Using Implementation Science to Understand Teledermatology Implementation Early in the COVID-19 Pandemic: Cross-sectional Study (Preprint)

Patient and Clinician Satisfaction in Teledermatology: Key Factors for Successful Implementation

Purpose of review

Teledermatology has emerged as a promising solution for remote dermatologic care, especially during COVID-19 pandemic. It improves access to care through information and communication technologies. This paper explores patient and clinician satisfaction in teledermatology.

Recent findings

Patient satisfaction encompasses various aspects, including future willingness, the quality of care, technical quality, and access to care. Clinician satisfaction is influenced by quality of care, implementation, technical aspects, clinician-patient rapport, and financial considerations. It is important to evaluate patient and clinician satisfaction in different teledermatology models, including store-and-forward, live interactive, and hybrid interactive approaches.

Summary

By evaluating satisfaction within different teledermatology models and addressing the factors that impact satisfaction, teledermatology can ensure high-quality care and promote positive outcomes. With proper implementation and ongoing evaluation, teledermatology has the potential to become a widely accepted and valuable component of dermatologic care, offering enhanced access to specialized services.

Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality

Importance

Telemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical determination.

Objective

To determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients.

Design, Setting, and Participants

This quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone.

Interventions

During the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient's assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos.

Main Outcomes and Measures

The main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support.

Results

Of the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%.

Conclusions and Relevance

In this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.

Implementation of Telemedicine Infectious Diseases Consultation in a Rural Hospital Using the Active Implementation Framework

In this pilot study, guided by the Active Implementation Framework, telemedicine infectious diseases consultation was provided to hospitalized inpatients at a rural Missouri hospital. Measured outcomes included the implementation outcomes of feasibility, acceptability, appropriateness, and fidelity, as well as clinical outcomes of readmissions and death.

Teledermatology in Rural, Underserved, and Isolated Environments: A Review

Purpose of Review

Recent Findings

Summary

Store-and-Forward Images in Teledermatology: Narrative Literature Review

Background

Store-and-forward (SAF) teledermatology uses electronically stored information, including patient photographs and demographic information, for clinical decision-making asynchronous to the patient encounter. The integration of SAF teledermatology into clinical practice has been increasing in recent years, especially during the COVID-19 pandemic. Despite this growth, data regarding the outcomes of SAF teledermatology are limited. A key distinction among current literature involves comparing the quality and utility of images obtained by patients and trained clinicians, as these metrics may vary by the clinical expertise of the photographer.

Objective

This narrative literature review aimed to characterize the outcomes of SAF teledermatology through the lens of patient- versus clinician-initiated photography and highlight important future directions for and challenges of the field.

Methods

A literature search of peer-reviewed research was performed between February and April 2021. Key search terms included patient-initiated, patient-submitted, clinician-initiated, clinician-submitted, store-and-forward, asynchronous, remote, image, photograph, and teledermatology. Only studies published after 2001 in English were included. In total, 47 studies were identified from the PubMed electronic database and Google Scholar after omitting duplicate articles.

Results

Image quality and diagnostic concordance are generally lower and more variable with patient-submitted images, which may impact their decision-making utility. SAF teledermatology can improve the efficiency of and access to care when photographs are taken by either clinicians or patients. The clinical outcomes of clinician-submitted images are comparable to those of in-person visits in the few studies that have investigated these outcomes. Coinciding with the onset of the COVID-19 pandemic, asynchronous teledermatology helped minimize unnecessary in-person visits in the outpatient setting, as many uncomplicated conditions could be adequately managed remotely via images captured by patients and referring clinicians. For the inpatient setting, SAF teledermatology minimized unnecessary contact during dermatology consultations, although current studies are limited by the heterogeneity of their outcomes.

Conclusions

In general, photographs taken by trained clinicians are higher quality and have better and more relevant diagnostic and clinical outcomes. SAF teledermatology helped clinicians avoid unnecessary physical contact with patients in the outpatient and inpatient settings during the COVID-19 pandemic. Asynchronous teledermatology will likely play a greater role in the future as SAF images become integrated into synchronous teledermatology workflows. However, the obstacles summarized in this review should be addressed before its widespread implementation into clinical practice.