shortening time to treatment.^3,97 Many other AI platforms are in use or development that use CT and MRI for the early detection of strokes as well as for treatment and prognosis.^9,97

AI technologies have been applied to neurodegenerative diseases, such as Alzheimer and Parkinson diseases.^16,98 For example, several studies have evaluated patient movements in Parkinson disease for both early diagnosis and to assess response to treatment.⁹⁸ These evaluations included assessment with both external cameras as well as wearable devices and smartphone apps.

Mental Health

Due to the interactive nature of mental health care, the field has been slower to develop AI applications.¹⁸ With heavy reliance on textual information (eg, clinic notes, mood rating scales, and documentation of conversations), successful AI applications in this field will likely rely heavily on NLP.¹⁸ However, studies investigating the application of AI to mental health have also incorporated data such as brain imaging, smartphone monitoring, and social media platforms, such as Facebook and Twitter.^18,103,104

The risk of suicide is higher in veteran patients, and ML algorithms have had limited success in predicting suicide risk in both veteran and nonveteran populations.^104-106 While early models have low positive predictive values and low sensitivities, they still promise to be a useful tool in conjunction with traditional risk assessments.¹⁰⁶ Kessler and colleagues suggest that combining multiple rather than single ML algorithms might lead to greater success.^105,106

AI may assist in diagnosing other mental health disorders, including major depressive disorder, attention deficit hyperactivity disorder (ADHD), schizophrenia, posttraumatic stress disorder, and Alzheimer disease.^103,104,107 These investigations are in the early stages with limited clinical applicability. However, 2 AI applications awaiting FDA approval relate to ADHD and opioid use.² Furthermore, potential exists for AI to not only assist with prevention and diagnosis of ADHD, but also to identify optimal treatment options.^2,103

General and Personalized Medicine

Additional AI applications include diagnosing patients with suspected sepsis, measuring liver iron concentrations, predicting hospital mortality at the time of admission, and more.^2,108,109 AI can guide end-of-life decisions such as resuscitation status or whether to initiate mechanical ventilation.⁴⁸

AI-driven smartphone apps can be beneficial to both patients and clinicians. Examples include predicting nonadherence to anticoagulation therapy, monitoring heart rhythms for atrial fibrillation or signs of hyperkalemia in patients with renal failure, and improving outcomes for patients with diabetes mellitus by decreasing glycemic variability and reducing hypoglycemia.^8,48,110,111 The potential for AI applications to health care and personalized medicine are almost limitless.

Discussion

With ever-increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has the potential to have numerous impacts. AI can improve diagnostic accuracy while limiting errors and impact patient safety such as assisting with prescription delivery.^8,9,34 It can screen and triage patients, alerting clinicians to those needing more urgent evaluation.^8,23,77,97 AI also may increase a clinician’s efficiency and speed to render a diagnosis.^12,13,55,97 AI can provide a rapid second opinion, an ability especially beneficial in underserved areas with shortages of specialists.^{23,25,26,29,34} Similarly, AI may decrease the inter- and intraobserver variability common in many medical specialties.^12,27,45 AI applications can also monitor disease progression, identifying patients at greatest risk, and provide information for prognosis.^21,23,56,58 Finally, as described with applications using IBM Watson, AI can allow for an integrated approach to health care that is currently lacking.

We have described many reports suggesting AI can render diagnoses as well as or better than experienced clinicians, and speculation exists that AI will replace many roles currently performed by health care practitioners.^9,26 However, most studies demonstrate that AI’s diagnostic benefits are best realized when used to supplement a clinician’s impression.^{8,22,30,33,52,54,56,69,84} AI is not likely to replace humans in health care in the foreseeable future. The technology can be likened to the impact of CT scans developed in the 1970s in neurology. Prior to such detailed imaging, neurologists spent extensive time performing detailed physicals to render diagnoses and locate lesions before surgery. There was mistrust of this new technology and concern that CT scans would eliminate the need for neurologists.¹¹² On the contrary, neurology is alive and well, frequently being augmented by the technologies once speculated to replace it.

Commercial AI health care platforms represented a $2 billion industry in 2018 and are growing rapidly each year.^13,32 Many AI products are offered ready for implementation for various tasks, including diagnostics, patient management, and improved efficiency. Others will likely be provided as templates suitable for modification to meet the specific needs of the facility, practice, or specialty for its patient population.

AI Risks and Limitations

AI has several risks and limitations. Although there is progress in explainable AI, at times we still struggle to understand how the output provided by machine learning algorithms was created.^44,48 The many layers associated with deep learning self-determine the criteria to reach its conclusion, and these criteria can continually evolve. The parameters of deep learning are not preprogrammed, and there are too many individual data points to be extrapolated or deconvoluted for evaluation at our current level of knowledge.^26,51 These apparent lack of constraints cause concern for patient safety and suggest that greater validation and continued scrutiny of validity is required.^8,48 Efforts are underway to create explainable AI programs to make their processes more transparent, but such clarification is limited presently.^14,26,48,77

Another challenge of AI is determining the amount of training data required to function optimally. Also, if the output describes multiple variables or diagnoses, are each equally valid?¹¹³ Furthermore, many AI applications look for a specific process, such as cancer diagnoses on CXRs. However, how coexisting conditions like cardiomegaly, emphysema, pneumonia, etc, seen on CXRs will affect the diagnosis needs to be considered.^51,52 Zech and colleagues provide the example that diagnoses for pneumothorax are frequently rendered on CXRs with chest tubes in place.⁵¹ They suggest that CNNs may develop a bias toward diagnosing pneumothorax when chest tubes are present. Many current studies approach an issue in isolation, a situation not realistic in real-world clinical practice.²⁶

Most studies on AI have been retrospective, and frequently data used to train the program are preselected.^13,26 The data are typically validated on available databases rather than actual patients in the clinical setting, limiting confidence in the validity of the AI output when applied to real-world situations. Currently, fewer than 12 prospective trials had been published comparing AI with traditional clinical care.^13,114 Randomized prospective clinical trials are even fewer, with none currently reported from the United States.^13,114 The results from several studies have been shown to diminish when repeated prospectively.¹¹⁴

The FDA has created a new category known as Software as a Medical Device and has a Digital Health Innovation Action Plan to regulate AI platforms. Still, the process of AI regulation is of necessity different from traditional approval processes and is continually evolving.⁸ The FDA approval process cannot account for the fact that the program’s parameters may continually evolve or adapt.²

Guidelines for investigating and reporting AI research with its unique attributes are being developed. Examples include the TRIPOD-ML statement and others.^49,115 In September 2020, 2 publications addressed the paucity of gold-standard randomized clinical trials in clinical AI applications.^116,117 The SPIRIT-AI statement expands on the original SPIRIT statement published in 2013 to guide minimal reporting standards for AI clinical trial protocols to promote transparency of design and methodology.¹¹⁶ Similarly, the CONSORT-AI extension, stemming from the original CONSORT statement in 1996, aims to ensure quality reporting of randomized controlled trials in AI.¹¹⁷

Another risk with AI is that while an individual physician making a mistake may adversely affect 1 patient, a single mistake in an AI algorithm could potentially affect thousands of patients.⁴⁸ Also, AI programs developed for patient populations at a facility may not translate to another. Referred to as overfitting, this phenomenon relates to selection bias in training data sets.^{15,34,49,51,52} Studies have shown that programs that underrepresent certain group characteristics such as age, sex, or race may be less effective when applied to a population in which these characteristics have differing representations.^8,48,49 This problem of underrepresentation has been demonstrated in programs interpreting pathology slides, radiographs, and skin lesions.^15,32,51

Admittedly, most of these challenges are not specific to AI and existed in health care previously. Physicians make mistakes, treatments are sometimes used without adequate prospective studies, and medications are given without understanding their mechanism of action, much like AI-facilitated processes reach a conclusion that cannot be fully explained.⁴⁸

Conclusions

The view that AI will dramatically impact health care in the coming years will likely prove true. However, much work is needed, especially because of the paucity of prospective clinical trials as has been historically required in medical research. Any concern that AI will replace HCPs seems unwarranted. Early studies suggest that even AI programs that appear to exceed human interpretation perform best when working in cooperation with and oversight from clinicians. AI’s greatest potential appears to be its ability to augment care from health professionals, improving efficiency and accuracy, and should be anticipated with enthusiasm as the field moves forward at an exponential rate.

Acknowledgments

The authors thank Makenna G. Thomas for proofreading and review of the manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital. This research has been approved by the James A. Haley Veteran’s Hospital Office of Communications and Media.

Increased mental health needs, higher acuity from delayed appointments, and added questions and conversations surrounding COVID-19 are forcing primary care offices to rethink priorities in office visits.

Evidence of this came from the latest Primary Care Collaborative (PCC) survey, which found that primary care clinicians are seeing more complex patients requiring longer appointments in the wake of COVID-19.

The PCC with the Larry A. Green Center regularly surveys primary care clinicians. This round of questions came August 14-17 and included 1,263 respondents from 49 states, the District of Columbia, and two territories.

More than 7 in 10 (71%) respondents said their patients are more complex and nearly the same percentage said appointments are taking more time.

Ann Greiner, president and CEO of the PCC, said in an interview that 55% of respondents reported that clinicians are struggling to keep up with pent-up demand after patients have delayed or canceled care. Sixty-five percent in the survey said they had seen a rise in children’s mental health issues, and 58% said they were unsure how to help their patients with long COVID.

In addition, primary care clinicians are having repeated conversations with patients on why they should get a vaccine and which one.

“I think that’s adding to the complexity. There is a lot going on here with patient trust,” Ms. Greiner said.
 

‘We’re going to be playing catch-up’

Jacqueline Fincher, MD, an internist in Thompson, Ga., said in an interview that appointments have gotten longer and more complex in the wake of the pandemic – “no question.”

The immediate past president of the American College of Physicians is seeing patients with chronic disease that has gone untreated for sometimes a year or more, she said.

“Their blood pressure was not under good control, they were under more stress, their sugars were up and weren’t being followed as closely for conditions such as congestive heart failure,” she said.

Dr. Fincher, who works in a rural practice 40 miles from Augusta, Ga., with her physician husband and two other physicians, said patients are ready to come back in, “but I don’t have enough slots for them.”

She said she prioritizes what to help patients with first and schedules the next tier for the next appointment, but added, “honestly, over the next 2 years we’re going to be playing catch-up.”

At the same time, the CDC has estimated that 45% of U.S. adults are at increased risk for complications from COVID-19 because of cardiovascular disease, diabetes, respiratory disease, hypertension, or cancer. Rates ranged from 19.8% for people 18-29 years old to 80.7% for people over 80 years of age.
 

Long COVID could overwhelm existing health care capacity

Primary care physicians are also having to diagnose sometimes “invisible” symptoms after people have recovered from acute COVID-19 infection. Diagnosing takes intent listening to patients who describe symptoms that tests can’t confirm.

As this news organization has previously reported, half of COVID-19 survivors report postacute sequelae of COVID-19 (PASC) lasting longer than 6 months.

“These long-term PASC effects occur on a scale that could overwhelm existing health care capacity, particularly in low- and middle-income countries,” the authors wrote.
 

Anxiety, depression ‘have gone off the charts’

Danielle Loeb, MD, MPH, associate professor of internal medicine at the University of Colorado in Denver, who studies complexity in primary care, said in the wake of COVID-19, more patients have developed “new, serious anxiety.”

Courtesy Dr. Danielle Loeb

“That got extremely exacerbated during the pandemic. Anxiety and depression have gone off the charts,” said Dr. Loeb, who prefers the pronoun “they.”

Dr. Loeb cares for a large number of transgender patients. As offices reopen, some patients are having trouble reintegrating into the workplace and resuming social contacts. The primary care doctor says appointments can get longer because of the need to complete tasks, such as filling out forms for Family Medical Leave Act for those not yet ready to return to work.

COVID-19–related fears are keeping many patients from coming into the office, Dr. Loeb said, either from fear of exposure or because they have mental health issues that keep them from feeling safe leaving the house.

“That really affects my ability to care for them,” they said.

Loss of employment in the pandemic or fear of job loss and subsequent changing of insurance has complicated primary care in terms of treatment and administrative tasks, according to Dr. Loeb.

To help treat patients with acute mental health issues and manage other patients, Dr. Loeb’s practice has brought in a social worker and a therapist.

Team-based care is key in the survival of primary care practices, though providing that is difficult in the smaller clinics because of the critical mass of patients needed to make it viable, they said.

“It’s the only answer. It’s the only way you don’t drown,” Dr. Loeb added. “I’m not drowning, and I credit that to my clinic having the help to support the mental health piece of things.”
 

Rethinking workflow

Tricia McGinnis, MPP, MPH, executive vice president of the nonprofit Center for Health Care Strategies (CHCS) says complexity has forced rethinking workflow.

“A lot of the trends we’re seeing in primary care were there pre-COVID, but COVID has exacerbated those trends,” she said in an interview.

“The good news ... is that it was already becoming clear that primary care needed to provide basic mental health services and integrate with behavioral health. It had also become clear that effective primary care needed to address social issues that keep patients from accessing health care,” she said.

Expanding care teams, as Dr. Loeb mentioned, is a key strategy, according to Ms. McGinnis. Potential teams would include the clinical staff, but also social workers and community health workers – people who come from the community primary care is serving who can help build trust with patients and connect the patient to the primary care team.

“There’s a lot that needs to happen that the clinician doesn’t need to do,” she said.

Telehealth can be a big factor in coordinating the team, Ms. McGinnis added.

“It’s thinking less about who’s doing the work, but more about the work that needs to be done to keep people healthy. Then let’s think about the type of workers best suited to perform those tasks,” she said.

As for reimbursing more complex care, population-based, up-front capitated payments linked to high-quality care and better outcomes will need to replace fee-for-service models, according to Ms. McGinnis.

That will provide reliable incomes for primary care offices, but also flexibility in how each patient with different levels of complexity is managed, she said.

Ms. Greiner, Dr. Fincher, Dr. Loeb, and Ms. McGinnis have no relevant financial relationships.

Increased mental health needs, higher acuity from delayed appointments, and added questions and conversations surrounding COVID-19 are forcing primary care offices to rethink priorities in office visits.

Evidence of this came from the latest Primary Care Collaborative (PCC) survey, which found that primary care clinicians are seeing more complex patients requiring longer appointments in the wake of COVID-19.

The PCC with the Larry A. Green Center regularly surveys primary care clinicians. This round of questions came August 14-17 and included 1,263 respondents from 49 states, the District of Columbia, and two territories.

More than 7 in 10 (71%) respondents said their patients are more complex and nearly the same percentage said appointments are taking more time.

Ann Greiner, president and CEO of the PCC, said in an interview that 55% of respondents reported that clinicians are struggling to keep up with pent-up demand after patients have delayed or canceled care. Sixty-five percent in the survey said they had seen a rise in children’s mental health issues, and 58% said they were unsure how to help their patients with long COVID.

In addition, primary care clinicians are having repeated conversations with patients on why they should get a vaccine and which one.

“I think that’s adding to the complexity. There is a lot going on here with patient trust,” Ms. Greiner said.
 

‘We’re going to be playing catch-up’

Jacqueline Fincher, MD, an internist in Thompson, Ga., said in an interview that appointments have gotten longer and more complex in the wake of the pandemic – “no question.”

The immediate past president of the American College of Physicians is seeing patients with chronic disease that has gone untreated for sometimes a year or more, she said.

“Their blood pressure was not under good control, they were under more stress, their sugars were up and weren’t being followed as closely for conditions such as congestive heart failure,” she said.

Dr. Fincher, who works in a rural practice 40 miles from Augusta, Ga., with her physician husband and two other physicians, said patients are ready to come back in, “but I don’t have enough slots for them.”

She said she prioritizes what to help patients with first and schedules the next tier for the next appointment, but added, “honestly, over the next 2 years we’re going to be playing catch-up.”

At the same time, the CDC has estimated that 45% of U.S. adults are at increased risk for complications from COVID-19 because of cardiovascular disease, diabetes, respiratory disease, hypertension, or cancer. Rates ranged from 19.8% for people 18-29 years old to 80.7% for people over 80 years of age.
 

Long COVID could overwhelm existing health care capacity

Primary care physicians are also having to diagnose sometimes “invisible” symptoms after people have recovered from acute COVID-19 infection. Diagnosing takes intent listening to patients who describe symptoms that tests can’t confirm.

As this news organization has previously reported, half of COVID-19 survivors report postacute sequelae of COVID-19 (PASC) lasting longer than 6 months.

“These long-term PASC effects occur on a scale that could overwhelm existing health care capacity, particularly in low- and middle-income countries,” the authors wrote.
 

Anxiety, depression ‘have gone off the charts’

Danielle Loeb, MD, MPH, associate professor of internal medicine at the University of Colorado in Denver, who studies complexity in primary care, said in the wake of COVID-19, more patients have developed “new, serious anxiety.”

Courtesy Dr. Danielle Loeb

“That got extremely exacerbated during the pandemic. Anxiety and depression have gone off the charts,” said Dr. Loeb, who prefers the pronoun “they.”

Dr. Loeb cares for a large number of transgender patients. As offices reopen, some patients are having trouble reintegrating into the workplace and resuming social contacts. The primary care doctor says appointments can get longer because of the need to complete tasks, such as filling out forms for Family Medical Leave Act for those not yet ready to return to work.

COVID-19–related fears are keeping many patients from coming into the office, Dr. Loeb said, either from fear of exposure or because they have mental health issues that keep them from feeling safe leaving the house.

“That really affects my ability to care for them,” they said.

Loss of employment in the pandemic or fear of job loss and subsequent changing of insurance has complicated primary care in terms of treatment and administrative tasks, according to Dr. Loeb.

To help treat patients with acute mental health issues and manage other patients, Dr. Loeb’s practice has brought in a social worker and a therapist.

Team-based care is key in the survival of primary care practices, though providing that is difficult in the smaller clinics because of the critical mass of patients needed to make it viable, they said.

“It’s the only answer. It’s the only way you don’t drown,” Dr. Loeb added. “I’m not drowning, and I credit that to my clinic having the help to support the mental health piece of things.”
 

Rethinking workflow

Tricia McGinnis, MPP, MPH, executive vice president of the nonprofit Center for Health Care Strategies (CHCS) says complexity has forced rethinking workflow.

“A lot of the trends we’re seeing in primary care were there pre-COVID, but COVID has exacerbated those trends,” she said in an interview.

“The good news ... is that it was already becoming clear that primary care needed to provide basic mental health services and integrate with behavioral health. It had also become clear that effective primary care needed to address social issues that keep patients from accessing health care,” she said.

Expanding care teams, as Dr. Loeb mentioned, is a key strategy, according to Ms. McGinnis. Potential teams would include the clinical staff, but also social workers and community health workers – people who come from the community primary care is serving who can help build trust with patients and connect the patient to the primary care team.

“There’s a lot that needs to happen that the clinician doesn’t need to do,” she said.

Telehealth can be a big factor in coordinating the team, Ms. McGinnis added.

“It’s thinking less about who’s doing the work, but more about the work that needs to be done to keep people healthy. Then let’s think about the type of workers best suited to perform those tasks,” she said.

As for reimbursing more complex care, population-based, up-front capitated payments linked to high-quality care and better outcomes will need to replace fee-for-service models, according to Ms. McGinnis.

That will provide reliable incomes for primary care offices, but also flexibility in how each patient with different levels of complexity is managed, she said.

Ms. Greiner, Dr. Fincher, Dr. Loeb, and Ms. McGinnis have no relevant financial relationships.

Increased mental health needs, higher acuity from delayed appointments, and added questions and conversations surrounding COVID-19 are forcing primary care offices to rethink priorities in office visits.

Evidence of this came from the latest Primary Care Collaborative (PCC) survey, which found that primary care clinicians are seeing more complex patients requiring longer appointments in the wake of COVID-19.

The PCC with the Larry A. Green Center regularly surveys primary care clinicians. This round of questions came August 14-17 and included 1,263 respondents from 49 states, the District of Columbia, and two territories.

More than 7 in 10 (71%) respondents said their patients are more complex and nearly the same percentage said appointments are taking more time.

Ann Greiner, president and CEO of the PCC, said in an interview that 55% of respondents reported that clinicians are struggling to keep up with pent-up demand after patients have delayed or canceled care. Sixty-five percent in the survey said they had seen a rise in children’s mental health issues, and 58% said they were unsure how to help their patients with long COVID.

In addition, primary care clinicians are having repeated conversations with patients on why they should get a vaccine and which one.

“I think that’s adding to the complexity. There is a lot going on here with patient trust,” Ms. Greiner said.
 

‘We’re going to be playing catch-up’

Jacqueline Fincher, MD, an internist in Thompson, Ga., said in an interview that appointments have gotten longer and more complex in the wake of the pandemic – “no question.”

The immediate past president of the American College of Physicians is seeing patients with chronic disease that has gone untreated for sometimes a year or more, she said.

“Their blood pressure was not under good control, they were under more stress, their sugars were up and weren’t being followed as closely for conditions such as congestive heart failure,” she said.

Dr. Fincher, who works in a rural practice 40 miles from Augusta, Ga., with her physician husband and two other physicians, said patients are ready to come back in, “but I don’t have enough slots for them.”

She said she prioritizes what to help patients with first and schedules the next tier for the next appointment, but added, “honestly, over the next 2 years we’re going to be playing catch-up.”

At the same time, the CDC has estimated that 45% of U.S. adults are at increased risk for complications from COVID-19 because of cardiovascular disease, diabetes, respiratory disease, hypertension, or cancer. Rates ranged from 19.8% for people 18-29 years old to 80.7% for people over 80 years of age.
 

Long COVID could overwhelm existing health care capacity

Primary care physicians are also having to diagnose sometimes “invisible” symptoms after people have recovered from acute COVID-19 infection. Diagnosing takes intent listening to patients who describe symptoms that tests can’t confirm.

As this news organization has previously reported, half of COVID-19 survivors report postacute sequelae of COVID-19 (PASC) lasting longer than 6 months.

“These long-term PASC effects occur on a scale that could overwhelm existing health care capacity, particularly in low- and middle-income countries,” the authors wrote.
 

Anxiety, depression ‘have gone off the charts’

Danielle Loeb, MD, MPH, associate professor of internal medicine at the University of Colorado in Denver, who studies complexity in primary care, said in the wake of COVID-19, more patients have developed “new, serious anxiety.”

Courtesy Dr. Danielle Loeb

“That got extremely exacerbated during the pandemic. Anxiety and depression have gone off the charts,” said Dr. Loeb, who prefers the pronoun “they.”

Dr. Loeb cares for a large number of transgender patients. As offices reopen, some patients are having trouble reintegrating into the workplace and resuming social contacts. The primary care doctor says appointments can get longer because of the need to complete tasks, such as filling out forms for Family Medical Leave Act for those not yet ready to return to work.

COVID-19–related fears are keeping many patients from coming into the office, Dr. Loeb said, either from fear of exposure or because they have mental health issues that keep them from feeling safe leaving the house.

“That really affects my ability to care for them,” they said.

Loss of employment in the pandemic or fear of job loss and subsequent changing of insurance has complicated primary care in terms of treatment and administrative tasks, according to Dr. Loeb.

To help treat patients with acute mental health issues and manage other patients, Dr. Loeb’s practice has brought in a social worker and a therapist.

Team-based care is key in the survival of primary care practices, though providing that is difficult in the smaller clinics because of the critical mass of patients needed to make it viable, they said.

“It’s the only answer. It’s the only way you don’t drown,” Dr. Loeb added. “I’m not drowning, and I credit that to my clinic having the help to support the mental health piece of things.”
 

Rethinking workflow

Tricia McGinnis, MPP, MPH, executive vice president of the nonprofit Center for Health Care Strategies (CHCS) says complexity has forced rethinking workflow.

“A lot of the trends we’re seeing in primary care were there pre-COVID, but COVID has exacerbated those trends,” she said in an interview.

“The good news ... is that it was already becoming clear that primary care needed to provide basic mental health services and integrate with behavioral health. It had also become clear that effective primary care needed to address social issues that keep patients from accessing health care,” she said.

Expanding care teams, as Dr. Loeb mentioned, is a key strategy, according to Ms. McGinnis. Potential teams would include the clinical staff, but also social workers and community health workers – people who come from the community primary care is serving who can help build trust with patients and connect the patient to the primary care team.

“There’s a lot that needs to happen that the clinician doesn’t need to do,” she said.

Telehealth can be a big factor in coordinating the team, Ms. McGinnis added.

“It’s thinking less about who’s doing the work, but more about the work that needs to be done to keep people healthy. Then let’s think about the type of workers best suited to perform those tasks,” she said.

As for reimbursing more complex care, population-based, up-front capitated payments linked to high-quality care and better outcomes will need to replace fee-for-service models, according to Ms. McGinnis.

That will provide reliable incomes for primary care offices, but also flexibility in how each patient with different levels of complexity is managed, she said.

Ms. Greiner, Dr. Fincher, Dr. Loeb, and Ms. McGinnis have no relevant financial relationships.

In May 15, 1976, Family Practice News published its first “Residents’ Viewpoint,” a monthly column the publication established “in an effort to keep established practitioners as well as residents up to date.”

We are currently republishing an installment of this column as part of our continuing celebration of Family Practice News’s 50th anniversary.

MDedge News

Bruce A. Bagley, MD, wrote the first batch of these columns, when he was chief resident in family medicine at St. Joseph’s Hospital, Syracuse, N.Y. Joseph E. Scherger, MD, was the second writer for Family Practice News’s monthly “Residents’ Viewpoint.” At the time Dr. Scher­ger became a columnist, he was a 26-year-old, 2nd-year family practice resident at the Family Medical Center, University Hospital, University of Washington, Seattle.

Dr. Scherger’s first column was published on Feb. 5, 1977. We are republishing his “Residents’ Viewpoint” from June 15, 1977 (see below) and a new column by Victoria Persampiere, DO, who is currently a 2nd-year resident in the family medicine program at Abington Jefferson Health. (See “My experience as a family medicine resident in 2021” after Dr. Scherger’s column.).

We hope you will enjoy comparing and contrasting the experiences of a resident practicing family medicine today to those of a resident practicing family medicine nearly 4½ decades ago.To learn about Dr. Scherger’s current practice and long career, you can read his profile on the cover of the September 2021 issue of Family Practice News or on MDedge.com/FamilyMedicine in our “Family Practice News 50th Anniversary” section.
 

Art of medicine or deception?

Originally published in Family Practice News on June 15, 1977.

The practice of medicine can be divided into the scientific aspects of diagnosis and treatment and the nonscientific aspects of meeting patients’ needs, the art of medicine.

In medical school I learned the science of medicine. There I diligently studied the basic sciences and gained a thorough understanding of the pathophysiology of disease. In the clinical years I learned to apply this knowledge to a wide variety of interesting patients who came to the academic center.

Yet, when I started my family practice residency, I lacked the ability to care for patients. Though I could take a thorough history, perform a complete physical examination, and diagnose and treat specific illnesses, I had little idea how to satisfy patients by meeting their needs.

The art of medicine is the nonscientific part of a successful doctor-patient interaction. For a doctor-patient interaction to be successful, not only must the illness be appropriately addressed, but both patient and physician must be satisfied.

In the university environment, the art of medicine often gets inadequate attention. Indeed, most academic physicians think that only scientific medicine exists and that patients should be satisfied with a sophisticated approach to their problems. Some patients are satisfied, but many are disgruntled. It is not unusual for a patient, after a $1,000 work-up, to go to a family physician or chiropractor for satisfaction.

I was eager to discover the art of medicine at its finest during my rotation away from the university in a rural community. During these 2 months I looked for the pearls of wisdom that allowed community physicians to be so successful. I found that a very explicit technique was used by some physicians to achieve not only satisfaction but adoration from their patients. Unfortunately, this technique is dishonest.

Early in my community experience I was impressed by how often patients told me a doctor had saved them. I heard such statements as “Dr. X saved my leg,” or “Dr. X saved my life.” I know that it does occur, but not as often as I was hearing it.

Investigating these statements I found such stories as, “One day l twisted my ankle very badly, and it became quite swollen. My doctor told me 1 could lose my leg from this but that he would take x-rays, put my leg in an Ace bandage, and give me crutches. In 3 days I was well. I am so thankful he saved my leg.”

And, “One day I had a temperature of 104. All of my muscles ached, my head hurt, and I had a terrible sore throat and cough. My doctor told me l could die from this, but he gave me a medicine and made me stay home. I was sick for about 2 weeks, but I got better. He saved my life.”

Is the art of medicine the art of deception? This horrifying thought actually came to me after hearing several such stories, but I learned that most of the physicians involved in such stories were not well respected by their colleagues.

I learned many honest techniques for successfully caring for patients. The several family physicians with whom I worked, all clinical instructors associated with my residency, were impeccably honest and taught me to combine compassion and efficiency.

Despite learning many positive techniques and having good role models, I left the community experience somewhat saddened by the lack of integrity that can exist in the profession. I was naive in believing that all the nonscientific aspects of medi­cine that made patients happy must be good.

By experiencing deception, I learned why quackery continues to flourish despite the widespread availability of honest medical care. Most significantly, I learned the importance of a sometimes frustrating humility; my patients with sprained ankles and influenza will not believe I saved their lives.

My experience as a family medicine resident in 2021

I graduated medical school in May 2020, right as COVID was taking over the country, and the specter of the virus has hung over every aspect of my residency education thus far.

I did not get a medical school graduation; I was one of the many thousands of newly graduated students who simply left their 4th-year rotation sites one chilly day in March 2020 and just never went back. My medical school education didn’t end with me walking triumphantly across the stage – a first-generation college student finally achieving the greatest dream in her life. Instead, it ended with a Zoom “graduation” and a cross-country move from Georgia to Pennsylvania amidst the greatest pandemic in recent memory. To say my impostor syndrome was bad would be an understatement.
 

Residency in the COVID-19 era

The joy and the draw to family medicine for me has always been the broad scope of conditions that we see and treat. From day 1, however, much of my residency has been devoted to one very small subset of patients – those with COVID-19. At one point, our hospital was so strained that our family medicine program had to run a second inpatient service alongside our usual five-resident service team just to provide care to everybody. Patients were in the hallways. The ER was packed to the gills. We were sleepless, terrified, unvaccinated, and desperate to help our patients survive a disease that was incompletely understood, with very few tools in our toolbox to combat it.

I distinctly remember sitting in the workroom with a coresident of mine, our faces seemingly permanently lined from wearing N95s all shift, and saying to him, “I worry I will be a bad family medicine physician. I worry I haven’t seen enough, other than COVID.” It was midway through my intern year; the days were short, so I was driving to and from the hospital in chilly darkness. My patients, like many around the country, were doing poorly. Vaccines seemed like a promise too good to be true. Worst of all: Those of us who were interns, who had no triumphant podium moment to end our medical school education, were suffering with an intense sense of impostor syndrome, which was strengthened by every “there is nothing else we can offer your loved one at this time” conversation we had. My apprehension about not having seen a wider breadth of medicine during my training is a sentiment still widely shared by COVID-era residents.

Luckily, my coresident was supportive.

“We’re going to be great family medicine physicians,” he said. “We’re learning the hard stuff – the bread and butter of FM – up-front. You’ll see.”

In some ways, I think he was right. Clinical skills, empathy, humility, and forging strong relationships are at the center of every family medicine physician’s heart; my generation has had to learn these skills early and under pressure. Sometimes, there are no answers. Sometimes, the best thing a family doctor can do for a patient is to hear them, understand them, and hold their hand.
 

‘We watched Cinderella together’

Shortly after that conversation with my coresident, I had a particular case which moved me. This gentleman with intellectual disability and COVID had been declining steadily since his admission to the hospital. He was isolated from everybody he knew and loved, but it did not dampen his spirits. He was cheerful to every person who entered his room, clad in their shrouds of PPE, which more often than not felt more like mourning garb than protective wear. I remember very little about this patient’s clinical picture – the COVID, the superimposed pneumonia, the repeated intubations. What I do remember is he loved the Disney classic Cinderella. I knew this because I developed a very close relationship with his family during the course of his hospitalization. Amidst the torrential onslaught of patients, I made sure to call families every day – not because I wanted to, but because my mentors and attendings and coresidents had all drilled into me from day 1 that we are family medicine, and a large part of our role is to advocate for our patients, and to communicate with their loved ones. So I called. I learned a lot about him; his likes, his dislikes, his close bond with his siblings, and of course his lifelong love for Cinderella. On the last week of my ICU rotation, my patient passed peacefully. His nurse and I were bedside. We held his hand. We told him his family loved him. We watched Cinderella together on an iPad encased in protective plastic.

My next rotation was an outpatient one and it looked more like the “bread and butter” of family medicine. But as I whisked in and out of patient rooms, attending to patients with diabetes, with depression, with pain, I could not stop thinking about my hospitalized patients who my coresidents had assumed care of. Each exam room I entered, I rather morbidly thought “this patient could be next on our hospital service.” Without realizing it, I made more of an effort to get to know each patient holistically. I learned who they were as people. I found myself writing small, medically low-yield details in the chart: “Margaret loves to sing in her church choir;” “Katherine is a self-published author.”

I learned from my attendings. As I sat at the precepting table with them, observing their conversations about patients, their collective decades of experience were apparent.

“I’ve been seeing this patient every few weeks since I was a resident,” said one of my attendings.

“I don’t even see my parents that often,” I thought.

The depth of her relationship with, understanding of, and compassion for this patient struck me deeply. This was why I went into family medicine. My attending knew her patients; they were not faceless unknowns in a hospital gown to her. She would have known to play Cinderella for them in the end.

This is a unique time for trainees. We have been challenged, terrified, overwhelmed, and heartbroken. But at no point have we been isolated. We’ve had the generations of doctors before us to lead the way, to teach us the “hard stuff.” We’ve had senior residents to lean on, who have taken us aside and told us, “I can do the goals-of-care talk today; you need a break.” While the plague seems to have passed over our hospital for now, it has left behind a class of family medicine residents who are proud to carry on our specialty’s long tradition of compassionate, empathetic, lifelong care. “We care for all life stages, from cradle to grave,” says every family medicine physician.

My class, for better or for worse, has cared more often for patients in the twilight of their lives, and while it has been hard, I believe it has made us all better doctors. Now, when I hold a newborn in my arms for a well-child check, I am exceptionally grateful – for the opportunities I have been given, for new beginnings amidst so much sadness, and for the great privilege of being a family medicine physician. ■

Dr. Persampiere is a second-year resident in the family medicine residency program at Abington (Pa.) Jefferson Health. You can contact her directly at [email protected] or via [email protected].

[email protected]

In May 15, 1976, Family Practice News published its first “Residents’ Viewpoint,” a monthly column the publication established “in an effort to keep established practitioners as well as residents up to date.”

We are currently republishing an installment of this column as part of our continuing celebration of Family Practice News’s 50th anniversary.

MDedge News

Bruce A. Bagley, MD, wrote the first batch of these columns, when he was chief resident in family medicine at St. Joseph’s Hospital, Syracuse, N.Y. Joseph E. Scherger, MD, was the second writer for Family Practice News’s monthly “Residents’ Viewpoint.” At the time Dr. Scher­ger became a columnist, he was a 26-year-old, 2nd-year family practice resident at the Family Medical Center, University Hospital, University of Washington, Seattle.

Dr. Scherger’s first column was published on Feb. 5, 1977. We are republishing his “Residents’ Viewpoint” from June 15, 1977 (see below) and a new column by Victoria Persampiere, DO, who is currently a 2nd-year resident in the family medicine program at Abington Jefferson Health. (See “My experience as a family medicine resident in 2021” after Dr. Scherger’s column.).

We hope you will enjoy comparing and contrasting the experiences of a resident practicing family medicine today to those of a resident practicing family medicine nearly 4½ decades ago.To learn about Dr. Scherger’s current practice and long career, you can read his profile on the cover of the September 2021 issue of Family Practice News or on MDedge.com/FamilyMedicine in our “Family Practice News 50th Anniversary” section.
 

Art of medicine or deception?

Originally published in Family Practice News on June 15, 1977.

The practice of medicine can be divided into the scientific aspects of diagnosis and treatment and the nonscientific aspects of meeting patients’ needs, the art of medicine.

In medical school I learned the science of medicine. There I diligently studied the basic sciences and gained a thorough understanding of the pathophysiology of disease. In the clinical years I learned to apply this knowledge to a wide variety of interesting patients who came to the academic center.

Yet, when I started my family practice residency, I lacked the ability to care for patients. Though I could take a thorough history, perform a complete physical examination, and diagnose and treat specific illnesses, I had little idea how to satisfy patients by meeting their needs.

The art of medicine is the nonscientific part of a successful doctor-patient interaction. For a doctor-patient interaction to be successful, not only must the illness be appropriately addressed, but both patient and physician must be satisfied.

In the university environment, the art of medicine often gets inadequate attention. Indeed, most academic physicians think that only scientific medicine exists and that patients should be satisfied with a sophisticated approach to their problems. Some patients are satisfied, but many are disgruntled. It is not unusual for a patient, after a $1,000 work-up, to go to a family physician or chiropractor for satisfaction.

I was eager to discover the art of medicine at its finest during my rotation away from the university in a rural community. During these 2 months I looked for the pearls of wisdom that allowed community physicians to be so successful. I found that a very explicit technique was used by some physicians to achieve not only satisfaction but adoration from their patients. Unfortunately, this technique is dishonest.

Early in my community experience I was impressed by how often patients told me a doctor had saved them. I heard such statements as “Dr. X saved my leg,” or “Dr. X saved my life.” I know that it does occur, but not as often as I was hearing it.

Investigating these statements I found such stories as, “One day l twisted my ankle very badly, and it became quite swollen. My doctor told me 1 could lose my leg from this but that he would take x-rays, put my leg in an Ace bandage, and give me crutches. In 3 days I was well. I am so thankful he saved my leg.”

And, “One day I had a temperature of 104. All of my muscles ached, my head hurt, and I had a terrible sore throat and cough. My doctor told me l could die from this, but he gave me a medicine and made me stay home. I was sick for about 2 weeks, but I got better. He saved my life.”

Is the art of medicine the art of deception? This horrifying thought actually came to me after hearing several such stories, but I learned that most of the physicians involved in such stories were not well respected by their colleagues.

I learned many honest techniques for successfully caring for patients. The several family physicians with whom I worked, all clinical instructors associated with my residency, were impeccably honest and taught me to combine compassion and efficiency.

Despite learning many positive techniques and having good role models, I left the community experience somewhat saddened by the lack of integrity that can exist in the profession. I was naive in believing that all the nonscientific aspects of medi­cine that made patients happy must be good.

By experiencing deception, I learned why quackery continues to flourish despite the widespread availability of honest medical care. Most significantly, I learned the importance of a sometimes frustrating humility; my patients with sprained ankles and influenza will not believe I saved their lives.

My experience as a family medicine resident in 2021

I graduated medical school in May 2020, right as COVID was taking over the country, and the specter of the virus has hung over every aspect of my residency education thus far.

I did not get a medical school graduation; I was one of the many thousands of newly graduated students who simply left their 4th-year rotation sites one chilly day in March 2020 and just never went back. My medical school education didn’t end with me walking triumphantly across the stage – a first-generation college student finally achieving the greatest dream in her life. Instead, it ended with a Zoom “graduation” and a cross-country move from Georgia to Pennsylvania amidst the greatest pandemic in recent memory. To say my impostor syndrome was bad would be an understatement.
 

Residency in the COVID-19 era

The joy and the draw to family medicine for me has always been the broad scope of conditions that we see and treat. From day 1, however, much of my residency has been devoted to one very small subset of patients – those with COVID-19. At one point, our hospital was so strained that our family medicine program had to run a second inpatient service alongside our usual five-resident service team just to provide care to everybody. Patients were in the hallways. The ER was packed to the gills. We were sleepless, terrified, unvaccinated, and desperate to help our patients survive a disease that was incompletely understood, with very few tools in our toolbox to combat it.

I distinctly remember sitting in the workroom with a coresident of mine, our faces seemingly permanently lined from wearing N95s all shift, and saying to him, “I worry I will be a bad family medicine physician. I worry I haven’t seen enough, other than COVID.” It was midway through my intern year; the days were short, so I was driving to and from the hospital in chilly darkness. My patients, like many around the country, were doing poorly. Vaccines seemed like a promise too good to be true. Worst of all: Those of us who were interns, who had no triumphant podium moment to end our medical school education, were suffering with an intense sense of impostor syndrome, which was strengthened by every “there is nothing else we can offer your loved one at this time” conversation we had. My apprehension about not having seen a wider breadth of medicine during my training is a sentiment still widely shared by COVID-era residents.

Luckily, my coresident was supportive.

“We’re going to be great family medicine physicians,” he said. “We’re learning the hard stuff – the bread and butter of FM – up-front. You’ll see.”

In some ways, I think he was right. Clinical skills, empathy, humility, and forging strong relationships are at the center of every family medicine physician’s heart; my generation has had to learn these skills early and under pressure. Sometimes, there are no answers. Sometimes, the best thing a family doctor can do for a patient is to hear them, understand them, and hold their hand.
 

‘We watched Cinderella together’

Shortly after that conversation with my coresident, I had a particular case which moved me. This gentleman with intellectual disability and COVID had been declining steadily since his admission to the hospital. He was isolated from everybody he knew and loved, but it did not dampen his spirits. He was cheerful to every person who entered his room, clad in their shrouds of PPE, which more often than not felt more like mourning garb than protective wear. I remember very little about this patient’s clinical picture – the COVID, the superimposed pneumonia, the repeated intubations. What I do remember is he loved the Disney classic Cinderella. I knew this because I developed a very close relationship with his family during the course of his hospitalization. Amidst the torrential onslaught of patients, I made sure to call families every day – not because I wanted to, but because my mentors and attendings and coresidents had all drilled into me from day 1 that we are family medicine, and a large part of our role is to advocate for our patients, and to communicate with their loved ones. So I called. I learned a lot about him; his likes, his dislikes, his close bond with his siblings, and of course his lifelong love for Cinderella. On the last week of my ICU rotation, my patient passed peacefully. His nurse and I were bedside. We held his hand. We told him his family loved him. We watched Cinderella together on an iPad encased in protective plastic.

My next rotation was an outpatient one and it looked more like the “bread and butter” of family medicine. But as I whisked in and out of patient rooms, attending to patients with diabetes, with depression, with pain, I could not stop thinking about my hospitalized patients who my coresidents had assumed care of. Each exam room I entered, I rather morbidly thought “this patient could be next on our hospital service.” Without realizing it, I made more of an effort to get to know each patient holistically. I learned who they were as people. I found myself writing small, medically low-yield details in the chart: “Margaret loves to sing in her church choir;” “Katherine is a self-published author.”

I learned from my attendings. As I sat at the precepting table with them, observing their conversations about patients, their collective decades of experience were apparent.

“I’ve been seeing this patient every few weeks since I was a resident,” said one of my attendings.

“I don’t even see my parents that often,” I thought.

The depth of her relationship with, understanding of, and compassion for this patient struck me deeply. This was why I went into family medicine. My attending knew her patients; they were not faceless unknowns in a hospital gown to her. She would have known to play Cinderella for them in the end.

This is a unique time for trainees. We have been challenged, terrified, overwhelmed, and heartbroken. But at no point have we been isolated. We’ve had the generations of doctors before us to lead the way, to teach us the “hard stuff.” We’ve had senior residents to lean on, who have taken us aside and told us, “I can do the goals-of-care talk today; you need a break.” While the plague seems to have passed over our hospital for now, it has left behind a class of family medicine residents who are proud to carry on our specialty’s long tradition of compassionate, empathetic, lifelong care. “We care for all life stages, from cradle to grave,” says every family medicine physician.

My class, for better or for worse, has cared more often for patients in the twilight of their lives, and while it has been hard, I believe it has made us all better doctors. Now, when I hold a newborn in my arms for a well-child check, I am exceptionally grateful – for the opportunities I have been given, for new beginnings amidst so much sadness, and for the great privilege of being a family medicine physician. ■

Dr. Persampiere is a second-year resident in the family medicine residency program at Abington (Pa.) Jefferson Health. You can contact her directly at [email protected] or via [email protected].

[email protected]

In May 15, 1976, Family Practice News published its first “Residents’ Viewpoint,” a monthly column the publication established “in an effort to keep established practitioners as well as residents up to date.”

We are currently republishing an installment of this column as part of our continuing celebration of Family Practice News’s 50th anniversary.

MDedge News

Bruce A. Bagley, MD, wrote the first batch of these columns, when he was chief resident in family medicine at St. Joseph’s Hospital, Syracuse, N.Y. Joseph E. Scherger, MD, was the second writer for Family Practice News’s monthly “Residents’ Viewpoint.” At the time Dr. Scher­ger became a columnist, he was a 26-year-old, 2nd-year family practice resident at the Family Medical Center, University Hospital, University of Washington, Seattle.

Dr. Scherger’s first column was published on Feb. 5, 1977. We are republishing his “Residents’ Viewpoint” from June 15, 1977 (see below) and a new column by Victoria Persampiere, DO, who is currently a 2nd-year resident in the family medicine program at Abington Jefferson Health. (See “My experience as a family medicine resident in 2021” after Dr. Scherger’s column.).

We hope you will enjoy comparing and contrasting the experiences of a resident practicing family medicine today to those of a resident practicing family medicine nearly 4½ decades ago.To learn about Dr. Scherger’s current practice and long career, you can read his profile on the cover of the September 2021 issue of Family Practice News or on MDedge.com/FamilyMedicine in our “Family Practice News 50th Anniversary” section.
 

Art of medicine or deception?

Originally published in Family Practice News on June 15, 1977.

The practice of medicine can be divided into the scientific aspects of diagnosis and treatment and the nonscientific aspects of meeting patients’ needs, the art of medicine.

In medical school I learned the science of medicine. There I diligently studied the basic sciences and gained a thorough understanding of the pathophysiology of disease. In the clinical years I learned to apply this knowledge to a wide variety of interesting patients who came to the academic center.

Yet, when I started my family practice residency, I lacked the ability to care for patients. Though I could take a thorough history, perform a complete physical examination, and diagnose and treat specific illnesses, I had little idea how to satisfy patients by meeting their needs.

The art of medicine is the nonscientific part of a successful doctor-patient interaction. For a doctor-patient interaction to be successful, not only must the illness be appropriately addressed, but both patient and physician must be satisfied.

In the university environment, the art of medicine often gets inadequate attention. Indeed, most academic physicians think that only scientific medicine exists and that patients should be satisfied with a sophisticated approach to their problems. Some patients are satisfied, but many are disgruntled. It is not unusual for a patient, after a $1,000 work-up, to go to a family physician or chiropractor for satisfaction.

I was eager to discover the art of medicine at its finest during my rotation away from the university in a rural community. During these 2 months I looked for the pearls of wisdom that allowed community physicians to be so successful. I found that a very explicit technique was used by some physicians to achieve not only satisfaction but adoration from their patients. Unfortunately, this technique is dishonest.

Early in my community experience I was impressed by how often patients told me a doctor had saved them. I heard such statements as “Dr. X saved my leg,” or “Dr. X saved my life.” I know that it does occur, but not as often as I was hearing it.

Investigating these statements I found such stories as, “One day l twisted my ankle very badly, and it became quite swollen. My doctor told me 1 could lose my leg from this but that he would take x-rays, put my leg in an Ace bandage, and give me crutches. In 3 days I was well. I am so thankful he saved my leg.”

And, “One day I had a temperature of 104. All of my muscles ached, my head hurt, and I had a terrible sore throat and cough. My doctor told me l could die from this, but he gave me a medicine and made me stay home. I was sick for about 2 weeks, but I got better. He saved my life.”

Is the art of medicine the art of deception? This horrifying thought actually came to me after hearing several such stories, but I learned that most of the physicians involved in such stories were not well respected by their colleagues.

I learned many honest techniques for successfully caring for patients. The several family physicians with whom I worked, all clinical instructors associated with my residency, were impeccably honest and taught me to combine compassion and efficiency.

Despite learning many positive techniques and having good role models, I left the community experience somewhat saddened by the lack of integrity that can exist in the profession. I was naive in believing that all the nonscientific aspects of medi­cine that made patients happy must be good.

By experiencing deception, I learned why quackery continues to flourish despite the widespread availability of honest medical care. Most significantly, I learned the importance of a sometimes frustrating humility; my patients with sprained ankles and influenza will not believe I saved their lives.

My experience as a family medicine resident in 2021

I graduated medical school in May 2020, right as COVID was taking over the country, and the specter of the virus has hung over every aspect of my residency education thus far.

I did not get a medical school graduation; I was one of the many thousands of newly graduated students who simply left their 4th-year rotation sites one chilly day in March 2020 and just never went back. My medical school education didn’t end with me walking triumphantly across the stage – a first-generation college student finally achieving the greatest dream in her life. Instead, it ended with a Zoom “graduation” and a cross-country move from Georgia to Pennsylvania amidst the greatest pandemic in recent memory. To say my impostor syndrome was bad would be an understatement.
 

Residency in the COVID-19 era

The joy and the draw to family medicine for me has always been the broad scope of conditions that we see and treat. From day 1, however, much of my residency has been devoted to one very small subset of patients – those with COVID-19. At one point, our hospital was so strained that our family medicine program had to run a second inpatient service alongside our usual five-resident service team just to provide care to everybody. Patients were in the hallways. The ER was packed to the gills. We were sleepless, terrified, unvaccinated, and desperate to help our patients survive a disease that was incompletely understood, with very few tools in our toolbox to combat it.

I distinctly remember sitting in the workroom with a coresident of mine, our faces seemingly permanently lined from wearing N95s all shift, and saying to him, “I worry I will be a bad family medicine physician. I worry I haven’t seen enough, other than COVID.” It was midway through my intern year; the days were short, so I was driving to and from the hospital in chilly darkness. My patients, like many around the country, were doing poorly. Vaccines seemed like a promise too good to be true. Worst of all: Those of us who were interns, who had no triumphant podium moment to end our medical school education, were suffering with an intense sense of impostor syndrome, which was strengthened by every “there is nothing else we can offer your loved one at this time” conversation we had. My apprehension about not having seen a wider breadth of medicine during my training is a sentiment still widely shared by COVID-era residents.

Luckily, my coresident was supportive.

“We’re going to be great family medicine physicians,” he said. “We’re learning the hard stuff – the bread and butter of FM – up-front. You’ll see.”

In some ways, I think he was right. Clinical skills, empathy, humility, and forging strong relationships are at the center of every family medicine physician’s heart; my generation has had to learn these skills early and under pressure. Sometimes, there are no answers. Sometimes, the best thing a family doctor can do for a patient is to hear them, understand them, and hold their hand.
 

‘We watched Cinderella together’

Shortly after that conversation with my coresident, I had a particular case which moved me. This gentleman with intellectual disability and COVID had been declining steadily since his admission to the hospital. He was isolated from everybody he knew and loved, but it did not dampen his spirits. He was cheerful to every person who entered his room, clad in their shrouds of PPE, which more often than not felt more like mourning garb than protective wear. I remember very little about this patient’s clinical picture – the COVID, the superimposed pneumonia, the repeated intubations. What I do remember is he loved the Disney classic Cinderella. I knew this because I developed a very close relationship with his family during the course of his hospitalization. Amidst the torrential onslaught of patients, I made sure to call families every day – not because I wanted to, but because my mentors and attendings and coresidents had all drilled into me from day 1 that we are family medicine, and a large part of our role is to advocate for our patients, and to communicate with their loved ones. So I called. I learned a lot about him; his likes, his dislikes, his close bond with his siblings, and of course his lifelong love for Cinderella. On the last week of my ICU rotation, my patient passed peacefully. His nurse and I were bedside. We held his hand. We told him his family loved him. We watched Cinderella together on an iPad encased in protective plastic.

My next rotation was an outpatient one and it looked more like the “bread and butter” of family medicine. But as I whisked in and out of patient rooms, attending to patients with diabetes, with depression, with pain, I could not stop thinking about my hospitalized patients who my coresidents had assumed care of. Each exam room I entered, I rather morbidly thought “this patient could be next on our hospital service.” Without realizing it, I made more of an effort to get to know each patient holistically. I learned who they were as people. I found myself writing small, medically low-yield details in the chart: “Margaret loves to sing in her church choir;” “Katherine is a self-published author.”

I learned from my attendings. As I sat at the precepting table with them, observing their conversations about patients, their collective decades of experience were apparent.

“I’ve been seeing this patient every few weeks since I was a resident,” said one of my attendings.

“I don’t even see my parents that often,” I thought.

The depth of her relationship with, understanding of, and compassion for this patient struck me deeply. This was why I went into family medicine. My attending knew her patients; they were not faceless unknowns in a hospital gown to her. She would have known to play Cinderella for them in the end.

This is a unique time for trainees. We have been challenged, terrified, overwhelmed, and heartbroken. But at no point have we been isolated. We’ve had the generations of doctors before us to lead the way, to teach us the “hard stuff.” We’ve had senior residents to lean on, who have taken us aside and told us, “I can do the goals-of-care talk today; you need a break.” While the plague seems to have passed over our hospital for now, it has left behind a class of family medicine residents who are proud to carry on our specialty’s long tradition of compassionate, empathetic, lifelong care. “We care for all life stages, from cradle to grave,” says every family medicine physician.

My class, for better or for worse, has cared more often for patients in the twilight of their lives, and while it has been hard, I believe it has made us all better doctors. Now, when I hold a newborn in my arms for a well-child check, I am exceptionally grateful – for the opportunities I have been given, for new beginnings amidst so much sadness, and for the great privilege of being a family medicine physician. ■

Dr. Persampiere is a second-year resident in the family medicine residency program at Abington (Pa.) Jefferson Health. You can contact her directly at [email protected] or via [email protected].

[email protected]

In this question-and-answer article (the first in a series), our objective is to reinforce for the clinician several practical points of management for common infectious diseases. The principal references for the answers to the questions are 2 textbook chapters written by Dr. Duff.^1,2 Other pertinent references are included in the text.

1. What are the best tests for the diagnosis of congenital cytomegalovirus (CMV) infection?

When congenital CMV is suspected, if the patient is at least 15 weeks’ gestation, an amniocentesis should be performed to test for CMV DNA in the amniotic fluid using polymerase chain reaction (PCR) methodology. If the initial test is negative, amniocentesis should be repeated in approximately 4 weeks. Coincident with amniocentesis, a detailed ultrasound examination should be performed to search for findings suggestive of fetal injury, such as growth restriction, microcephaly, periventricular calcifications, hepatosplenomegaly, echogenic bowel, and serous effusions in the pleural space or abdomen.

2. Which major organisms cause urinary tract infections (UTIs) in women?

The most common causative organism is Escherichia coli, which is responsible for approximately 70% of all UTIs. Klebsiella pneumoniae and Proteus species are the 2 other aerobic gram-negative bacilli that are common uropathogens. In addition, 3 gram-positive cocci are important: enterococci, Staphylococcus saprophyticus, and group B streptococcus.

3. What are the major complications of pyelonephritis in pregnancy?

Pyelonephritis is an important cause of preterm labor, sepsis, and adult respiratory distress syndrome. Most cases of pyelonephritis develop as a result of an untreated or inadequately treated lower urinary tract infection.

4. What is the most ominous manifestation of congenital parvovirus infection, and what is the cause of this abnormality?

Hydrops fetalis is the most ominous complication of congenital parvovirus infection. The virus crosses the placenta and attacks red cell progenitor cells, resulting in an aplastic anemia. In addition, the virus may cause myocarditis that, in turn, may result in cardiac failure in the fetus.

5. What are the major manifestations of congenital rubella syndrome?

Rubella is one of the most highly teratogenic of all the viral infections, particularly when maternal infection occurs in the first trimester. Manifestations of congenital rubella include hearing deficits, cataracts, glaucoma, microcephaly, mental retardation, cardiac malformations such as patent ductus arteriosus and pulmonic stenosis, and growth restriction.

6. Which vaccines are contraindicated in pregnancy?

Live virus vaccines should not be used in pregnancy because of the possibility of teratogenic effects. Live agents include the measles, mumps, and rubella (MMR) vaccine; live influenza vaccine (FluMist); oral polio vaccine; BCG (bacille Calmette-Guerin) vaccine; yellow fever vaccine; and smallpox vaccine.

7. What is the most appropriate treatment for trichomonas infection in pregnancy?

Trichomonas infection should be treated with oral metronidazole 500 mg twice daily for 7 days. Metronidazole also can be given as a single oral 2-g dose. This treatment is not quite as effective as the multidose regimen, but it may be appropriate for patients who are not likely to be adherent with the longer course of treatment.

Resistance to metronidazole is rare; in such instances, oral tinidazole 2 g in a single dose may be effective.

8. For uncomplicated gonorrhea in a pregnant woman, what is the most appropriate treatment?

The current recommendation from the Centers for Disease Control and Prevention for treatment of uncomplicated gonorrhea is a single 500-mg intramuscular dose of ceftriaxone. For the patient who is opposed to an intramuscular injection, an alternative treatment is cefixime 800 mg orally. With either of these regimens, if chlamydia infection cannot be excluded, the pregnant patient also should receive azithromycin 1,000 mg orally in a single dose. In a nonpregnant patient, doxycycline 100 mg orally twice daily for 7 days should be used to cover for concurrent chlamydia infection.

In a patient with an allergy to β-lactam antibiotics, an alternative regimen for treatment of uncomplicated gonorrhea is intramuscular gentamicin 240 mg plus a single 2,000-mg dose of oral azithromycin. (St Cyr S, Barbee L, Workowski KA, et al. Update to CDC’s treatment guidelines for gonococcal infection, 2020. MMWR Morbid Mortal Wkly Rep. 2020;69:1911-1916.) ●

In this question-and-answer article (the first in a series), our objective is to reinforce for the clinician several practical points of management for common infectious diseases. The principal references for the answers to the questions are 2 textbook chapters written by Dr. Duff.^1,2 Other pertinent references are included in the text.

1. What are the best tests for the diagnosis of congenital cytomegalovirus (CMV) infection?

When congenital CMV is suspected, if the patient is at least 15 weeks’ gestation, an amniocentesis should be performed to test for CMV DNA in the amniotic fluid using polymerase chain reaction (PCR) methodology. If the initial test is negative, amniocentesis should be repeated in approximately 4 weeks. Coincident with amniocentesis, a detailed ultrasound examination should be performed to search for findings suggestive of fetal injury, such as growth restriction, microcephaly, periventricular calcifications, hepatosplenomegaly, echogenic bowel, and serous effusions in the pleural space or abdomen.

2. Which major organisms cause urinary tract infections (UTIs) in women?

The most common causative organism is Escherichia coli, which is responsible for approximately 70% of all UTIs. Klebsiella pneumoniae and Proteus species are the 2 other aerobic gram-negative bacilli that are common uropathogens. In addition, 3 gram-positive cocci are important: enterococci, Staphylococcus saprophyticus, and group B streptococcus.

3. What are the major complications of pyelonephritis in pregnancy?

Pyelonephritis is an important cause of preterm labor, sepsis, and adult respiratory distress syndrome. Most cases of pyelonephritis develop as a result of an untreated or inadequately treated lower urinary tract infection.

4. What is the most ominous manifestation of congenital parvovirus infection, and what is the cause of this abnormality?

Hydrops fetalis is the most ominous complication of congenital parvovirus infection. The virus crosses the placenta and attacks red cell progenitor cells, resulting in an aplastic anemia. In addition, the virus may cause myocarditis that, in turn, may result in cardiac failure in the fetus.

5. What are the major manifestations of congenital rubella syndrome?

Rubella is one of the most highly teratogenic of all the viral infections, particularly when maternal infection occurs in the first trimester. Manifestations of congenital rubella include hearing deficits, cataracts, glaucoma, microcephaly, mental retardation, cardiac malformations such as patent ductus arteriosus and pulmonic stenosis, and growth restriction.

6. Which vaccines are contraindicated in pregnancy?

Live virus vaccines should not be used in pregnancy because of the possibility of teratogenic effects. Live agents include the measles, mumps, and rubella (MMR) vaccine; live influenza vaccine (FluMist); oral polio vaccine; BCG (bacille Calmette-Guerin) vaccine; yellow fever vaccine; and smallpox vaccine.

7. What is the most appropriate treatment for trichomonas infection in pregnancy?

Trichomonas infection should be treated with oral metronidazole 500 mg twice daily for 7 days. Metronidazole also can be given as a single oral 2-g dose. This treatment is not quite as effective as the multidose regimen, but it may be appropriate for patients who are not likely to be adherent with the longer course of treatment.

Resistance to metronidazole is rare; in such instances, oral tinidazole 2 g in a single dose may be effective.

8. For uncomplicated gonorrhea in a pregnant woman, what is the most appropriate treatment?

The current recommendation from the Centers for Disease Control and Prevention for treatment of uncomplicated gonorrhea is a single 500-mg intramuscular dose of ceftriaxone. For the patient who is opposed to an intramuscular injection, an alternative treatment is cefixime 800 mg orally. With either of these regimens, if chlamydia infection cannot be excluded, the pregnant patient also should receive azithromycin 1,000 mg orally in a single dose. In a nonpregnant patient, doxycycline 100 mg orally twice daily for 7 days should be used to cover for concurrent chlamydia infection.

In a patient with an allergy to β-lactam antibiotics, an alternative regimen for treatment of uncomplicated gonorrhea is intramuscular gentamicin 240 mg plus a single 2,000-mg dose of oral azithromycin. (St Cyr S, Barbee L, Workowski KA, et al. Update to CDC’s treatment guidelines for gonococcal infection, 2020. MMWR Morbid Mortal Wkly Rep. 2020;69:1911-1916.) ●

In this question-and-answer article (the first in a series), our objective is to reinforce for the clinician several practical points of management for common infectious diseases. The principal references for the answers to the questions are 2 textbook chapters written by Dr. Duff.^1,2 Other pertinent references are included in the text.

1. What are the best tests for the diagnosis of congenital cytomegalovirus (CMV) infection?

When congenital CMV is suspected, if the patient is at least 15 weeks’ gestation, an amniocentesis should be performed to test for CMV DNA in the amniotic fluid using polymerase chain reaction (PCR) methodology. If the initial test is negative, amniocentesis should be repeated in approximately 4 weeks. Coincident with amniocentesis, a detailed ultrasound examination should be performed to search for findings suggestive of fetal injury, such as growth restriction, microcephaly, periventricular calcifications, hepatosplenomegaly, echogenic bowel, and serous effusions in the pleural space or abdomen.

2. Which major organisms cause urinary tract infections (UTIs) in women?

The most common causative organism is Escherichia coli, which is responsible for approximately 70% of all UTIs. Klebsiella pneumoniae and Proteus species are the 2 other aerobic gram-negative bacilli that are common uropathogens. In addition, 3 gram-positive cocci are important: enterococci, Staphylococcus saprophyticus, and group B streptococcus.

3. What are the major complications of pyelonephritis in pregnancy?

Pyelonephritis is an important cause of preterm labor, sepsis, and adult respiratory distress syndrome. Most cases of pyelonephritis develop as a result of an untreated or inadequately treated lower urinary tract infection.

4. What is the most ominous manifestation of congenital parvovirus infection, and what is the cause of this abnormality?

Hydrops fetalis is the most ominous complication of congenital parvovirus infection. The virus crosses the placenta and attacks red cell progenitor cells, resulting in an aplastic anemia. In addition, the virus may cause myocarditis that, in turn, may result in cardiac failure in the fetus.

5. What are the major manifestations of congenital rubella syndrome?

Rubella is one of the most highly teratogenic of all the viral infections, particularly when maternal infection occurs in the first trimester. Manifestations of congenital rubella include hearing deficits, cataracts, glaucoma, microcephaly, mental retardation, cardiac malformations such as patent ductus arteriosus and pulmonic stenosis, and growth restriction.

6. Which vaccines are contraindicated in pregnancy?

Live virus vaccines should not be used in pregnancy because of the possibility of teratogenic effects. Live agents include the measles, mumps, and rubella (MMR) vaccine; live influenza vaccine (FluMist); oral polio vaccine; BCG (bacille Calmette-Guerin) vaccine; yellow fever vaccine; and smallpox vaccine.

7. What is the most appropriate treatment for trichomonas infection in pregnancy?

Trichomonas infection should be treated with oral metronidazole 500 mg twice daily for 7 days. Metronidazole also can be given as a single oral 2-g dose. This treatment is not quite as effective as the multidose regimen, but it may be appropriate for patients who are not likely to be adherent with the longer course of treatment.

Resistance to metronidazole is rare; in such instances, oral tinidazole 2 g in a single dose may be effective.

8. For uncomplicated gonorrhea in a pregnant woman, what is the most appropriate treatment?

The current recommendation from the Centers for Disease Control and Prevention for treatment of uncomplicated gonorrhea is a single 500-mg intramuscular dose of ceftriaxone. For the patient who is opposed to an intramuscular injection, an alternative treatment is cefixime 800 mg orally. With either of these regimens, if chlamydia infection cannot be excluded, the pregnant patient also should receive azithromycin 1,000 mg orally in a single dose. In a nonpregnant patient, doxycycline 100 mg orally twice daily for 7 days should be used to cover for concurrent chlamydia infection.

In a patient with an allergy to β-lactam antibiotics, an alternative regimen for treatment of uncomplicated gonorrhea is intramuscular gentamicin 240 mg plus a single 2,000-mg dose of oral azithromycin. (St Cyr S, Barbee L, Workowski KA, et al. Update to CDC’s treatment guidelines for gonococcal infection, 2020. MMWR Morbid Mortal Wkly Rep. 2020;69:1911-1916.) ●

The number of suicides in 2020 declined in comparison to 2019, despite an increase in some risk factors associated with suicidal behavior, including pandemic-related job loss, financial strain, and deteriorating mental health, according to new federal statistics.

The number of annual suicides in the United States increased steadily from 2003 through 2018, followed by a 2% decline between 2018 and 2019. There was concern that deaths due to suicide would increase in 2020, but this doesn’t appear to be the case.

The provisional numbers show 45,855 deaths by suicide in the United States in 2020 – 3% lower than in 2019 (47,511), and 5% below the 2018 peak of 48,344 suicides, report Sally Curtin, MA, and colleagues with the National Center for Health Statistics, part of the U.S. Centers for Disease Control and Prevention.

The data were published online Nov. 3 in the National Vital Statistics System (NVSS) Vital Statistics Rapid Release.

On a monthly basis, the number of suicides was lower in 2020 than in 2019 in March through October and December – with the largest drop happening in April 2020 at a time when deaths from COVID-19 were peaking, the authors note. In April 2020, suicide deaths were 14% lower than in April 2019 (3,468 vs. 4,029).

The provisional age-adjusted suicide rate was 3% lower in 2020 (13.5 per 100,000) than in 2019 (13.9 per 100,000). It was 2% lower among men (21.9 compared with 22.4), and 8% lower for women (5.5 compared with 6.0).

Suicide rates among younger adults aged 10 to 34 years rose slightly between 2019 and 2020 but was only significant in those 25 to 34, with a 5% increase between 2019 and 2020.

Individuals aged 35 to 74 years had significant declines in suicide with the largest drop in those aged 45 to 54 years and 55 to 64 years.

Women in all race and Hispanic-origin groups showed declines in suicide rates between 2019 and 2020, but the decline was significant only among non-Hispanic white women (10%).

Suicide rates declined for non-Hispanic white and non-Hispanic Asian men but increased among non-Hispanic black, non-Hispanic American Indian or Alaska Native, and Hispanic men.

This analysis is based on more than 99% of expected death records. Based on previous patterns between provisional and final data, these provisional findings are expected to be consistent with final 2020 data, the authors say.

The study had no commercial funding. The authors have disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

The number of suicides in 2020 declined in comparison to 2019, despite an increase in some risk factors associated with suicidal behavior, including pandemic-related job loss, financial strain, and deteriorating mental health, according to new federal statistics.

The number of annual suicides in the United States increased steadily from 2003 through 2018, followed by a 2% decline between 2018 and 2019. There was concern that deaths due to suicide would increase in 2020, but this doesn’t appear to be the case.

The provisional numbers show 45,855 deaths by suicide in the United States in 2020 – 3% lower than in 2019 (47,511), and 5% below the 2018 peak of 48,344 suicides, report Sally Curtin, MA, and colleagues with the National Center for Health Statistics, part of the U.S. Centers for Disease Control and Prevention.

The data were published online Nov. 3 in the National Vital Statistics System (NVSS) Vital Statistics Rapid Release.

On a monthly basis, the number of suicides was lower in 2020 than in 2019 in March through October and December – with the largest drop happening in April 2020 at a time when deaths from COVID-19 were peaking, the authors note. In April 2020, suicide deaths were 14% lower than in April 2019 (3,468 vs. 4,029).

The provisional age-adjusted suicide rate was 3% lower in 2020 (13.5 per 100,000) than in 2019 (13.9 per 100,000). It was 2% lower among men (21.9 compared with 22.4), and 8% lower for women (5.5 compared with 6.0).

Suicide rates among younger adults aged 10 to 34 years rose slightly between 2019 and 2020 but was only significant in those 25 to 34, with a 5% increase between 2019 and 2020.

Individuals aged 35 to 74 years had significant declines in suicide with the largest drop in those aged 45 to 54 years and 55 to 64 years.

Women in all race and Hispanic-origin groups showed declines in suicide rates between 2019 and 2020, but the decline was significant only among non-Hispanic white women (10%).

Suicide rates declined for non-Hispanic white and non-Hispanic Asian men but increased among non-Hispanic black, non-Hispanic American Indian or Alaska Native, and Hispanic men.

This analysis is based on more than 99% of expected death records. Based on previous patterns between provisional and final data, these provisional findings are expected to be consistent with final 2020 data, the authors say.

The study had no commercial funding. The authors have disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

The number of suicides in 2020 declined in comparison to 2019, despite an increase in some risk factors associated with suicidal behavior, including pandemic-related job loss, financial strain, and deteriorating mental health, according to new federal statistics.

The number of annual suicides in the United States increased steadily from 2003 through 2018, followed by a 2% decline between 2018 and 2019. There was concern that deaths due to suicide would increase in 2020, but this doesn’t appear to be the case.

The provisional numbers show 45,855 deaths by suicide in the United States in 2020 – 3% lower than in 2019 (47,511), and 5% below the 2018 peak of 48,344 suicides, report Sally Curtin, MA, and colleagues with the National Center for Health Statistics, part of the U.S. Centers for Disease Control and Prevention.

The data were published online Nov. 3 in the National Vital Statistics System (NVSS) Vital Statistics Rapid Release.

On a monthly basis, the number of suicides was lower in 2020 than in 2019 in March through October and December – with the largest drop happening in April 2020 at a time when deaths from COVID-19 were peaking, the authors note. In April 2020, suicide deaths were 14% lower than in April 2019 (3,468 vs. 4,029).

The provisional age-adjusted suicide rate was 3% lower in 2020 (13.5 per 100,000) than in 2019 (13.9 per 100,000). It was 2% lower among men (21.9 compared with 22.4), and 8% lower for women (5.5 compared with 6.0).

Suicide rates among younger adults aged 10 to 34 years rose slightly between 2019 and 2020 but was only significant in those 25 to 34, with a 5% increase between 2019 and 2020.

Individuals aged 35 to 74 years had significant declines in suicide with the largest drop in those aged 45 to 54 years and 55 to 64 years.

Women in all race and Hispanic-origin groups showed declines in suicide rates between 2019 and 2020, but the decline was significant only among non-Hispanic white women (10%).

Suicide rates declined for non-Hispanic white and non-Hispanic Asian men but increased among non-Hispanic black, non-Hispanic American Indian or Alaska Native, and Hispanic men.

This analysis is based on more than 99% of expected death records. Based on previous patterns between provisional and final data, these provisional findings are expected to be consistent with final 2020 data, the authors say.

The study had no commercial funding. The authors have disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Douglas Flora, MD, an oncologist with St. Elizabeth Healthcare, in Edgewood, Ky., considers himself a deep empath. It’s one reason he became an oncologist.

But when he was diagnosed with kidney cancer in 2017, he was shocked at the places his brain took him. His mind fast-forwarded through treatment options, statistical probabilities, and anguish over his wife and children.

“It’s a very surreal experience,” Dr. Flora said. “In 20 seconds, you go from diagnostics to, ‘What videos will I have to film for my babies?’ “

He could be having a wonderful evening surrounded by friends, music, and beer. Then he would go to the restroom and the realization of what was lurking inside his would body hit him like a brick.

“It’s like the scene in the Harry Potter movies where the Dementors fly over,” he explained. “Everything feels dark. There’s no hope. Everything you thought was good is gone.”

Oncologists counsel patients through life-threatening diagnoses and frightening decisions every day, so one might think they’d be ready to confront their own diagnosis, treatment, and mortality better than anyone. But that’s not always the case.

So, what happens when oncology practitioners trade their white coat for a hospital gown? Does their expertise equip them to navigate their diagnosis and treatment better than their patients? How does the emotional toll of their personal cancer journey change the way they interact with their patients?
 

Navigating the diagnosis and treatment

In January 2017, Karen Hendershott, MD, a breast surgical oncologist, felt a lump in her armpit while taking a shower. The blunt force of her fate came into view in an instant: It was almost certainly a locally advanced breast cancer that had spread to her lymph nodes and would require surgery, radiotherapy, and chemotherapy.

She said a few unprintable words and headed to work at St. Mary’s Hospital, in Tucson, Ariz., where her assumptions were confirmed.

Taylor Riall, MD, PhD, also suspected cancer.

Last December, Dr. Riall, a general surgeon and surgical oncologist at the University of Arizona Cancer Center, in Tucson, developed a persistent cough. An x-ray revealed a mass in her lung. Initially, she was misdiagnosed with a fungal infection and was given medication that made her skin peel off.

Doctors advised Dr. Riall to monitor her condition for another 6 months. But her knowledge of oncology made her think cancer, so she insisted on more tests. In June 2021, a biopsy confirmed she had lung cancer.

Having oncology expertise helped Dr. Riall and Dr. Hendershott recognize the signs of cancer and push for a diagnosis. But there are also downsides to being hyper-informed, Dr. Hendershott, said.

“I think sometimes knowing everything at once is harder vs. giving yourself time to wrap your mind around this and do it in baby steps,” she explained. “There weren’t any baby steps here.”

Still, oncology practitioners who are diagnosed with cancer are navigating a familiar landscape and are often buoyed by a support network of expert colleagues. That makes a huge difference psychologically, explained Shenitha Edwards, a pharmacy technician at Cancer Specialists of North Florida, in Jacksonville, who was diagnosed with breast cancer in July.

“I felt stronger and a little more ready to fight because I had resources, whereas my patients sometimes do not,” Ms. Edwards said. “I was connected with a lot of people who could help me make informed decisions, so I didn’t have to walk so much in fear.”

It can also prepare practitioners to make bold treatment choices. In Dr. Riall’s case, surgeons were reluctant to excise her tumor because they would have to remove the entire upper lobe of her lung, and she is a marathoner and triathlete. Still, because of her surgical oncology experience, Dr. Riall didn’t flinch at the prospect of a major operation.

“I was, like, ‘Look, just take it out.’ I’m less afraid to have cancer than I am to not know and let it grow,” said Dr. Riall, whose Peloton name is WhoNeeds2Lungs.

Similarly, Dr. Hendershott’s experience gave her the assurance to pursue a more intense strategy. “Because I had a really candid understanding of the risks and what the odds looked like, it helped me be more comfortable with a more aggressive approach,” she said. “There wasn’t a doubt in my mind, particularly [having] a 10-year-old child, that I wanted to do everything I could, and even do a couple of things that were still in clinical trials.”

Almost paradoxically, Mark Lewis’ oncology training gave him the courage to risk watching and waiting after finding benign growths in his parathyroid and malignant tumors in his pancreas. Dr. Lewis monitored the tumors amassing in his pancreas for 8 years. When some grew so large they threatened to metastasize to his liver, he underwent the Whipple procedure to remove the head of the pancreas, part of the small intestine, and the gallbladder.

“It was a bit of a gamble, but one that paid off and allowed me to get my career off the ground and have another child,” said Dr. Lewis, a gastrointestinal oncologist at Intermountain Healthcare, in Salt Lake City. Treating patients for nearly a decade also showed him how fortunate he was to have a slow-growing, operable cancer. That gratitude, he said, gave him mental strength to endure the ordeal.

Whether taking a more aggressive or minimalist approach to their own care, each practitioner’s decision was deeply personal and deeply informed by their oncology expertise.

Although research on this question is scarce, studies show that differences in end-of-life care may occur. According to a 2016 study published in JAMA, physicians choose significantly less intensive end-stage care in three of five categories — undergoing surgery, being admitted to the intensive care unit (ICU), and dying in the hospital — than the general U.S. population. The reason, the researchers posited, is because doctors know these eleventh-hour interventions are typically brutal and futile.

But these differences were fairly small, and a 2019 study published in JAMA Open Network found the opposite: Physicians with cancer were more likely to die in an ICU and receive chemotherapy in the last 6 months of life, suggesting a more aggressive approach to end-of-life care.

When it comes to their own long-term or curative cancer care, oncologists generally don’t seem to approach treatment differently than their patients. In a 2015 study, researchers compared two groups of people with early breast cancer — 46 physicians and 230 well-educated, nonmedically qualified patients — and found no differences in the choices the groups made about whether to undergo mastectomy, chemotherapy, radiotherapy, or breast reconstruction.

Still, no amount of oncology expertise can fully prepare a person for the emotional crucible of cancer.
 

“A very surreal experience”

Although the fear can become less intense and more manageable over time, it may never truly go away.

At first, despair dragged Flora into an abyss for 6 hours a night, then overcame him 10 times a day, then gripped him briefly at random moments. Four years later and cancer-free, the dread still returns.

Hendershott cried every time she got into her car and contemplated her prognosis. Now 47, she has about a 60% chance of being alive in 15 years, and the fear still hits her.

“I think it’s hard to understand the moments of sheer terror that you have at 2 AM when you’re confronting your own mortality,” she said. “The implications that has not just for you but more importantly for the people that you love and want to protect. That just kind of washes over you in waves that you don’t have much control over.”

Cancer, Riall felt, had smashed her life, but she figured out a way to help herself cope. Severe blood loss, chest tubes, and tests and needles ad nauseum left Riall feeling excruciatingly exhausted after her surgery and delayed her return to work. At the same time, she was passed over for a promotion. Frustrated and dejected, she took comfort in the memory of doing Kintsugi with her surgery residents. The Japanese art form involves shattering pottery with a hammer, fitting the fragments back together, and painting the cracks gold.

“My instinct as a surgeon is to pick up those pieces and put them back together so nobody sees it’s broken,” she reflected. But as a patient, she learned that an important part of recovery is to allow yourself to sit in a broken state and feel angry, miserable, and betrayed by your body. And then examine your shattered priorities and consider how you want to reassemble them.

For Barbara Buttin, MD, a gynecologic oncologist at Cancer Treatment Centers of America, in Chicago, Illinois, it wasn’t cancer that almost took her life. Rather, a near-death experience and life-threatening diagnosis made her a better, more empathetic cancer doctor — a refrain echoed by many oncologist-patients. Confronting her own mortality crystallized what matters in life. She uses that understanding to make sure she understands what matters to her patients ― what they care about most, what their greatest fear is, what is going to keep them up at night.
 

“We’re part of the same club”

Ultimately, when oncology practitioners become patients, it balances the in-control and vulnerable, the rational and emotional. And their patients respond positively.

In fall 2020, oncology nurse Jenn Adams, RN, turned 40 and underwent her first mammogram. Unexpectedly, it revealed invasive stage I cancer that would require a double mastectomy, chemotherapy, and a year of immunotherapy. A week after her diagnosis, she was scheduled to start a new job at Cancer Clinic, in Bryan, Tex. So, she asked her manager if she could become a patient and an employee.

Ms. Adams worked 5 days a week, but every Thursday at 2 PM, she sat next to her patients while her coworkers became her nurses. Her chemo port was implanted, she lost her hair, and she felt terrible along with her patients. “It just created this incredible bond,” said the mother of three.

Having cancer, Dr. Flora said, “was completely different than I had imagined. When I thought I was walking with [my patients] in the depths of their caves, I wasn’t even visiting their caves.” But, he added, it has also “let me connect with [patients] on a deeper level because we’re part of the same club. You can see their body language change when I share that. They almost relax, like, ‘Oh, this guy gets it. He does understand how terrified I am.’ And I do.”

When Dr. Flora’s patients are scanned, he gives them their results immediately, because he knows what it’s like to wait on tenterhooks. He tells his patients to text him anytime they’re afraid or depressed, which he admits isn’t great for his own mental health but believes is worth it.

Likewise, Dr. Hendershott can hold out her shoulder-length locks to reassure a crying patient that hair does grow back after chemo. She can describe her experience with hormone-blocking pills to allay the fears of a pharmaceutical skeptic.

This role equalizer fosters so much empathy that doctors sometimes find themselves being helped by their patients. When one of Dr. Flora’s patients heard he had cancer, she sent him an email that began. “A wise doctor once told me....” and repeated the advice he’d given her years before.

Dr. Lewis has a special bond with his patients because people who have pancreatic neuroendocrine tumors seek him out for treatment. “I’m getting to take care of people who, on some level, are like my kindred spirits,” he said. “So, I get to see their coping mechanisms and how they do.”

Ms. Edwards told some of her patients about her breast cancer diagnosis, and now they give each other high-fives and share words of encouragement. “I made it a big thing of mine to associate my patients as my family,” she said. “If you’ve learned to embrace love and love people, there’s nothing you wouldn’t do for people. I’ve chosen that to be my practice when I’m dealing with all of my patients.”

Ms. Adams is on a similar mission. She joined a group of moms with cancer so she can receive guidance and then become a guide for others. “I feel like that’s what I want to be at my cancer practice,” she said, “so [my patients] have someone to say, ‘I’m gonna walk alongside you because I’ve been there.’ “

That transformation has made all the heartbreaking moments worth it, Ms. Adams said. “I love the oncology nurse that I get to be now because of my diagnosis. I don’t love the diagnosis. But I love the way it’s changed what I do.”

A version of this article first appeared on Medscape.com.

Douglas Flora, MD, an oncologist with St. Elizabeth Healthcare, in Edgewood, Ky., considers himself a deep empath. It’s one reason he became an oncologist.

But when he was diagnosed with kidney cancer in 2017, he was shocked at the places his brain took him. His mind fast-forwarded through treatment options, statistical probabilities, and anguish over his wife and children.

“It’s a very surreal experience,” Dr. Flora said. “In 20 seconds, you go from diagnostics to, ‘What videos will I have to film for my babies?’ “

He could be having a wonderful evening surrounded by friends, music, and beer. Then he would go to the restroom and the realization of what was lurking inside his would body hit him like a brick.

“It’s like the scene in the Harry Potter movies where the Dementors fly over,” he explained. “Everything feels dark. There’s no hope. Everything you thought was good is gone.”

Oncologists counsel patients through life-threatening diagnoses and frightening decisions every day, so one might think they’d be ready to confront their own diagnosis, treatment, and mortality better than anyone. But that’s not always the case.

So, what happens when oncology practitioners trade their white coat for a hospital gown? Does their expertise equip them to navigate their diagnosis and treatment better than their patients? How does the emotional toll of their personal cancer journey change the way they interact with their patients?
 

Navigating the diagnosis and treatment

In January 2017, Karen Hendershott, MD, a breast surgical oncologist, felt a lump in her armpit while taking a shower. The blunt force of her fate came into view in an instant: It was almost certainly a locally advanced breast cancer that had spread to her lymph nodes and would require surgery, radiotherapy, and chemotherapy.

She said a few unprintable words and headed to work at St. Mary’s Hospital, in Tucson, Ariz., where her assumptions were confirmed.

Taylor Riall, MD, PhD, also suspected cancer.

Last December, Dr. Riall, a general surgeon and surgical oncologist at the University of Arizona Cancer Center, in Tucson, developed a persistent cough. An x-ray revealed a mass in her lung. Initially, she was misdiagnosed with a fungal infection and was given medication that made her skin peel off.

Doctors advised Dr. Riall to monitor her condition for another 6 months. But her knowledge of oncology made her think cancer, so she insisted on more tests. In June 2021, a biopsy confirmed she had lung cancer.

Having oncology expertise helped Dr. Riall and Dr. Hendershott recognize the signs of cancer and push for a diagnosis. But there are also downsides to being hyper-informed, Dr. Hendershott, said.

“I think sometimes knowing everything at once is harder vs. giving yourself time to wrap your mind around this and do it in baby steps,” she explained. “There weren’t any baby steps here.”

Still, oncology practitioners who are diagnosed with cancer are navigating a familiar landscape and are often buoyed by a support network of expert colleagues. That makes a huge difference psychologically, explained Shenitha Edwards, a pharmacy technician at Cancer Specialists of North Florida, in Jacksonville, who was diagnosed with breast cancer in July.

“I felt stronger and a little more ready to fight because I had resources, whereas my patients sometimes do not,” Ms. Edwards said. “I was connected with a lot of people who could help me make informed decisions, so I didn’t have to walk so much in fear.”

It can also prepare practitioners to make bold treatment choices. In Dr. Riall’s case, surgeons were reluctant to excise her tumor because they would have to remove the entire upper lobe of her lung, and she is a marathoner and triathlete. Still, because of her surgical oncology experience, Dr. Riall didn’t flinch at the prospect of a major operation.

“I was, like, ‘Look, just take it out.’ I’m less afraid to have cancer than I am to not know and let it grow,” said Dr. Riall, whose Peloton name is WhoNeeds2Lungs.

Similarly, Dr. Hendershott’s experience gave her the assurance to pursue a more intense strategy. “Because I had a really candid understanding of the risks and what the odds looked like, it helped me be more comfortable with a more aggressive approach,” she said. “There wasn’t a doubt in my mind, particularly [having] a 10-year-old child, that I wanted to do everything I could, and even do a couple of things that were still in clinical trials.”

Almost paradoxically, Mark Lewis’ oncology training gave him the courage to risk watching and waiting after finding benign growths in his parathyroid and malignant tumors in his pancreas. Dr. Lewis monitored the tumors amassing in his pancreas for 8 years. When some grew so large they threatened to metastasize to his liver, he underwent the Whipple procedure to remove the head of the pancreas, part of the small intestine, and the gallbladder.

“It was a bit of a gamble, but one that paid off and allowed me to get my career off the ground and have another child,” said Dr. Lewis, a gastrointestinal oncologist at Intermountain Healthcare, in Salt Lake City. Treating patients for nearly a decade also showed him how fortunate he was to have a slow-growing, operable cancer. That gratitude, he said, gave him mental strength to endure the ordeal.

Whether taking a more aggressive or minimalist approach to their own care, each practitioner’s decision was deeply personal and deeply informed by their oncology expertise.

Although research on this question is scarce, studies show that differences in end-of-life care may occur. According to a 2016 study published in JAMA, physicians choose significantly less intensive end-stage care in three of five categories — undergoing surgery, being admitted to the intensive care unit (ICU), and dying in the hospital — than the general U.S. population. The reason, the researchers posited, is because doctors know these eleventh-hour interventions are typically brutal and futile.

But these differences were fairly small, and a 2019 study published in JAMA Open Network found the opposite: Physicians with cancer were more likely to die in an ICU and receive chemotherapy in the last 6 months of life, suggesting a more aggressive approach to end-of-life care.

When it comes to their own long-term or curative cancer care, oncologists generally don’t seem to approach treatment differently than their patients. In a 2015 study, researchers compared two groups of people with early breast cancer — 46 physicians and 230 well-educated, nonmedically qualified patients — and found no differences in the choices the groups made about whether to undergo mastectomy, chemotherapy, radiotherapy, or breast reconstruction.

Still, no amount of oncology expertise can fully prepare a person for the emotional crucible of cancer.
 

“A very surreal experience”

Although the fear can become less intense and more manageable over time, it may never truly go away.

At first, despair dragged Flora into an abyss for 6 hours a night, then overcame him 10 times a day, then gripped him briefly at random moments. Four years later and cancer-free, the dread still returns.

Hendershott cried every time she got into her car and contemplated her prognosis. Now 47, she has about a 60% chance of being alive in 15 years, and the fear still hits her.

“I think it’s hard to understand the moments of sheer terror that you have at 2 AM when you’re confronting your own mortality,” she said. “The implications that has not just for you but more importantly for the people that you love and want to protect. That just kind of washes over you in waves that you don’t have much control over.”

Cancer, Riall felt, had smashed her life, but she figured out a way to help herself cope. Severe blood loss, chest tubes, and tests and needles ad nauseum left Riall feeling excruciatingly exhausted after her surgery and delayed her return to work. At the same time, she was passed over for a promotion. Frustrated and dejected, she took comfort in the memory of doing Kintsugi with her surgery residents. The Japanese art form involves shattering pottery with a hammer, fitting the fragments back together, and painting the cracks gold.

“My instinct as a surgeon is to pick up those pieces and put them back together so nobody sees it’s broken,” she reflected. But as a patient, she learned that an important part of recovery is to allow yourself to sit in a broken state and feel angry, miserable, and betrayed by your body. And then examine your shattered priorities and consider how you want to reassemble them.

For Barbara Buttin, MD, a gynecologic oncologist at Cancer Treatment Centers of America, in Chicago, Illinois, it wasn’t cancer that almost took her life. Rather, a near-death experience and life-threatening diagnosis made her a better, more empathetic cancer doctor — a refrain echoed by many oncologist-patients. Confronting her own mortality crystallized what matters in life. She uses that understanding to make sure she understands what matters to her patients ― what they care about most, what their greatest fear is, what is going to keep them up at night.
 

“We’re part of the same club”

Ultimately, when oncology practitioners become patients, it balances the in-control and vulnerable, the rational and emotional. And their patients respond positively.

In fall 2020, oncology nurse Jenn Adams, RN, turned 40 and underwent her first mammogram. Unexpectedly, it revealed invasive stage I cancer that would require a double mastectomy, chemotherapy, and a year of immunotherapy. A week after her diagnosis, she was scheduled to start a new job at Cancer Clinic, in Bryan, Tex. So, she asked her manager if she could become a patient and an employee.

Ms. Adams worked 5 days a week, but every Thursday at 2 PM, she sat next to her patients while her coworkers became her nurses. Her chemo port was implanted, she lost her hair, and she felt terrible along with her patients. “It just created this incredible bond,” said the mother of three.

Having cancer, Dr. Flora said, “was completely different than I had imagined. When I thought I was walking with [my patients] in the depths of their caves, I wasn’t even visiting their caves.” But, he added, it has also “let me connect with [patients] on a deeper level because we’re part of the same club. You can see their body language change when I share that. They almost relax, like, ‘Oh, this guy gets it. He does understand how terrified I am.’ And I do.”

When Dr. Flora’s patients are scanned, he gives them their results immediately, because he knows what it’s like to wait on tenterhooks. He tells his patients to text him anytime they’re afraid or depressed, which he admits isn’t great for his own mental health but believes is worth it.

Likewise, Dr. Hendershott can hold out her shoulder-length locks to reassure a crying patient that hair does grow back after chemo. She can describe her experience with hormone-blocking pills to allay the fears of a pharmaceutical skeptic.

This role equalizer fosters so much empathy that doctors sometimes find themselves being helped by their patients. When one of Dr. Flora’s patients heard he had cancer, she sent him an email that began. “A wise doctor once told me....” and repeated the advice he’d given her years before.

Dr. Lewis has a special bond with his patients because people who have pancreatic neuroendocrine tumors seek him out for treatment. “I’m getting to take care of people who, on some level, are like my kindred spirits,” he said. “So, I get to see their coping mechanisms and how they do.”

Ms. Edwards told some of her patients about her breast cancer diagnosis, and now they give each other high-fives and share words of encouragement. “I made it a big thing of mine to associate my patients as my family,” she said. “If you’ve learned to embrace love and love people, there’s nothing you wouldn’t do for people. I’ve chosen that to be my practice when I’m dealing with all of my patients.”

Ms. Adams is on a similar mission. She joined a group of moms with cancer so she can receive guidance and then become a guide for others. “I feel like that’s what I want to be at my cancer practice,” she said, “so [my patients] have someone to say, ‘I’m gonna walk alongside you because I’ve been there.’ “

That transformation has made all the heartbreaking moments worth it, Ms. Adams said. “I love the oncology nurse that I get to be now because of my diagnosis. I don’t love the diagnosis. But I love the way it’s changed what I do.”

A version of this article first appeared on Medscape.com.

Douglas Flora, MD, an oncologist with St. Elizabeth Healthcare, in Edgewood, Ky., considers himself a deep empath. It’s one reason he became an oncologist.

But when he was diagnosed with kidney cancer in 2017, he was shocked at the places his brain took him. His mind fast-forwarded through treatment options, statistical probabilities, and anguish over his wife and children.

“It’s a very surreal experience,” Dr. Flora said. “In 20 seconds, you go from diagnostics to, ‘What videos will I have to film for my babies?’ “

He could be having a wonderful evening surrounded by friends, music, and beer. Then he would go to the restroom and the realization of what was lurking inside his would body hit him like a brick.

“It’s like the scene in the Harry Potter movies where the Dementors fly over,” he explained. “Everything feels dark. There’s no hope. Everything you thought was good is gone.”

Oncologists counsel patients through life-threatening diagnoses and frightening decisions every day, so one might think they’d be ready to confront their own diagnosis, treatment, and mortality better than anyone. But that’s not always the case.

So, what happens when oncology practitioners trade their white coat for a hospital gown? Does their expertise equip them to navigate their diagnosis and treatment better than their patients? How does the emotional toll of their personal cancer journey change the way they interact with their patients?
 

Navigating the diagnosis and treatment

In January 2017, Karen Hendershott, MD, a breast surgical oncologist, felt a lump in her armpit while taking a shower. The blunt force of her fate came into view in an instant: It was almost certainly a locally advanced breast cancer that had spread to her lymph nodes and would require surgery, radiotherapy, and chemotherapy.

She said a few unprintable words and headed to work at St. Mary’s Hospital, in Tucson, Ariz., where her assumptions were confirmed.

Taylor Riall, MD, PhD, also suspected cancer.

Last December, Dr. Riall, a general surgeon and surgical oncologist at the University of Arizona Cancer Center, in Tucson, developed a persistent cough. An x-ray revealed a mass in her lung. Initially, she was misdiagnosed with a fungal infection and was given medication that made her skin peel off.

Doctors advised Dr. Riall to monitor her condition for another 6 months. But her knowledge of oncology made her think cancer, so she insisted on more tests. In June 2021, a biopsy confirmed she had lung cancer.

Having oncology expertise helped Dr. Riall and Dr. Hendershott recognize the signs of cancer and push for a diagnosis. But there are also downsides to being hyper-informed, Dr. Hendershott, said.

“I think sometimes knowing everything at once is harder vs. giving yourself time to wrap your mind around this and do it in baby steps,” she explained. “There weren’t any baby steps here.”

Still, oncology practitioners who are diagnosed with cancer are navigating a familiar landscape and are often buoyed by a support network of expert colleagues. That makes a huge difference psychologically, explained Shenitha Edwards, a pharmacy technician at Cancer Specialists of North Florida, in Jacksonville, who was diagnosed with breast cancer in July.

“I felt stronger and a little more ready to fight because I had resources, whereas my patients sometimes do not,” Ms. Edwards said. “I was connected with a lot of people who could help me make informed decisions, so I didn’t have to walk so much in fear.”

It can also prepare practitioners to make bold treatment choices. In Dr. Riall’s case, surgeons were reluctant to excise her tumor because they would have to remove the entire upper lobe of her lung, and she is a marathoner and triathlete. Still, because of her surgical oncology experience, Dr. Riall didn’t flinch at the prospect of a major operation.

“I was, like, ‘Look, just take it out.’ I’m less afraid to have cancer than I am to not know and let it grow,” said Dr. Riall, whose Peloton name is WhoNeeds2Lungs.

Similarly, Dr. Hendershott’s experience gave her the assurance to pursue a more intense strategy. “Because I had a really candid understanding of the risks and what the odds looked like, it helped me be more comfortable with a more aggressive approach,” she said. “There wasn’t a doubt in my mind, particularly [having] a 10-year-old child, that I wanted to do everything I could, and even do a couple of things that were still in clinical trials.”

Almost paradoxically, Mark Lewis’ oncology training gave him the courage to risk watching and waiting after finding benign growths in his parathyroid and malignant tumors in his pancreas. Dr. Lewis monitored the tumors amassing in his pancreas for 8 years. When some grew so large they threatened to metastasize to his liver, he underwent the Whipple procedure to remove the head of the pancreas, part of the small intestine, and the gallbladder.

“It was a bit of a gamble, but one that paid off and allowed me to get my career off the ground and have another child,” said Dr. Lewis, a gastrointestinal oncologist at Intermountain Healthcare, in Salt Lake City. Treating patients for nearly a decade also showed him how fortunate he was to have a slow-growing, operable cancer. That gratitude, he said, gave him mental strength to endure the ordeal.

Whether taking a more aggressive or minimalist approach to their own care, each practitioner’s decision was deeply personal and deeply informed by their oncology expertise.

Although research on this question is scarce, studies show that differences in end-of-life care may occur. According to a 2016 study published in JAMA, physicians choose significantly less intensive end-stage care in three of five categories — undergoing surgery, being admitted to the intensive care unit (ICU), and dying in the hospital — than the general U.S. population. The reason, the researchers posited, is because doctors know these eleventh-hour interventions are typically brutal and futile.

But these differences were fairly small, and a 2019 study published in JAMA Open Network found the opposite: Physicians with cancer were more likely to die in an ICU and receive chemotherapy in the last 6 months of life, suggesting a more aggressive approach to end-of-life care.

When it comes to their own long-term or curative cancer care, oncologists generally don’t seem to approach treatment differently than their patients. In a 2015 study, researchers compared two groups of people with early breast cancer — 46 physicians and 230 well-educated, nonmedically qualified patients — and found no differences in the choices the groups made about whether to undergo mastectomy, chemotherapy, radiotherapy, or breast reconstruction.

Still, no amount of oncology expertise can fully prepare a person for the emotional crucible of cancer.
 

“A very surreal experience”

Although the fear can become less intense and more manageable over time, it may never truly go away.

At first, despair dragged Flora into an abyss for 6 hours a night, then overcame him 10 times a day, then gripped him briefly at random moments. Four years later and cancer-free, the dread still returns.

Hendershott cried every time she got into her car and contemplated her prognosis. Now 47, she has about a 60% chance of being alive in 15 years, and the fear still hits her.

“I think it’s hard to understand the moments of sheer terror that you have at 2 AM when you’re confronting your own mortality,” she said. “The implications that has not just for you but more importantly for the people that you love and want to protect. That just kind of washes over you in waves that you don’t have much control over.”

Cancer, Riall felt, had smashed her life, but she figured out a way to help herself cope. Severe blood loss, chest tubes, and tests and needles ad nauseum left Riall feeling excruciatingly exhausted after her surgery and delayed her return to work. At the same time, she was passed over for a promotion. Frustrated and dejected, she took comfort in the memory of doing Kintsugi with her surgery residents. The Japanese art form involves shattering pottery with a hammer, fitting the fragments back together, and painting the cracks gold.

“My instinct as a surgeon is to pick up those pieces and put them back together so nobody sees it’s broken,” she reflected. But as a patient, she learned that an important part of recovery is to allow yourself to sit in a broken state and feel angry, miserable, and betrayed by your body. And then examine your shattered priorities and consider how you want to reassemble them.

For Barbara Buttin, MD, a gynecologic oncologist at Cancer Treatment Centers of America, in Chicago, Illinois, it wasn’t cancer that almost took her life. Rather, a near-death experience and life-threatening diagnosis made her a better, more empathetic cancer doctor — a refrain echoed by many oncologist-patients. Confronting her own mortality crystallized what matters in life. She uses that understanding to make sure she understands what matters to her patients ― what they care about most, what their greatest fear is, what is going to keep them up at night.
 

“We’re part of the same club”

Ultimately, when oncology practitioners become patients, it balances the in-control and vulnerable, the rational and emotional. And their patients respond positively.

In fall 2020, oncology nurse Jenn Adams, RN, turned 40 and underwent her first mammogram. Unexpectedly, it revealed invasive stage I cancer that would require a double mastectomy, chemotherapy, and a year of immunotherapy. A week after her diagnosis, she was scheduled to start a new job at Cancer Clinic, in Bryan, Tex. So, she asked her manager if she could become a patient and an employee.

Ms. Adams worked 5 days a week, but every Thursday at 2 PM, she sat next to her patients while her coworkers became her nurses. Her chemo port was implanted, she lost her hair, and she felt terrible along with her patients. “It just created this incredible bond,” said the mother of three.

Having cancer, Dr. Flora said, “was completely different than I had imagined. When I thought I was walking with [my patients] in the depths of their caves, I wasn’t even visiting their caves.” But, he added, it has also “let me connect with [patients] on a deeper level because we’re part of the same club. You can see their body language change when I share that. They almost relax, like, ‘Oh, this guy gets it. He does understand how terrified I am.’ And I do.”

When Dr. Flora’s patients are scanned, he gives them their results immediately, because he knows what it’s like to wait on tenterhooks. He tells his patients to text him anytime they’re afraid or depressed, which he admits isn’t great for his own mental health but believes is worth it.

Likewise, Dr. Hendershott can hold out her shoulder-length locks to reassure a crying patient that hair does grow back after chemo. She can describe her experience with hormone-blocking pills to allay the fears of a pharmaceutical skeptic.

This role equalizer fosters so much empathy that doctors sometimes find themselves being helped by their patients. When one of Dr. Flora’s patients heard he had cancer, she sent him an email that began. “A wise doctor once told me....” and repeated the advice he’d given her years before.

Dr. Lewis has a special bond with his patients because people who have pancreatic neuroendocrine tumors seek him out for treatment. “I’m getting to take care of people who, on some level, are like my kindred spirits,” he said. “So, I get to see their coping mechanisms and how they do.”

Ms. Edwards told some of her patients about her breast cancer diagnosis, and now they give each other high-fives and share words of encouragement. “I made it a big thing of mine to associate my patients as my family,” she said. “If you’ve learned to embrace love and love people, there’s nothing you wouldn’t do for people. I’ve chosen that to be my practice when I’m dealing with all of my patients.”

Ms. Adams is on a similar mission. She joined a group of moms with cancer so she can receive guidance and then become a guide for others. “I feel like that’s what I want to be at my cancer practice,” she said, “so [my patients] have someone to say, ‘I’m gonna walk alongside you because I’ve been there.’ “

That transformation has made all the heartbreaking moments worth it, Ms. Adams said. “I love the oncology nurse that I get to be now because of my diagnosis. I don’t love the diagnosis. But I love the way it’s changed what I do.”

A version of this article first appeared on Medscape.com.

Uncontrolled growth isn’t the only way cancers wreak havoc on the human body. These aggregations of freely dividing cells also release chemicals that can cause damage from a distance. But pinning down how they harm faraway healthy tissues isn’t straightforward.

Fortunately, biologists can turn to the tiny fruit fly to address some of these questions: This insect’s body is not as complex as ours in many ways, but we share important genes and organ functions.

Fruit flies already are a crucial and inexpensive animal for genetics research. Because their life span is about 7 weeks, investigators can track the effects of mutations across several generations in a short period. The animals also are proving useful for learning how chemicals released by malignant tumors can harm tissues in the body that are not near the cancer.

One recent lesson from the fruit flies involves the blood-brain barrier, which determines which molecules gain access to the brain. Researchers at the University of California, Berkeley, have found that malignant tumors in the tiny insects release interleukin 6 (IL-6), an inflammatory chemical that disrupts this important barrier. The investigators showed that the tumors act similarly in mice.

When the scientists blocked the effects of IL-6, both the fruit flies and the mice lived longer. Even if cancer cells persisted, damage related to IL-6 could be diminished.

Fruit flies and mice are only distant relatives of each other and of humans, and the relevance of this discovery to human cancers has not been established. One hurdle is that IL-6 has many important, normal functions related to health. Researchers need to learn how to target only its unwanted blood-brain barrier effects.

A version of this article first appeared on Medscape.com.

Uncontrolled growth isn’t the only way cancers wreak havoc on the human body. These aggregations of freely dividing cells also release chemicals that can cause damage from a distance. But pinning down how they harm faraway healthy tissues isn’t straightforward.

Fortunately, biologists can turn to the tiny fruit fly to address some of these questions: This insect’s body is not as complex as ours in many ways, but we share important genes and organ functions.

Fruit flies already are a crucial and inexpensive animal for genetics research. Because their life span is about 7 weeks, investigators can track the effects of mutations across several generations in a short period. The animals also are proving useful for learning how chemicals released by malignant tumors can harm tissues in the body that are not near the cancer.

One recent lesson from the fruit flies involves the blood-brain barrier, which determines which molecules gain access to the brain. Researchers at the University of California, Berkeley, have found that malignant tumors in the tiny insects release interleukin 6 (IL-6), an inflammatory chemical that disrupts this important barrier. The investigators showed that the tumors act similarly in mice.

When the scientists blocked the effects of IL-6, both the fruit flies and the mice lived longer. Even if cancer cells persisted, damage related to IL-6 could be diminished.

Fruit flies and mice are only distant relatives of each other and of humans, and the relevance of this discovery to human cancers has not been established. One hurdle is that IL-6 has many important, normal functions related to health. Researchers need to learn how to target only its unwanted blood-brain barrier effects.

A version of this article first appeared on Medscape.com.

Uncontrolled growth isn’t the only way cancers wreak havoc on the human body. These aggregations of freely dividing cells also release chemicals that can cause damage from a distance. But pinning down how they harm faraway healthy tissues isn’t straightforward.

Fortunately, biologists can turn to the tiny fruit fly to address some of these questions: This insect’s body is not as complex as ours in many ways, but we share important genes and organ functions.

Fruit flies already are a crucial and inexpensive animal for genetics research. Because their life span is about 7 weeks, investigators can track the effects of mutations across several generations in a short period. The animals also are proving useful for learning how chemicals released by malignant tumors can harm tissues in the body that are not near the cancer.

One recent lesson from the fruit flies involves the blood-brain barrier, which determines which molecules gain access to the brain. Researchers at the University of California, Berkeley, have found that malignant tumors in the tiny insects release interleukin 6 (IL-6), an inflammatory chemical that disrupts this important barrier. The investigators showed that the tumors act similarly in mice.

When the scientists blocked the effects of IL-6, both the fruit flies and the mice lived longer. Even if cancer cells persisted, damage related to IL-6 could be diminished.

Fruit flies and mice are only distant relatives of each other and of humans, and the relevance of this discovery to human cancers has not been established. One hurdle is that IL-6 has many important, normal functions related to health. Researchers need to learn how to target only its unwanted blood-brain barrier effects.

A version of this article first appeared on Medscape.com.

The Diagnosis: Calcinosis Cutis Due to Systemic Sclerosis Sine Scleroderma

Laboratory evaluation was notable for high titers of antinuclear antibodies (>1/320; reference range, 0–1/80) and positive anticentromere antibodies. There were no other relevant laboratory findings; phosphocalcic metabolism was within normal limits, and urinary sediment was normal. Biopsy of the edge of the ulcer revealed basophilic material compatible with calcium deposits. In a 3D volume rendering reconstruction from the lower limb scanner, grouped calcifications were observed in subcutaneous cellular tissue near the ulcer (Figure). The patient had a restrictive ventilatory pattern observed in a pulmonary function test. An esophageal motility study was normal.

The patient was diagnosed with systemic sclerosis sine scleroderma (ssSSc) type II because she met the 4 criteria established by Poormoghim et al¹ : (1) Raynaud phenomenon or a peripheral vascular equivalent (ie, digital pitting scars, digital-tip ulcers, digital-tip gangrene, abnormal nail fold capillaries); (2) positive antinuclear antibodies; (3) distal esophageal hypomotility, small bowel hypomotility, pulmonary interstitial fibrosis, primary pulmonary arterial hypertension (without fibrosis), cardiac involvement typical of scleroderma, or renal failure; and (4) no other defined connective tissue or other disease as a cause of the prior conditions.

Systemic sclerosis is a chronic disease characterized by progressive fibrosis of the skin and other internal organs—especially the lungs, kidneys, digestive tract, and heart—as well as generalized vascular dysfunction. Cutaneous induration is its hallmark; however, up to 10% of affected patients have ssSSc.² This entity is characterized by the total or partial absence of cutaneous manifestations of systemic sclerosis with the occurrence of internal organ involvement and serologic abnormalities. There are 3 types of ssSSc depending on the grade of skin involvement. Type I is characterized by the lack of any typical cutaneous stigmata of the disease. Type II is without sclerodactyly but can coexist with other cutaneous findings such as calcifications, telangiectases, or pitting scars. Type III is characterized clinically by internal organ involvement, typical of systemic sclerosis, that has appeared before skin changes.²

An abnormal deposit of calcium in the cutaneous and subcutaneous tissue is called calcinosis cutis. There are 5 subtypes of calcinosis cutis: dystrophic, metastatic, idiopathic, iatrogenic, and calciphylaxis. Dystrophic skin calcifications may appear in patients with connective tissue diseases such as dermatomyositis or systemic sclerosis.³ Up to 25% of patients with systemic sclerosis can develop calcinosis cutis due to local tissue damage, with normal phosphocalcic metabolism.³

Calcinosis cutis is more common in patients with systemic sclerosis and positive anticentromere antibodies.⁴ The calcifications usually are located in areas that are subject to repeated trauma, such as the fingers or arms, though other locations have been described such as cervical, paraspinal, or on the hips.^5,6 Our patient developed calcifications on both legs, which represent atypical areas for this process.

Dermatomyositis also can present with calcinosis cutis. There are 4 patterns of calcification: superficial nodulelike calcified masses; deep calcified masses; deep sheetlike calcifications within the fascial planes; and a rare, diffuse, superficial lacy and reticular calcification that involves almost the entire body surface area.⁷ Patients with calcinosis cutis secondary to dermatomyositis usually develop proximal muscle weakness, high titers of creatine kinase, heliotrope rash, or interstitial lung disease with specific antibodies.

Calciphylaxis is a serious disorder involving the calcification of dermal and subcutaneous arterioles and capillaries. It presents with painful cutaneous areas of necrosis.

Venous ulcers also can present with secondary dystrophic calcification due to local tissue damage. These patients usually have cutaneous signs of chronic venous insufficiency. Our patient denied prior trauma to the area; therefore, a traumatic ulcer with secondary calcification was ruled out.

The most concerning complication of calcinosis cutis is the development of ulcers, which occurred in 154 of 316 calcinoses (48.7%) in patients with systemic sclerosis and secondary calcifications.⁸ These ulcers can cause disabling pain or become superinfected, as in our patient.

There currently is no drug capable of removing dystrophic calcifications, but diltiazem, minocycline, or colchicine can reduce their size and prevent their progression. In the event of neurologic compromise or intractable pain, the treatment of choice is surgical removal of the calcification.⁹ Curettage, intralesional sodium thiosulfate, and intravenous sodium thiosulfate also have been suggested as therapeutic options.¹⁰ Antibiotic treatment was carried out in our patient, which controlled the superinfection of the ulcers. Diltiazem also was started, with stabilization of the calcium deposits without a reduction in their size.

There are few studies evaluating the presence of nondigital ulcers in patients with systemic sclerosis. Shanmugam et al¹¹ calculated a 4% (N=249) prevalence of ulcers in the lower limbs of systemic sclerosis patients. In a study by Bohelay et al¹² of 45 patients, the estimated prevalence of lower limb ulcers was 12.8%, and the etiologies consisted of 22 cases of venous insufficiency (49%), 21 cases of ischemic causes (47%), and 2 cases of other causes (4%).

We present the case of a woman with ssSSc who developed dystrophic calcinosis cutis in atypical areas with secondary ulceration and superinfection. The skin usually plays a key role in the diagnosis of systemic sclerosis, as sclerodactyly and the characteristic generalized skin induration stand out in affected individuals. Although our patient was diagnosed with ssSSc, her skin manifestations also were crucial for the diagnosis, as she had ulcers on the lower limbs.

The Diagnosis: Calcinosis Cutis Due to Systemic Sclerosis Sine Scleroderma

Laboratory evaluation was notable for high titers of antinuclear antibodies (>1/320; reference range, 0–1/80) and positive anticentromere antibodies. There were no other relevant laboratory findings; phosphocalcic metabolism was within normal limits, and urinary sediment was normal. Biopsy of the edge of the ulcer revealed basophilic material compatible with calcium deposits. In a 3D volume rendering reconstruction from the lower limb scanner, grouped calcifications were observed in subcutaneous cellular tissue near the ulcer (Figure). The patient had a restrictive ventilatory pattern observed in a pulmonary function test. An esophageal motility study was normal.

The patient was diagnosed with systemic sclerosis sine scleroderma (ssSSc) type II because she met the 4 criteria established by Poormoghim et al¹ : (1) Raynaud phenomenon or a peripheral vascular equivalent (ie, digital pitting scars, digital-tip ulcers, digital-tip gangrene, abnormal nail fold capillaries); (2) positive antinuclear antibodies; (3) distal esophageal hypomotility, small bowel hypomotility, pulmonary interstitial fibrosis, primary pulmonary arterial hypertension (without fibrosis), cardiac involvement typical of scleroderma, or renal failure; and (4) no other defined connective tissue or other disease as a cause of the prior conditions.

Systemic sclerosis is a chronic disease characterized by progressive fibrosis of the skin and other internal organs—especially the lungs, kidneys, digestive tract, and heart—as well as generalized vascular dysfunction. Cutaneous induration is its hallmark; however, up to 10% of affected patients have ssSSc.² This entity is characterized by the total or partial absence of cutaneous manifestations of systemic sclerosis with the occurrence of internal organ involvement and serologic abnormalities. There are 3 types of ssSSc depending on the grade of skin involvement. Type I is characterized by the lack of any typical cutaneous stigmata of the disease. Type II is without sclerodactyly but can coexist with other cutaneous findings such as calcifications, telangiectases, or pitting scars. Type III is characterized clinically by internal organ involvement, typical of systemic sclerosis, that has appeared before skin changes.²

An abnormal deposit of calcium in the cutaneous and subcutaneous tissue is called calcinosis cutis. There are 5 subtypes of calcinosis cutis: dystrophic, metastatic, idiopathic, iatrogenic, and calciphylaxis. Dystrophic skin calcifications may appear in patients with connective tissue diseases such as dermatomyositis or systemic sclerosis.³ Up to 25% of patients with systemic sclerosis can develop calcinosis cutis due to local tissue damage, with normal phosphocalcic metabolism.³

Calcinosis cutis is more common in patients with systemic sclerosis and positive anticentromere antibodies.⁴ The calcifications usually are located in areas that are subject to repeated trauma, such as the fingers or arms, though other locations have been described such as cervical, paraspinal, or on the hips.^5,6 Our patient developed calcifications on both legs, which represent atypical areas for this process.

Dermatomyositis also can present with calcinosis cutis. There are 4 patterns of calcification: superficial nodulelike calcified masses; deep calcified masses; deep sheetlike calcifications within the fascial planes; and a rare, diffuse, superficial lacy and reticular calcification that involves almost the entire body surface area.⁷ Patients with calcinosis cutis secondary to dermatomyositis usually develop proximal muscle weakness, high titers of creatine kinase, heliotrope rash, or interstitial lung disease with specific antibodies.

Calciphylaxis is a serious disorder involving the calcification of dermal and subcutaneous arterioles and capillaries. It presents with painful cutaneous areas of necrosis.

Venous ulcers also can present with secondary dystrophic calcification due to local tissue damage. These patients usually have cutaneous signs of chronic venous insufficiency. Our patient denied prior trauma to the area; therefore, a traumatic ulcer with secondary calcification was ruled out.

The most concerning complication of calcinosis cutis is the development of ulcers, which occurred in 154 of 316 calcinoses (48.7%) in patients with systemic sclerosis and secondary calcifications.⁸ These ulcers can cause disabling pain or become superinfected, as in our patient.

There currently is no drug capable of removing dystrophic calcifications, but diltiazem, minocycline, or colchicine can reduce their size and prevent their progression. In the event of neurologic compromise or intractable pain, the treatment of choice is surgical removal of the calcification.⁹ Curettage, intralesional sodium thiosulfate, and intravenous sodium thiosulfate also have been suggested as therapeutic options.¹⁰ Antibiotic treatment was carried out in our patient, which controlled the superinfection of the ulcers. Diltiazem also was started, with stabilization of the calcium deposits without a reduction in their size.

There are few studies evaluating the presence of nondigital ulcers in patients with systemic sclerosis. Shanmugam et al¹¹ calculated a 4% (N=249) prevalence of ulcers in the lower limbs of systemic sclerosis patients. In a study by Bohelay et al¹² of 45 patients, the estimated prevalence of lower limb ulcers was 12.8%, and the etiologies consisted of 22 cases of venous insufficiency (49%), 21 cases of ischemic causes (47%), and 2 cases of other causes (4%).

We present the case of a woman with ssSSc who developed dystrophic calcinosis cutis in atypical areas with secondary ulceration and superinfection. The skin usually plays a key role in the diagnosis of systemic sclerosis, as sclerodactyly and the characteristic generalized skin induration stand out in affected individuals. Although our patient was diagnosed with ssSSc, her skin manifestations also were crucial for the diagnosis, as she had ulcers on the lower limbs.

The Diagnosis: Calcinosis Cutis Due to Systemic Sclerosis Sine Scleroderma

Laboratory evaluation was notable for high titers of antinuclear antibodies (>1/320; reference range, 0–1/80) and positive anticentromere antibodies. There were no other relevant laboratory findings; phosphocalcic metabolism was within normal limits, and urinary sediment was normal. Biopsy of the edge of the ulcer revealed basophilic material compatible with calcium deposits. In a 3D volume rendering reconstruction from the lower limb scanner, grouped calcifications were observed in subcutaneous cellular tissue near the ulcer (Figure). The patient had a restrictive ventilatory pattern observed in a pulmonary function test. An esophageal motility study was normal.

The patient was diagnosed with systemic sclerosis sine scleroderma (ssSSc) type II because she met the 4 criteria established by Poormoghim et al¹ : (1) Raynaud phenomenon or a peripheral vascular equivalent (ie, digital pitting scars, digital-tip ulcers, digital-tip gangrene, abnormal nail fold capillaries); (2) positive antinuclear antibodies; (3) distal esophageal hypomotility, small bowel hypomotility, pulmonary interstitial fibrosis, primary pulmonary arterial hypertension (without fibrosis), cardiac involvement typical of scleroderma, or renal failure; and (4) no other defined connective tissue or other disease as a cause of the prior conditions.

Systemic sclerosis is a chronic disease characterized by progressive fibrosis of the skin and other internal organs—especially the lungs, kidneys, digestive tract, and heart—as well as generalized vascular dysfunction. Cutaneous induration is its hallmark; however, up to 10% of affected patients have ssSSc.² This entity is characterized by the total or partial absence of cutaneous manifestations of systemic sclerosis with the occurrence of internal organ involvement and serologic abnormalities. There are 3 types of ssSSc depending on the grade of skin involvement. Type I is characterized by the lack of any typical cutaneous stigmata of the disease. Type II is without sclerodactyly but can coexist with other cutaneous findings such as calcifications, telangiectases, or pitting scars. Type III is characterized clinically by internal organ involvement, typical of systemic sclerosis, that has appeared before skin changes.²

An abnormal deposit of calcium in the cutaneous and subcutaneous tissue is called calcinosis cutis. There are 5 subtypes of calcinosis cutis: dystrophic, metastatic, idiopathic, iatrogenic, and calciphylaxis. Dystrophic skin calcifications may appear in patients with connective tissue diseases such as dermatomyositis or systemic sclerosis.³ Up to 25% of patients with systemic sclerosis can develop calcinosis cutis due to local tissue damage, with normal phosphocalcic metabolism.³

Calcinosis cutis is more common in patients with systemic sclerosis and positive anticentromere antibodies.⁴ The calcifications usually are located in areas that are subject to repeated trauma, such as the fingers or arms, though other locations have been described such as cervical, paraspinal, or on the hips.^5,6 Our patient developed calcifications on both legs, which represent atypical areas for this process.

Dermatomyositis also can present with calcinosis cutis. There are 4 patterns of calcification: superficial nodulelike calcified masses; deep calcified masses; deep sheetlike calcifications within the fascial planes; and a rare, diffuse, superficial lacy and reticular calcification that involves almost the entire body surface area.⁷ Patients with calcinosis cutis secondary to dermatomyositis usually develop proximal muscle weakness, high titers of creatine kinase, heliotrope rash, or interstitial lung disease with specific antibodies.

Calciphylaxis is a serious disorder involving the calcification of dermal and subcutaneous arterioles and capillaries. It presents with painful cutaneous areas of necrosis.

Venous ulcers also can present with secondary dystrophic calcification due to local tissue damage. These patients usually have cutaneous signs of chronic venous insufficiency. Our patient denied prior trauma to the area; therefore, a traumatic ulcer with secondary calcification was ruled out.

The most concerning complication of calcinosis cutis is the development of ulcers, which occurred in 154 of 316 calcinoses (48.7%) in patients with systemic sclerosis and secondary calcifications.⁸ These ulcers can cause disabling pain or become superinfected, as in our patient.

There currently is no drug capable of removing dystrophic calcifications, but diltiazem, minocycline, or colchicine can reduce their size and prevent their progression. In the event of neurologic compromise or intractable pain, the treatment of choice is surgical removal of the calcification.⁹ Curettage, intralesional sodium thiosulfate, and intravenous sodium thiosulfate also have been suggested as therapeutic options.¹⁰ Antibiotic treatment was carried out in our patient, which controlled the superinfection of the ulcers. Diltiazem also was started, with stabilization of the calcium deposits without a reduction in their size.

There are few studies evaluating the presence of nondigital ulcers in patients with systemic sclerosis. Shanmugam et al¹¹ calculated a 4% (N=249) prevalence of ulcers in the lower limbs of systemic sclerosis patients. In a study by Bohelay et al¹² of 45 patients, the estimated prevalence of lower limb ulcers was 12.8%, and the etiologies consisted of 22 cases of venous insufficiency (49%), 21 cases of ischemic causes (47%), and 2 cases of other causes (4%).

We present the case of a woman with ssSSc who developed dystrophic calcinosis cutis in atypical areas with secondary ulceration and superinfection. The skin usually plays a key role in the diagnosis of systemic sclerosis, as sclerodactyly and the characteristic generalized skin induration stand out in affected individuals. Although our patient was diagnosed with ssSSc, her skin manifestations also were crucial for the diagnosis, as she had ulcers on the lower limbs.

In 2018, Atashi Mandal, MD, a hospitalist residing in Orange County, Calif., was recruited along with several other doctors to fill hospitalist positions in rural Bishop, Calif. She has since driven 600 miles round trip every month for a week of hospital medicine shifts at Northern Inyo Hospital.

Dr. Mandal said she has really enjoyed her time at the small rural hospital and found it professionally fulfilling to participate so fully in the health of its local community. She was building personal bonds and calling the experience the pinnacle of her career when the COVID-19 pandemic swept across America and the world, even reaching up into Bishop, population 3,760, in the isolated Owens Valley.

The 25-bed hospital has seen at least 100 COVID patients in the past year and some months. Responsibility for taking care of these patients has been both humbling and gratifying, Dr. Mandal said. The facility’s hospitalists made a commitment to keep working through the pandemic. “We were able to come together (around COVID) as a team and our teamwork really made a difference,” she said.

“One of the advantages in a smaller hospital is you can have greater cohesiveness and your communication can be tighter. That played a big role in how we were able to accomplish so much with fewer resources as a rural hospital.” But staffing shortages, recruitment, and retention remain a perennial challenge for rural hospitals. “And COVID only exacerbated the problems,” she said. “I’ve had my challenges trying to make proper treatment plans without access to specialists.”

It was also difficult to witness so many patients severely ill or dying from COVID, Dr. Mandal said, especially since patients were not allowed family visitors – even though that was for a good reason, to minimize the virus’s spread.

HM in rural communities

Hospital medicine continues to extend into rural communities and small rural hospitals. In 2018, 35.7% of all rural counties in America had hospitals staffed with hospitalists, and 63.3% of rural hospitals had hospitalist programs (compared with 79.2% of urban hospitals). These numbers come from Medicare resources files from the Department of Health & Human Services, analyzed by Peiyin Hung, PhD, assistant professor of health services management and policy at the University of South Carolina, Columbia.¹ Hospitalist penetration rates rose steadily from 2011 to 2017, with a slight dip in 2018, Dr. Hung said in an interview.

A total of 138 rural hospitals have closed since 2010, according to the Cecil G. Sheps Center for Health Services Research in Chapel Hill, N.C. Nineteen rural hospitals closed in 2020 alone, although many of those were caused by factors predating the pandemic. Only one has closed so far in 2021. But financial pressures, including low patient volumes and loss of revenue from canceled routine services like elective surgeries during the pandemic, have added to hospitals’ difficulties. Pandemic relief funding may have helped some hospitals stay open, but that support eventually will go away.

Experts emphasize the diversity of rural America and its health care systems. Rural economies are volatile and more diverse than is often appreciated. The hospital may be a cornerstone of the local economy; when one closes, it can devastate the community. Workforce is one of the chief components of a hospital’s ability to meet its strategic vision, and hospitalists are a big part in that. But while hospitalists are valued and appreciated, if the hospital is suffering severe financial problems, that will impact its doctors’ jobs and livelihoods.

“Bandwidth” varies widely for rural hospitalists and their hospitalist groups, said Ken Simone, DO, SFHM, executive chair of SHM’s Rural Special Interest Group and founder and principal of KGS Consultants, a Hospital Medicine and Primary Care Practice Management Consulting company. They may face scarce resources, scarce clinical staffing, lack of support staff to help operations run smoothly, lack of access to specialists locally, and lack of technology. While practicing in a rural setting presents various challenges, it can be rewarding for those clinicians who embrace its autonomy and broad scope of services, Dr. Simone said.

SHM’s Rural SIG focuses on the unique needs of rural hospitalists, providing them with an opportunity to share their concerns, challenges and solutions through roundtable discussions every other month and a special interest forum held in conjunction with the SHM Converge annual conference, Dr. Simone said. (The next SHM Converge will be April 7-10, 2022, in Nashville, Tenn.) The Rural SIG also collaborates with other hospital medicine SIGs and committees and is working on a white paper, “Key Principles and Characteristics of an Effective Rural Hospital Medicine Group.” It is also looking to develop a rural mentorship exchange program.

COVID reaches rural America

Early COVID caseloads tended to be in urban areas, but subsequent surges of infections have spread to many rural areas. Some rural settings became epicenters for the pandemic in November and December 2020. More recent troubling rises in COVID cases, particularly in areas with lower vaccination rates – suggest that the challenges of the pandemic are still not behind us.

“By no means is the crisis done in rural America,” said Alan Morgan, CEO of the National Rural Health Association, in a Virtual Rural Health Journalism workshop on rural health care sponsored by the Association of Health Care Journalists.²

Mr. Morgan’s colleague, Brock Slabach, NRHA’s chief operations officer, said in an interview that, while 453 of the 1,800 hospitals in rural areas fit NRHA’s criteria as being vulnerable to closure, the rest are not, and are fulfilling their missions for their communities. Hospitalists are becoming more common in these hospitals, he said, and rural hospitalists can be an important asset in attracting primary care physicians – who might not appreciate being perpetually on call for their hospitalized patients – to rural communities.

In many cases, traveling doctors like Dr. Mandal or telemedicine backup, particularly for after-hours coverage or ICU beds, are important pieces of the puzzle for smaller hospitals. There are different ways to use the spectrum of telemedicine services to interact with a hospital’s daytime and night routines. In some isolated locations, nurse practitioners or physician assistants provide on-the-ground coverage with virtual backup. Rural hospitals often affiliate with telemedicine networks within health systems – or else contract with independent specialized providers of telemedicine consultation.

Mr. Slabach said another alternative for staffing hospitals with smaller ED and inpatient volumes is to have one doctor on duty who can cover both departments simultaneously. Meanwhile, the new federal Rural Emergency Hospital Program proposes to allow rural hospitals to become essentially freestanding EDs – starting Jan. 1, 2023 – that can manage patients for a maximum of 24 hours.³

Community connections and proactive staffing

Lisa Kaufmann, MD, works as a hospitalist for a two-hospital system in North Carolina, Appalachian Regional Health Care. She practices at Watauga Medical Center, with 100 licensed beds in Boone, and at Cannon Memorial Hospital, a critical access hospital in unincorporated Linville. “We are proud of what we have been able to accomplish during the pandemic,” she said.

A former critical care unit at Watauga had been shut down, but its wiring remained intact. “We turned it into a COVID unit in three days. Then we opened another COVID unit with 18 beds, but that still wasn’t enough. We converted half of our med/surg capacity into a COVID unit. At one point almost half of all of our acute beds were for COVID patients. We made plans for what we would do if it got worse, since we had almost run out of beds,” she said. Demand peaked at the end of January 2021.

“The biggest barrier for us was if someone needed to be transferred, for example, if they needed ECMO [extracorporeal membrane oxygenation], and we couldn’t find another hospital to provide that technology.” In ARHC’s mountainous region – known as the “High Country” – weather can also make it difficult to transport patients. “Sometimes the ambulance can’t make it off the mountain, and half of the time the medical helicopter can’t fly. So we have to be prepared to keep people who we might think ought to be transferred,” she said.

Like many rural communities, the High Country is tightly knit, and its hospitals are really connected to their communities, Dr. Kaufmann said. The health system already had a lot of community connections beyond acute care, and that meant the pandemic wasn’t experienced as severely as it was in some other rural communities. “But without hospitalists in our hospitals, it would have been much more difficult.”

Proactive supply fulfillment meant that her hospitals never ran out of personal protective equipment. “Staffing was a challenge, but we were proactive in getting traveling doctors to come here. We also utilized extra doctors from the local community,” she said. Another key was well-established disaster planning, with regular drills, and a robust incident command structure, which just needed to be activated in the crisis. “Small hospitals need to be prepared for disaster,” Dr. Kaufmann said.

For Dale Wiersma, MD, a hospitalist with Spectrum Health, a 14-hospital system in western Michigan, telemedicine services are coordinated across 8 rural regional hospitals. “We don’t tend to use it for direct hospitalist work during daytime hours, unless a facility is swamped, in which case we can cross-cover. We do more telemedicine at night. But during daytime hours we have access to stroke neurology, cardiology, psychiatry, critical care and infectious disease specialists who are able to offer virtual consults,” Dr. Wiersma said. A virtual critical care team of doctor and nurse is often the only intensivist service covering Spectrum’s rural hospitals.

“In our system, the pandemic accelerated the adoption of telemedicine,” Dr. Wiersma said. “We had been working on the tele-ICU program, trying to get it rolled out. When the pandemic hit, we launched it in just 6 weeks.”

There have been several COVID surges in Michigan, he said. “We were stretched pretty close to our limit several times, but never to the breaking point. For our physicians, it was the protracted nature of the pandemic that was fatiguing for everyone involved. Our system worked hard to staff up as well as it could, to make sure our people didn’t go over the edge.” It was also hard for hospitals that typically might see one or two deaths in a month to suddenly have five in a week.

Another Spectrum hospitalist, Christopher Skinner, MD, works at two rural Michigan hospitals 15 minutes apart in Big Rapids and Reed City. “I prefer working in rural areas. I’ve never had an ambition to be a top dog. I like the style of practice where you don’t have all of the medical subspecialties on site. It frees you up to use all your skills,” Dr. Skinner said.

But that approach was put to the test by the pandemic, since it was harder to transfer those patients who normally would not have stayed at these rural hospitals. “We had to make do,” he said, although virtual backup and second opinions from Spectrum’s virtual critical care team helped.

“It was a great collaboration, which helped us to handle critical care cases that we hadn’t had to manage pre-COVID. We’ve gotten used to it, with the backup, so I expect we’ll still be taking care of these kind of sick ventilator patients even after the pandemic ends,” Dr. Skinner said. “We’ve gotten pretty good at it.”

Sukhbir Pannu, MD, a hospitalist in Denver and CEO and founder of Rural Physicians Group, said the pandemic was highly impactful, operationally and logistically, for his firm, which contracts with 54 hospitals to provide their hospitalist staffing. “There was no preparation. Everything had to be done on the fly. Initially, it was felt that rural areas weren’t at as great a risk for COVID, but that proved not to be true. Many experienced a sudden increase in very sick patients. We set up a task force to manage daily census in all of our contracted facilities.”

How did Rural Physicians Group manage through the crisis? “The short answer is telemedicine,” he said. “We had physicians on the ground in these hospitals. But we needed intensivists at the other end of the line to support them.” A lot of conversations about telemedicine were already going on in the company, but the pandemic provided the impetus to launch its network, which has grown to include rheumatologists, pulmonologists, cardiologists, infection medicine, neurology, and psychiatry, all reachable through a central command structure.

Telemedicine is not a cure-all, Dr. Pannu said. It doesn’t work in a vacuum. It requires both a provider on the ground and specialists available remotely. “But it can be a massive multiplier.”

Critical medicine

Other hospitals, including small and rural ones, have reported taking on the challenge of covering critical care with nonintensivist physicians because the pandemic demanded it. David Aymond, MD, a hospitalist at 60-bed Byrd Regional Hospital in Leesville, La., population 6,612, has advocated for years for expanded training and credentialing opportunities in intensive care medicine beyond the traditional path of becoming a board-certified intensivist. Some rural hospitalists were already experienced in providing critical care for ICU patients even before the pandemic hit.

“What COVID did was to highlight the problem that there aren’t enough intensivists in this country, particular for smaller hospitals,” Dr. Aymond said. Some hospitalists who stepped into crisis roles in ICUs during COVID surges showed that they could take care of COVID patients very well.

Dr. Aymond, who is a fellowship-trained hospitalist with primary training in family medicine, has used his ICU experience in both fellowship and practice to make a thorough study of critical care medicine, which he put to good use when the seven-bed ICU at Byrd Memorial filled with COVID patients. “Early on, we were managing multiple ventilators throughout the hospital,” he said. “But we were having good outcomes. Our COVID patients were surviving.” That led to Dr. Aymond being interviewed by local news media, which led to other patients across the state asking to be transferred to “the COVID specialist who practices at Byrd.”

Dr. Aymond would like to see opportunities for abbreviated 1-year critical care fellowships for hospitalists who have amassed enough ICU experience in practice or in residency, and to make room for family medicine physicians in such programs. He is also working through SHM with the Society of Critical Care Medicine to generate educational ICU content. SHM now has a critical care lecture series at: www.hospitalmedicine.org/clinical-topics/critical-care/.

Dr. Mandal, who also works as a pediatric hospitalist, said that experience gave her more familiarity with using noninvasive methods for delivering respiratory therapies like high-flow oxygen. “When I saw a COVID patient who had hypoxia but was still able to talk, I didn’t hesitate to deliver oxygen through noninvasive means.” Eventually hospital practice generally for COVID caught up with this approach.

But she ran into personal difficulties because N95 face masks didn’t fit her face. Instead, she had to wear a portable respirator, which made it hard to hear what her patients were saying. “I formulated a lot of workarounds, such as interviewing the patient over the phone before going into the room for the physical exam.”

Throughout the pandemic, she never wavered in her commitment to rural hospital medicine and its opportunities for working in a small and wonderful community, where she could practice at the top of her license, with a degree of autonomy not granted in other settings. For doctors who want that kind of practice, she said, “the rewards will be paid back in spades. That’s been my experience.”

For more information on SHM’s Rural SIG and its supports for rural hospitalists, contact its executive chair, Kenneth Simone, DO, at [email protected].
 

References

1. Personal communication from Peiyin Hung, June 2021.

2. Association of Health Care Journalists. Rural Health Journalism Workshop 2021. June 21, 2021. https://healthjournalism.org/calendar-details.php?id=2369.

3. Congress Establishes New Medicare Provider Category and Reimbursement for Rural Emergency Hospitals. National Law Review. Jan. 5, 2021. https://www.natlawreview.com/article/congress-establishes-new-medicare-provider-category-and-reimbursement-rural.

In 2018, Atashi Mandal, MD, a hospitalist residing in Orange County, Calif., was recruited along with several other doctors to fill hospitalist positions in rural Bishop, Calif. She has since driven 600 miles round trip every month for a week of hospital medicine shifts at Northern Inyo Hospital.

Dr. Mandal said she has really enjoyed her time at the small rural hospital and found it professionally fulfilling to participate so fully in the health of its local community. She was building personal bonds and calling the experience the pinnacle of her career when the COVID-19 pandemic swept across America and the world, even reaching up into Bishop, population 3,760, in the isolated Owens Valley.

The 25-bed hospital has seen at least 100 COVID patients in the past year and some months. Responsibility for taking care of these patients has been both humbling and gratifying, Dr. Mandal said. The facility’s hospitalists made a commitment to keep working through the pandemic. “We were able to come together (around COVID) as a team and our teamwork really made a difference,” she said.

“One of the advantages in a smaller hospital is you can have greater cohesiveness and your communication can be tighter. That played a big role in how we were able to accomplish so much with fewer resources as a rural hospital.” But staffing shortages, recruitment, and retention remain a perennial challenge for rural hospitals. “And COVID only exacerbated the problems,” she said. “I’ve had my challenges trying to make proper treatment plans without access to specialists.”

It was also difficult to witness so many patients severely ill or dying from COVID, Dr. Mandal said, especially since patients were not allowed family visitors – even though that was for a good reason, to minimize the virus’s spread.

HM in rural communities

Hospital medicine continues to extend into rural communities and small rural hospitals. In 2018, 35.7% of all rural counties in America had hospitals staffed with hospitalists, and 63.3% of rural hospitals had hospitalist programs (compared with 79.2% of urban hospitals). These numbers come from Medicare resources files from the Department of Health & Human Services, analyzed by Peiyin Hung, PhD, assistant professor of health services management and policy at the University of South Carolina, Columbia.¹ Hospitalist penetration rates rose steadily from 2011 to 2017, with a slight dip in 2018, Dr. Hung said in an interview.

A total of 138 rural hospitals have closed since 2010, according to the Cecil G. Sheps Center for Health Services Research in Chapel Hill, N.C. Nineteen rural hospitals closed in 2020 alone, although many of those were caused by factors predating the pandemic. Only one has closed so far in 2021. But financial pressures, including low patient volumes and loss of revenue from canceled routine services like elective surgeries during the pandemic, have added to hospitals’ difficulties. Pandemic relief funding may have helped some hospitals stay open, but that support eventually will go away.

Experts emphasize the diversity of rural America and its health care systems. Rural economies are volatile and more diverse than is often appreciated. The hospital may be a cornerstone of the local economy; when one closes, it can devastate the community. Workforce is one of the chief components of a hospital’s ability to meet its strategic vision, and hospitalists are a big part in that. But while hospitalists are valued and appreciated, if the hospital is suffering severe financial problems, that will impact its doctors’ jobs and livelihoods.

“Bandwidth” varies widely for rural hospitalists and their hospitalist groups, said Ken Simone, DO, SFHM, executive chair of SHM’s Rural Special Interest Group and founder and principal of KGS Consultants, a Hospital Medicine and Primary Care Practice Management Consulting company. They may face scarce resources, scarce clinical staffing, lack of support staff to help operations run smoothly, lack of access to specialists locally, and lack of technology. While practicing in a rural setting presents various challenges, it can be rewarding for those clinicians who embrace its autonomy and broad scope of services, Dr. Simone said.

SHM’s Rural SIG focuses on the unique needs of rural hospitalists, providing them with an opportunity to share their concerns, challenges and solutions through roundtable discussions every other month and a special interest forum held in conjunction with the SHM Converge annual conference, Dr. Simone said. (The next SHM Converge will be April 7-10, 2022, in Nashville, Tenn.) The Rural SIG also collaborates with other hospital medicine SIGs and committees and is working on a white paper, “Key Principles and Characteristics of an Effective Rural Hospital Medicine Group.” It is also looking to develop a rural mentorship exchange program.

COVID reaches rural America

Early COVID caseloads tended to be in urban areas, but subsequent surges of infections have spread to many rural areas. Some rural settings became epicenters for the pandemic in November and December 2020. More recent troubling rises in COVID cases, particularly in areas with lower vaccination rates – suggest that the challenges of the pandemic are still not behind us.

“By no means is the crisis done in rural America,” said Alan Morgan, CEO of the National Rural Health Association, in a Virtual Rural Health Journalism workshop on rural health care sponsored by the Association of Health Care Journalists.²

Mr. Morgan’s colleague, Brock Slabach, NRHA’s chief operations officer, said in an interview that, while 453 of the 1,800 hospitals in rural areas fit NRHA’s criteria as being vulnerable to closure, the rest are not, and are fulfilling their missions for their communities. Hospitalists are becoming more common in these hospitals, he said, and rural hospitalists can be an important asset in attracting primary care physicians – who might not appreciate being perpetually on call for their hospitalized patients – to rural communities.

In many cases, traveling doctors like Dr. Mandal or telemedicine backup, particularly for after-hours coverage or ICU beds, are important pieces of the puzzle for smaller hospitals. There are different ways to use the spectrum of telemedicine services to interact with a hospital’s daytime and night routines. In some isolated locations, nurse practitioners or physician assistants provide on-the-ground coverage with virtual backup. Rural hospitals often affiliate with telemedicine networks within health systems – or else contract with independent specialized providers of telemedicine consultation.

Mr. Slabach said another alternative for staffing hospitals with smaller ED and inpatient volumes is to have one doctor on duty who can cover both departments simultaneously. Meanwhile, the new federal Rural Emergency Hospital Program proposes to allow rural hospitals to become essentially freestanding EDs – starting Jan. 1, 2023 – that can manage patients for a maximum of 24 hours.³

Community connections and proactive staffing

Lisa Kaufmann, MD, works as a hospitalist for a two-hospital system in North Carolina, Appalachian Regional Health Care. She practices at Watauga Medical Center, with 100 licensed beds in Boone, and at Cannon Memorial Hospital, a critical access hospital in unincorporated Linville. “We are proud of what we have been able to accomplish during the pandemic,” she said.

A former critical care unit at Watauga had been shut down, but its wiring remained intact. “We turned it into a COVID unit in three days. Then we opened another COVID unit with 18 beds, but that still wasn’t enough. We converted half of our med/surg capacity into a COVID unit. At one point almost half of all of our acute beds were for COVID patients. We made plans for what we would do if it got worse, since we had almost run out of beds,” she said. Demand peaked at the end of January 2021.

“The biggest barrier for us was if someone needed to be transferred, for example, if they needed ECMO [extracorporeal membrane oxygenation], and we couldn’t find another hospital to provide that technology.” In ARHC’s mountainous region – known as the “High Country” – weather can also make it difficult to transport patients. “Sometimes the ambulance can’t make it off the mountain, and half of the time the medical helicopter can’t fly. So we have to be prepared to keep people who we might think ought to be transferred,” she said.

Like many rural communities, the High Country is tightly knit, and its hospitals are really connected to their communities, Dr. Kaufmann said. The health system already had a lot of community connections beyond acute care, and that meant the pandemic wasn’t experienced as severely as it was in some other rural communities. “But without hospitalists in our hospitals, it would have been much more difficult.”

Proactive supply fulfillment meant that her hospitals never ran out of personal protective equipment. “Staffing was a challenge, but we were proactive in getting traveling doctors to come here. We also utilized extra doctors from the local community,” she said. Another key was well-established disaster planning, with regular drills, and a robust incident command structure, which just needed to be activated in the crisis. “Small hospitals need to be prepared for disaster,” Dr. Kaufmann said.

For Dale Wiersma, MD, a hospitalist with Spectrum Health, a 14-hospital system in western Michigan, telemedicine services are coordinated across 8 rural regional hospitals. “We don’t tend to use it for direct hospitalist work during daytime hours, unless a facility is swamped, in which case we can cross-cover. We do more telemedicine at night. But during daytime hours we have access to stroke neurology, cardiology, psychiatry, critical care and infectious disease specialists who are able to offer virtual consults,” Dr. Wiersma said. A virtual critical care team of doctor and nurse is often the only intensivist service covering Spectrum’s rural hospitals.

“In our system, the pandemic accelerated the adoption of telemedicine,” Dr. Wiersma said. “We had been working on the tele-ICU program, trying to get it rolled out. When the pandemic hit, we launched it in just 6 weeks.”

There have been several COVID surges in Michigan, he said. “We were stretched pretty close to our limit several times, but never to the breaking point. For our physicians, it was the protracted nature of the pandemic that was fatiguing for everyone involved. Our system worked hard to staff up as well as it could, to make sure our people didn’t go over the edge.” It was also hard for hospitals that typically might see one or two deaths in a month to suddenly have five in a week.

Another Spectrum hospitalist, Christopher Skinner, MD, works at two rural Michigan hospitals 15 minutes apart in Big Rapids and Reed City. “I prefer working in rural areas. I’ve never had an ambition to be a top dog. I like the style of practice where you don’t have all of the medical subspecialties on site. It frees you up to use all your skills,” Dr. Skinner said.

But that approach was put to the test by the pandemic, since it was harder to transfer those patients who normally would not have stayed at these rural hospitals. “We had to make do,” he said, although virtual backup and second opinions from Spectrum’s virtual critical care team helped.

“It was a great collaboration, which helped us to handle critical care cases that we hadn’t had to manage pre-COVID. We’ve gotten used to it, with the backup, so I expect we’ll still be taking care of these kind of sick ventilator patients even after the pandemic ends,” Dr. Skinner said. “We’ve gotten pretty good at it.”

Sukhbir Pannu, MD, a hospitalist in Denver and CEO and founder of Rural Physicians Group, said the pandemic was highly impactful, operationally and logistically, for his firm, which contracts with 54 hospitals to provide their hospitalist staffing. “There was no preparation. Everything had to be done on the fly. Initially, it was felt that rural areas weren’t at as great a risk for COVID, but that proved not to be true. Many experienced a sudden increase in very sick patients. We set up a task force to manage daily census in all of our contracted facilities.”

How did Rural Physicians Group manage through the crisis? “The short answer is telemedicine,” he said. “We had physicians on the ground in these hospitals. But we needed intensivists at the other end of the line to support them.” A lot of conversations about telemedicine were already going on in the company, but the pandemic provided the impetus to launch its network, which has grown to include rheumatologists, pulmonologists, cardiologists, infection medicine, neurology, and psychiatry, all reachable through a central command structure.

Telemedicine is not a cure-all, Dr. Pannu said. It doesn’t work in a vacuum. It requires both a provider on the ground and specialists available remotely. “But it can be a massive multiplier.”

Critical medicine

Other hospitals, including small and rural ones, have reported taking on the challenge of covering critical care with nonintensivist physicians because the pandemic demanded it. David Aymond, MD, a hospitalist at 60-bed Byrd Regional Hospital in Leesville, La., population 6,612, has advocated for years for expanded training and credentialing opportunities in intensive care medicine beyond the traditional path of becoming a board-certified intensivist. Some rural hospitalists were already experienced in providing critical care for ICU patients even before the pandemic hit.

“What COVID did was to highlight the problem that there aren’t enough intensivists in this country, particular for smaller hospitals,” Dr. Aymond said. Some hospitalists who stepped into crisis roles in ICUs during COVID surges showed that they could take care of COVID patients very well.

Dr. Aymond, who is a fellowship-trained hospitalist with primary training in family medicine, has used his ICU experience in both fellowship and practice to make a thorough study of critical care medicine, which he put to good use when the seven-bed ICU at Byrd Memorial filled with COVID patients. “Early on, we were managing multiple ventilators throughout the hospital,” he said. “But we were having good outcomes. Our COVID patients were surviving.” That led to Dr. Aymond being interviewed by local news media, which led to other patients across the state asking to be transferred to “the COVID specialist who practices at Byrd.”

Dr. Aymond would like to see opportunities for abbreviated 1-year critical care fellowships for hospitalists who have amassed enough ICU experience in practice or in residency, and to make room for family medicine physicians in such programs. He is also working through SHM with the Society of Critical Care Medicine to generate educational ICU content. SHM now has a critical care lecture series at: www.hospitalmedicine.org/clinical-topics/critical-care/.

Dr. Mandal, who also works as a pediatric hospitalist, said that experience gave her more familiarity with using noninvasive methods for delivering respiratory therapies like high-flow oxygen. “When I saw a COVID patient who had hypoxia but was still able to talk, I didn’t hesitate to deliver oxygen through noninvasive means.” Eventually hospital practice generally for COVID caught up with this approach.

But she ran into personal difficulties because N95 face masks didn’t fit her face. Instead, she had to wear a portable respirator, which made it hard to hear what her patients were saying. “I formulated a lot of workarounds, such as interviewing the patient over the phone before going into the room for the physical exam.”

Throughout the pandemic, she never wavered in her commitment to rural hospital medicine and its opportunities for working in a small and wonderful community, where she could practice at the top of her license, with a degree of autonomy not granted in other settings. For doctors who want that kind of practice, she said, “the rewards will be paid back in spades. That’s been my experience.”

For more information on SHM’s Rural SIG and its supports for rural hospitalists, contact its executive chair, Kenneth Simone, DO, at [email protected].
 

References

1. Personal communication from Peiyin Hung, June 2021.

2. Association of Health Care Journalists. Rural Health Journalism Workshop 2021. June 21, 2021. https://healthjournalism.org/calendar-details.php?id=2369.

3. Congress Establishes New Medicare Provider Category and Reimbursement for Rural Emergency Hospitals. National Law Review. Jan. 5, 2021. https://www.natlawreview.com/article/congress-establishes-new-medicare-provider-category-and-reimbursement-rural.

In 2018, Atashi Mandal, MD, a hospitalist residing in Orange County, Calif., was recruited along with several other doctors to fill hospitalist positions in rural Bishop, Calif. She has since driven 600 miles round trip every month for a week of hospital medicine shifts at Northern Inyo Hospital.

Dr. Mandal said she has really enjoyed her time at the small rural hospital and found it professionally fulfilling to participate so fully in the health of its local community. She was building personal bonds and calling the experience the pinnacle of her career when the COVID-19 pandemic swept across America and the world, even reaching up into Bishop, population 3,760, in the isolated Owens Valley.

The 25-bed hospital has seen at least 100 COVID patients in the past year and some months. Responsibility for taking care of these patients has been both humbling and gratifying, Dr. Mandal said. The facility’s hospitalists made a commitment to keep working through the pandemic. “We were able to come together (around COVID) as a team and our teamwork really made a difference,” she said.

“One of the advantages in a smaller hospital is you can have greater cohesiveness and your communication can be tighter. That played a big role in how we were able to accomplish so much with fewer resources as a rural hospital.” But staffing shortages, recruitment, and retention remain a perennial challenge for rural hospitals. “And COVID only exacerbated the problems,” she said. “I’ve had my challenges trying to make proper treatment plans without access to specialists.”

It was also difficult to witness so many patients severely ill or dying from COVID, Dr. Mandal said, especially since patients were not allowed family visitors – even though that was for a good reason, to minimize the virus’s spread.

HM in rural communities

Hospital medicine continues to extend into rural communities and small rural hospitals. In 2018, 35.7% of all rural counties in America had hospitals staffed with hospitalists, and 63.3% of rural hospitals had hospitalist programs (compared with 79.2% of urban hospitals). These numbers come from Medicare resources files from the Department of Health & Human Services, analyzed by Peiyin Hung, PhD, assistant professor of health services management and policy at the University of South Carolina, Columbia.¹ Hospitalist penetration rates rose steadily from 2011 to 2017, with a slight dip in 2018, Dr. Hung said in an interview.

A total of 138 rural hospitals have closed since 2010, according to the Cecil G. Sheps Center for Health Services Research in Chapel Hill, N.C. Nineteen rural hospitals closed in 2020 alone, although many of those were caused by factors predating the pandemic. Only one has closed so far in 2021. But financial pressures, including low patient volumes and loss of revenue from canceled routine services like elective surgeries during the pandemic, have added to hospitals’ difficulties. Pandemic relief funding may have helped some hospitals stay open, but that support eventually will go away.

Experts emphasize the diversity of rural America and its health care systems. Rural economies are volatile and more diverse than is often appreciated. The hospital may be a cornerstone of the local economy; when one closes, it can devastate the community. Workforce is one of the chief components of a hospital’s ability to meet its strategic vision, and hospitalists are a big part in that. But while hospitalists are valued and appreciated, if the hospital is suffering severe financial problems, that will impact its doctors’ jobs and livelihoods.

“Bandwidth” varies widely for rural hospitalists and their hospitalist groups, said Ken Simone, DO, SFHM, executive chair of SHM’s Rural Special Interest Group and founder and principal of KGS Consultants, a Hospital Medicine and Primary Care Practice Management Consulting company. They may face scarce resources, scarce clinical staffing, lack of support staff to help operations run smoothly, lack of access to specialists locally, and lack of technology. While practicing in a rural setting presents various challenges, it can be rewarding for those clinicians who embrace its autonomy and broad scope of services, Dr. Simone said.

SHM’s Rural SIG focuses on the unique needs of rural hospitalists, providing them with an opportunity to share their concerns, challenges and solutions through roundtable discussions every other month and a special interest forum held in conjunction with the SHM Converge annual conference, Dr. Simone said. (The next SHM Converge will be April 7-10, 2022, in Nashville, Tenn.) The Rural SIG also collaborates with other hospital medicine SIGs and committees and is working on a white paper, “Key Principles and Characteristics of an Effective Rural Hospital Medicine Group.” It is also looking to develop a rural mentorship exchange program.

COVID reaches rural America

Early COVID caseloads tended to be in urban areas, but subsequent surges of infections have spread to many rural areas. Some rural settings became epicenters for the pandemic in November and December 2020. More recent troubling rises in COVID cases, particularly in areas with lower vaccination rates – suggest that the challenges of the pandemic are still not behind us.

“By no means is the crisis done in rural America,” said Alan Morgan, CEO of the National Rural Health Association, in a Virtual Rural Health Journalism workshop on rural health care sponsored by the Association of Health Care Journalists.²

Mr. Morgan’s colleague, Brock Slabach, NRHA’s chief operations officer, said in an interview that, while 453 of the 1,800 hospitals in rural areas fit NRHA’s criteria as being vulnerable to closure, the rest are not, and are fulfilling their missions for their communities. Hospitalists are becoming more common in these hospitals, he said, and rural hospitalists can be an important asset in attracting primary care physicians – who might not appreciate being perpetually on call for their hospitalized patients – to rural communities.

In many cases, traveling doctors like Dr. Mandal or telemedicine backup, particularly for after-hours coverage or ICU beds, are important pieces of the puzzle for smaller hospitals. There are different ways to use the spectrum of telemedicine services to interact with a hospital’s daytime and night routines. In some isolated locations, nurse practitioners or physician assistants provide on-the-ground coverage with virtual backup. Rural hospitals often affiliate with telemedicine networks within health systems – or else contract with independent specialized providers of telemedicine consultation.

Mr. Slabach said another alternative for staffing hospitals with smaller ED and inpatient volumes is to have one doctor on duty who can cover both departments simultaneously. Meanwhile, the new federal Rural Emergency Hospital Program proposes to allow rural hospitals to become essentially freestanding EDs – starting Jan. 1, 2023 – that can manage patients for a maximum of 24 hours.³

Community connections and proactive staffing

Lisa Kaufmann, MD, works as a hospitalist for a two-hospital system in North Carolina, Appalachian Regional Health Care. She practices at Watauga Medical Center, with 100 licensed beds in Boone, and at Cannon Memorial Hospital, a critical access hospital in unincorporated Linville. “We are proud of what we have been able to accomplish during the pandemic,” she said.

A former critical care unit at Watauga had been shut down, but its wiring remained intact. “We turned it into a COVID unit in three days. Then we opened another COVID unit with 18 beds, but that still wasn’t enough. We converted half of our med/surg capacity into a COVID unit. At one point almost half of all of our acute beds were for COVID patients. We made plans for what we would do if it got worse, since we had almost run out of beds,” she said. Demand peaked at the end of January 2021.

“The biggest barrier for us was if someone needed to be transferred, for example, if they needed ECMO [extracorporeal membrane oxygenation], and we couldn’t find another hospital to provide that technology.” In ARHC’s mountainous region – known as the “High Country” – weather can also make it difficult to transport patients. “Sometimes the ambulance can’t make it off the mountain, and half of the time the medical helicopter can’t fly. So we have to be prepared to keep people who we might think ought to be transferred,” she said.

Like many rural communities, the High Country is tightly knit, and its hospitals are really connected to their communities, Dr. Kaufmann said. The health system already had a lot of community connections beyond acute care, and that meant the pandemic wasn’t experienced as severely as it was in some other rural communities. “But without hospitalists in our hospitals, it would have been much more difficult.”

Proactive supply fulfillment meant that her hospitals never ran out of personal protective equipment. “Staffing was a challenge, but we were proactive in getting traveling doctors to come here. We also utilized extra doctors from the local community,” she said. Another key was well-established disaster planning, with regular drills, and a robust incident command structure, which just needed to be activated in the crisis. “Small hospitals need to be prepared for disaster,” Dr. Kaufmann said.

For Dale Wiersma, MD, a hospitalist with Spectrum Health, a 14-hospital system in western Michigan, telemedicine services are coordinated across 8 rural regional hospitals. “We don’t tend to use it for direct hospitalist work during daytime hours, unless a facility is swamped, in which case we can cross-cover. We do more telemedicine at night. But during daytime hours we have access to stroke neurology, cardiology, psychiatry, critical care and infectious disease specialists who are able to offer virtual consults,” Dr. Wiersma said. A virtual critical care team of doctor and nurse is often the only intensivist service covering Spectrum’s rural hospitals.

“In our system, the pandemic accelerated the adoption of telemedicine,” Dr. Wiersma said. “We had been working on the tele-ICU program, trying to get it rolled out. When the pandemic hit, we launched it in just 6 weeks.”

There have been several COVID surges in Michigan, he said. “We were stretched pretty close to our limit several times, but never to the breaking point. For our physicians, it was the protracted nature of the pandemic that was fatiguing for everyone involved. Our system worked hard to staff up as well as it could, to make sure our people didn’t go over the edge.” It was also hard for hospitals that typically might see one or two deaths in a month to suddenly have five in a week.

Another Spectrum hospitalist, Christopher Skinner, MD, works at two rural Michigan hospitals 15 minutes apart in Big Rapids and Reed City. “I prefer working in rural areas. I’ve never had an ambition to be a top dog. I like the style of practice where you don’t have all of the medical subspecialties on site. It frees you up to use all your skills,” Dr. Skinner said.

But that approach was put to the test by the pandemic, since it was harder to transfer those patients who normally would not have stayed at these rural hospitals. “We had to make do,” he said, although virtual backup and second opinions from Spectrum’s virtual critical care team helped.

“It was a great collaboration, which helped us to handle critical care cases that we hadn’t had to manage pre-COVID. We’ve gotten used to it, with the backup, so I expect we’ll still be taking care of these kind of sick ventilator patients even after the pandemic ends,” Dr. Skinner said. “We’ve gotten pretty good at it.”

Sukhbir Pannu, MD, a hospitalist in Denver and CEO and founder of Rural Physicians Group, said the pandemic was highly impactful, operationally and logistically, for his firm, which contracts with 54 hospitals to provide their hospitalist staffing. “There was no preparation. Everything had to be done on the fly. Initially, it was felt that rural areas weren’t at as great a risk for COVID, but that proved not to be true. Many experienced a sudden increase in very sick patients. We set up a task force to manage daily census in all of our contracted facilities.”

How did Rural Physicians Group manage through the crisis? “The short answer is telemedicine,” he said. “We had physicians on the ground in these hospitals. But we needed intensivists at the other end of the line to support them.” A lot of conversations about telemedicine were already going on in the company, but the pandemic provided the impetus to launch its network, which has grown to include rheumatologists, pulmonologists, cardiologists, infection medicine, neurology, and psychiatry, all reachable through a central command structure.

Telemedicine is not a cure-all, Dr. Pannu said. It doesn’t work in a vacuum. It requires both a provider on the ground and specialists available remotely. “But it can be a massive multiplier.”

Critical medicine

Other hospitals, including small and rural ones, have reported taking on the challenge of covering critical care with nonintensivist physicians because the pandemic demanded it. David Aymond, MD, a hospitalist at 60-bed Byrd Regional Hospital in Leesville, La., population 6,612, has advocated for years for expanded training and credentialing opportunities in intensive care medicine beyond the traditional path of becoming a board-certified intensivist. Some rural hospitalists were already experienced in providing critical care for ICU patients even before the pandemic hit.

“What COVID did was to highlight the problem that there aren’t enough intensivists in this country, particular for smaller hospitals,” Dr. Aymond said. Some hospitalists who stepped into crisis roles in ICUs during COVID surges showed that they could take care of COVID patients very well.

Dr. Aymond, who is a fellowship-trained hospitalist with primary training in family medicine, has used his ICU experience in both fellowship and practice to make a thorough study of critical care medicine, which he put to good use when the seven-bed ICU at Byrd Memorial filled with COVID patients. “Early on, we were managing multiple ventilators throughout the hospital,” he said. “But we were having good outcomes. Our COVID patients were surviving.” That led to Dr. Aymond being interviewed by local news media, which led to other patients across the state asking to be transferred to “the COVID specialist who practices at Byrd.”

Dr. Aymond would like to see opportunities for abbreviated 1-year critical care fellowships for hospitalists who have amassed enough ICU experience in practice or in residency, and to make room for family medicine physicians in such programs. He is also working through SHM with the Society of Critical Care Medicine to generate educational ICU content. SHM now has a critical care lecture series at: www.hospitalmedicine.org/clinical-topics/critical-care/.

Dr. Mandal, who also works as a pediatric hospitalist, said that experience gave her more familiarity with using noninvasive methods for delivering respiratory therapies like high-flow oxygen. “When I saw a COVID patient who had hypoxia but was still able to talk, I didn’t hesitate to deliver oxygen through noninvasive means.” Eventually hospital practice generally for COVID caught up with this approach.

But she ran into personal difficulties because N95 face masks didn’t fit her face. Instead, she had to wear a portable respirator, which made it hard to hear what her patients were saying. “I formulated a lot of workarounds, such as interviewing the patient over the phone before going into the room for the physical exam.”

Throughout the pandemic, she never wavered in her commitment to rural hospital medicine and its opportunities for working in a small and wonderful community, where she could practice at the top of her license, with a degree of autonomy not granted in other settings. For doctors who want that kind of practice, she said, “the rewards will be paid back in spades. That’s been my experience.”

For more information on SHM’s Rural SIG and its supports for rural hospitalists, contact its executive chair, Kenneth Simone, DO, at [email protected].
 

References

1. Personal communication from Peiyin Hung, June 2021.

2. Association of Health Care Journalists. Rural Health Journalism Workshop 2021. June 21, 2021. https://healthjournalism.org/calendar-details.php?id=2369.

3. Congress Establishes New Medicare Provider Category and Reimbursement for Rural Emergency Hospitals. National Law Review. Jan. 5, 2021. https://www.natlawreview.com/article/congress-establishes-new-medicare-provider-category-and-reimbursement-rural.