Teledermatology to Facilitate Patient Care Transitions From Inpatient to Outpatient Dermatology: Mixed Methods Evaluation.

BACKGROUND: Both clinicians and patients have increasingly turned to telemedicine to improve care access, even in physical examination-dependent specialties such as dermatology. However, little is known about whether teledermatology supports effective and timely transitions from inpatient to outpatient care, which is a common care coordination gap. OBJECTIVE: Using mixed methods, this study sought to retrospectively evaluate how teledermatology affected clinic capacity, scheduling efficiency, and timeliness of follow-up care for patients transitioning from inpatient to outpatient dermatology care. METHODS: Patient-level encounter scheduling data were used to compare the number and proportion of patients who were scheduled and received in-clinic or video dermatology follow-ups within 14 and 90 days after discharge across 3 phases: June to September 2019 (before teledermatology), June to September 2020 (early teledermatology), and February to May 2021 (sustained teledermatology). The time from discharge to scheduling and completion of patient follow-up visits for each care modality was also compared. Dermatology clinicians and schedulers were also interviewed between April and May 2021 to assess their perceptions of teledermatology for postdischarge patients. RESULTS: More patients completed follow-up within 90 days after discharge during early (n=101) and sustained (n=100) teledermatology use than at baseline (n=74). Thus, the clinic's capacity to provide follow-up to patients transitioning from inpatient increased from baseline by 36% in the early (101 from 74) and sustained (100 from 74) teledermatology periods. During early teledermatology use, 61.4% (62/101) of the follow-ups were conducted via video. This decreased significantly to 47% (47/100) in the following year, when COVID-19-related restrictions started to lift (P=.04), indicating more targeted but still substantial use. The proportion of patients who were followed up within the recommended 14 days after discharge did not differ significantly between video and in-clinic visits during the early (33/62, 53% vs 15/39, 38%; P=.15) or sustained (26/53, 60% vs 28/47, 49%; P=.29) teledermatology periods. Interviewees agreed that teledermatology would continue to be offered. Most considered postdischarge follow-up patients to be ideal candidates for teledermatology as they had undergone a recent in-person assessment and might have difficulty attending in-clinic visits because of competing health priorities. Some reported patients needing technological support. Ultimately, most agreed that the choice of follow-up care modality should be the patient's own. CONCLUSIONS: Teledermatology could be an important tool for maintaining accessible, flexible, and convenient care for recently discharged patients needing follow-up care. Teledermatology increased clinic capacity, even during the pandemic, although the timeliness of care transitions did not improve. Ultimately, the care modality should be determined through communication with patients to incorporate their and their caregivers' preferences.

Partnering with a senior living community to optimise teledermatology via full body skin screening during the COVID-19 pandemic: A pilot programme.

Background: Elderly patients in senior communities faced high barriers to care during the COVID-19 pandemic, including increased vulnerability to COVID-19, long quarantines for clinic visits, and difficulties with telemedicine adoption. Objective: To pilot a new model of dermatologic care to overcome barriers for senior living communities during the COVID-19 pandemic and assess patient satisfaction. Methods: From 16 November 2020 to 9 July 2021, this quality improvement programme combined in-residence full body imaging with real-time outlier lesion identification and virtual teledermatology. Residents from the Sequoias Portola Valley Senior Living Retirement Community (Portola Valley, California) voluntarily enroled in the Stanford Skin Scan Programme. Non-physician clinical staff with a recent negative COVID-19 test travelled on-site to obtain in-residence full body photographs using a mobile app-based system on an iPad called SkinIO that leverages deep learning to analyse patient images and suggest suspicious, outlier lesions for dermoscopic photos. A single dermatologist reviewed photographs with the patient and provided recommendations via a video visit. Objective measures included follow-up course and number of skin cancers detected. Subjective findings were obtained through patient experience surveys. Results: Twenty-seven individuals participated, three skin cancers were identified, with 11 individuals scheduled for a follow up in-person visit and four individuals starting home treatment. Overall, 88% of patients were satisfied with the Skin Scan programme, with 77% likely to recommend the programme to others. 92% of patients agreed that the Skin Scan photographs were representative of their skin. In the context of the COVID-19 pandemic, 100% of patients felt the process was safer or comparable to an in-person visit. Despite overall appreciation for the programme, 31% of patients reported that they would prefer to see dermatologist in-person after the pandemic. Conclusions: This programme offers a framework for how a hybrid skin scan programme may provide high utility for individuals with barriers to accessing in-person clinics.

Management of a Child vs an Adult Presenting With Acral Lesions During the COVID-19 Pandemic: A Practical Review.

During the coronavirus disease 2019 (COVID-19) pandemic, there has been a rise in the diagnosis of acral lesions, including chilblains-like lesions, ischemia, and retiform purpura. Understanding the differences in presentation and severity of illness between children and adult patients is important for physicians to understand risk stratification and management of these lesions. We reviewed the literature on the acral lesions seen in children and adults with COVID-19 infection to offer guidelines for diagnosis and treatment.

TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos.

Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.