Pediatric Teledermatology: A Survey of Usage,Perspectives, and Practice

Pediatric dermatology is one of the smallest subspecialties, and expanding the availability of care is of great interest. Teledermatology has been proposed as a way to expand access and improve care delivery, but no current assessment of pediatric teledermatology exists. The objective of the current study was to assess usage and perspectives on pediatric teledermatology. Surveys were distributed electronically to all 226 board‐certified U.S. pediatric dermatologists; 44% (100/226) responded. Nearly all respondents (89%) have experience with teledermatology. Formal teledermatology reimbursement success rates have increased to 35%. Respondents were positive about teledermatology's present and future prospects, and 41% want to use teledermatology more often, although they viewed teledermatology as somewhat inferior to in‐person care regarding accuracy of diagnosis and appropriation of management plans. Significant differences were found between formal teledermatology users and nonusers in salary structure, practice environment, sex, and region. Substantial increases in pediatric teledermatology have occurred in the last 5 to 10 years, and there remains cause for optimism for teledermatology's future. Concerns about diagnostic confidence and care quality indicate that teledermatology may be best for care of patients with characteristic clinical presentations or management of patients with established diagnoses.     

A Case of Loose Anagen Syndrome in an African American Girl

We present the case of 7‐year‐old African American girl with loose anagen syndrome. Although this is a common cause of hair loss in Caucasian children, and there have been reports of cases occurring in dark‐skinned children of North African and Middle Eastern descent, to our knowledge there have been no cases reported in black children of sub‐Saharan African ancestry. We present this case to broaden the differential diagnosis of hair loss in African Americans.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.