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The Association of American Medical Colleges

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.

A deep learning system for differential diagnosis of skin diseases

Skin conditions affect an estimated 1.9 billion people worldwide. A shortage of dermatologists causes long wait times and leads patients to seek dermatologic care from general practitioners. However, the diagnostic accuracy of general practitioners has been reported to be only 0.24-0.70 (compared to 0.77-0.96 for dermatologists), resulting in referral errors, delays in care, and errors in diagnosis and treatment. In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories). The DLS distinguishes between 26 skin conditions that represent roughly 80% of the volume of skin conditions seen in primary care. The DLS was developed and validated using de-identified cases from a teledermatology practice serving 17 clinical sites via a temporal split: the first 14,021 cases for development and the last 3,756 cases for validation. On the validation set, where a panel of three board-certified dermatologists defined the reference standard for every case, the DLS achieved 0.71 and 0.93 top-1 and top-3 accuracies respectively. For a random subset of the validation set (n=963 cases), 18 clinicians reviewed the cases for comparison. On this subset, the DLS achieved a 0.67 top-1 accuracy, non-inferior to board-certified dermatologists (0.63, p<0.001), and higher than primary care physicians (PCPs, 0.45) and nurse practitioners (NPs, 0.41). The top-3 accuracy showed a similar trend: 0.90 DLS, 0.75 dermatologists, 0.60 PCPs, and 0.55 NPs. These results highlight the potential of the DLS to augment general practitioners to accurately diagnose skin conditions by suggesting differential diagnoses that may not have been considered. Future work will be needed to prospectively assess the clinical impact of using this tool in actual clinical workflows.

PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones.

Deep learning based classification of facial dermatological disorders

Importance As the US population continues to increase and age, there is an unmet need for dermatologic care; therefore, it is important to identify and understand the characteristics and patterns of the dermatologist workforce. Objective To analyze the longitudinal dermatologist density and urban-rural disparities using a standardized classification scheme. Design, Setting, and Participants This study analyzed county-level data for 1995 to 2013 from the Area Health Resources File to evaluate the longitudinal trends and demographic and environmental factors associated with the geographic distribution of dermatologists. Main Outcomes and Measures Active US dermatologist and physician density. Results In this study of nationwide data on dermatologists, dermatologist density increased by 21% from 3.02 per 100 000 people to 3.65 per 100 000 people from 1995 to 2013; the gap between the density of dermatologists in urban and other areas increased from 2.63 to 3.06 in nonmetropolitan areas and from 3.41 to 4.03 in rural areas. The ratio of dermatologists older than 55 years to younger than 55 years increased 75% in nonmetropolitan and rural areas (from 0.32 to 0.56) and 170% in metropolitan areas (from 0.34 to 0.93). Dermatologists tended to be located in well-resourced, urban communities. Conclusions and Relevance Our findings suggest that substantial disparities in the geographic distribution of dermatologists exist and have been increasing with time. Correcting the workforce disparity is important for patient care.

/pdf/comparison-of-dermatologist-density-between-urban-and-rural-22kkkkrzyn.pdf

Comparison of Dermatologist Density Between Urban and Rural Counties in the United States

National Trends in the United States Eye Care Workforce from 1995 to 2017

Background Prescription opioid use often precedes opioid misuse, abuse, and addiction. Objective To characterize the national opioid prescription practices of Mohs surgeons, with general dermatologists serving as a comparison group. Methods A retrospective cross-sectional study using Medicare prescription data to analyze opioid prescription characteristics. Results In 2014, 2,190 Mohs surgeons prescribed a total of 86,526 opioids, whereas 10,347 non-Mohs dermatologists prescribed 45,033 opioids. Among Mohs surgeons, 51.8% prescribed more than 10 opioids, whereas 93.3% of non-Mohs dermatologists prescribed 0 to 10. The estimated opioid prescription rates for Mohs surgeons and non-Mohs dermatologists were 5.9% and 0.7%, respectively. Among those prescribing at least 10 opioid claims, the mean number of opioids supplied and mean opioid prescription rate was 72.4 and 13.5, respectively, for Mohs surgeons versus 32.5 and 5.1 , respectively, for non-Mohs dermatologists. The mean days' supply of opioids was 3.9 for Mohs surgeons versus 7.9 for non-Mohs dermatologists. There was a minimal-to-moderate association between the number of procedures performed and the number of opioids prescribed. Men and Mohs surgeons located in southern states tend to prescribe a greater number of opioids. Conclusion Mohs surgeons prescribed short courses of opioids and less than both the national average and providers that care for patients in pain.

Characteristics of Opioid Prescriptions by Mohs Surgeons in the Medicare Population.

Gender Differences in Surgical Volume among Cataract Surgeons.

This cross-sectional study explores participation in the Merit-Based Incentive Payment System by dermatologists, including those who would benefit from resources to enhance engagement and performance.

Paula W. Feng

Papers

Comparison of Dermatologist Density Between Urban and Rural Counties in the United States

National Trends in the United States Eye Care Workforce from 1995 to 2017

Characteristics of Opioid Prescriptions by Mohs Surgeons in the Medicare Population.

Gender Differences in Surgical Volume among Cataract Surgeons.

Participation and Performance of Dermatologists in the 2017 Merit-Based Incentive Payment System