Telehealth Utilization and Associations in the United States During the Third Year of the COVID-19 Pandemic: Population-Based Survey Study in 2022.

BACKGROUND: The COVID-19 pandemic rapidly changed the landscape of clinical practice in the United States; telehealth became an essential mode of health care delivery, yet many components of telehealth use remain unknown years after the disease's emergence. OBJECTIVE: We aim to comprehensively assess telehealth use and its associated factors in the United States. METHODS: This cross-sectional study used a nationally representative survey (Health Information National Trends Survey) administered to US adults (≥18 years) from March 2022 through November 2022. To assess telehealth adoption, perceptions of telehealth, satisfaction with telehealth, and the telehealth care purpose, we conducted weighted descriptive analyses. To identify the subpopulations with low adoption of telehealth, we developed a weighted multivariable logistic regression model. RESULTS: Among a total of 6252 survey participants, 39.3% (2517/6252) reported telehealth use in the past 12 months (video: 1110/6252, 17.8%; audio: 876/6252, 11.6%). The most prominent reason for not using telehealth was due to telehealth providers failing to offer this option (2200/3529, 63%). The most common reason for respondents not using offered telehealth services was a preference for in-person care (527/578, 84.4%). Primary motivations to use telehealth were providers' recommendations (1716/2517, 72.7%) and convenience (1516/2517, 65.6%), mainly for acute minor illness (600/2397, 29.7%) and chronic condition management (583/2397, 21.4%), yet care purposes differed by age, race/ethnicity, and income. The satisfaction rate was predominately high, with no technical problems (1829/2517, 80.5%), comparable care quality to that of in-person care (1779/2517, 75%), and no privacy concerns (1958/2517, 83.7%). Younger individuals (odd ratios [ORs] 1.48-2.23; 18-64 years vs ≥75 years), women (OR 1.33, 95% CI 1.09-1.61), Hispanic individuals (OR 1.37, 95% CI 1.05-1.80; vs non-Hispanic White), those with more education (OR 1.72, 95% CI 1.03-2.87; at least a college graduate vs less than high school), unemployed individuals (OR 1.25, 95% CI 1.02-1.54), insured individuals (OR 1.83, 95% CI 1.25-2.69), or those with poor general health status (OR 1.66, 95% CI 1.30-2.13) had higher odds of using telehealth. CONCLUSIONS: To our best knowledge, this is among the first studies to examine patient factors around telehealth use, including motivations to use, perceptions of, satisfaction with, and care purpose of telehealth, as well as sociodemographic factors associated with telehealth adoption using a nationally representative survey. The wide array of descriptive findings and identified associations will help providers and health systems understand the factors that drive patients toward or away from telehealth visits as the technology becomes more routinely available across the United States, providing future directions for telehealth use and telehealth research.

Best Practices for Research in Virtual and Augmented Reality in Dermatology.

Virtual reality (VR) and augmented reality (AR) technologies have advanced rapidly in recent years. These cutting-edge technologies provide dermatology researchers, educators, proceduralists, and patients with opportunities in new scientific horizons. VR is a technology that facilitates immersive human experiences by allowing users to connect with various simulated environments through natural head and hand movements, whereas AR supplements a user's perception of their real environment with virtual elements. Despite technological advancements, there is limited literature on the methodological steps for conducting rigorous VR and AR research in dermatology. Effective storyboarding, user-driven design, and interdisciplinary teamwork play a central role in ensuring that VR/AR applications meet the specific needs of dermatology clinical and research teams. We present a step-by-step approach for their design, team composition, and evaluation in dermatology research, medical education, procedures, and habit formation strategies. We also discuss current VR and AR dermatology applications and the importance of ethical and safety considerations in deploying this new technology.

Evolution of a Project to Improve Inpatient-to-Outpatient Dermatology Care Transitions: Mixed Methods Evaluation.

BACKGROUND: In-hospital dermatological care has shifted from dedicated dermatology wards to consultation services, and some consulted patients may require postdischarge follow-up in outpatient dermatology. Safe and timely care transitions from inpatient-to-outpatient specialty care are critical for patient health, but communication around these transitions can be disjointed, and workflows can be complex. OBJECTIVE: In this 3-phase quality improvement effort, we developed and evaluated an intervention that leveraged an electronic health record (EHR) feature, known as SmartPhrase, to enable a new workflow to improve transitions from inpatient care to outpatient dermatology. METHODS: Phase 1 (February-March 2021) included interviews with patients and process mapping with key stakeholders to identify gaps and inform an intervention: a SmartPhrase table and associated workflow to promote collection of patient information needed for scheduling follow-up and closed-loop communication between dermatology and scheduling teams. In phase 2 (April-May 2021), semistructured interviews-with dermatologists (n=5), dermatology residents (n=5), and schedulers (n=6)-identified pain points and refinements. In phase 3, the intervention was evaluated by triangulating data from these interviews with measured changes in scheduling efficiency, visit completion, and messaging volume preimplementation (January-February 2021) and postimplementation (April-May 2021). RESULTS: Preintervention pain points included unclear workflow for care transitions, limited patient input in follow-up planning, multiple messaging channels (eg, EHR based, email, and phone messages), and time-inefficient patient tracking. The intervention addressed most pain points; interviewees reported the intervention was easy to adopt and improved scheduling efficiency, workload, and patient involvement. More visits were completed within the desired timeframe of 14 days after discharge during the postimplementation period (21/47, 45%) than the preimplementation period (28/41, 68%; P=.03). The messaging workload also decreased from 88 scheduling-related messages sent for 25 patients before implementation to 30 messages for 8 patients after implementation. CONCLUSIONS: Inpatient-to-outpatient specialty care transitions are complex and involve multiple stakeholders, thus requiring multifaceted solutions. With deliberate evaluation, broad stakeholder input, and iteration, we designed and implemented a successful solution using a standard EHR feature, SmartPhrase, integrated into a standardized workflow to improve the timeliness of posthospital specialty care and reduce workload.

Clinical Outcomes for Uptitration of Baricitinib Therapy in Patients With Severe Alopecia Areata: A Pooled Analysis of the BRAVE-AA1 and BRAVE-AA2 Trials.

Importance: Baricitinib is an oral selective Janus kinase 1/2 inhibitor that has achieved clinically meaningful outcomes for scalp, eyebrow, and eyelash hair regrowth in patients with severe alopecia areata (AA) at week 36 of treatment. Treatment with baricitinib, 4 mg, has resulted in higher response rates than baricitinib, 2 mg, at weeks 36 and 52. Objective: To determine the efficacy of uptitration to baricitinib, 4 mg, for 24 weeks in patients who had previously not responded to baricitinib, 2 mg (Severity of Alopecia Tool [SALT] score of >20). Design, Setting, and Participants: BRAVE-AA1 and BRAVE-AA2 are multicenter, placebo-controlled, phase 3 randomized clinical trials that were initiated on September 24, 2018, and July 8, 2019, respectively, with follow-up to 200 weeks (data cutoffs of November 11, 2021, and November 5, 2021, respectively). This pooled analysis reports long-term extension data up to week 76. At baseline, 1200 adult patients with severe AA (SALT score ≥50) were randomly assigned in a 3:2:2 ratio to receive baricitinib, 4 mg; baricitinib, 2 mg; or placebo. Patients treated with baricitinib remained on the same treatment dose until week 52. Patients were considered nonresponders to baricitinib, 2 mg, if they had a SALT score greater than 20 after 52 weeks of therapy. Main Outcomes and Measures: The proportions of patients achieving a SALT score of 20 or lower and clinician-reported outcome for eyebrow hair loss and eyelash hair loss scores of 0 or 1 (full coverage or minimal gaps) with 2-point or higher improvements from baseline (among those with baseline scores ≥2 [significant gaps to no notable hair]) were analyzed through week 76. Results: At week 52, of the 340 patients (mean [SD] age, 38.4 [12.9] years; 212 [62.4%] female) treated with baricitinib, 2 mg, 212 (62.4%) had a SALT score higher than 20 and were uptitrated to baricitinib, 4 mg. Two-thirds of these patients (142 of 212 [67.0%]) had a baseline SALT score of 95 to 100, indicating very severe AA. At week 76, 55 of the 212 patients (25.9%) had achieved a SALT score of 20 or lower. During the same period, response rates for clinician-reported outcome scores of 0 or 1 increased from 19.3% (31 of 161 patients) to 37.9% (61 of 161 patients) for eyebrows and from 24.1% (33 of 137 patients) to 40.9% (56 of 137 patients) for eyelashes. Conclusions and Relevance: In this pooled analysis of the BRAVE-AA1 and BRAVE-AA2 trials, uptitration of baricitinib, 2 mg, to baricitinib, 4 mg, in those who did not respond to the 2-mg dose resulted in meaningful improvement of response rates over the subsequent 24 weeks for scalp, eyebrow, and eyelash hair loss. Trial Registration: ClinicalTrials.gov Identifiers: NCT03570749 and NCT03899259.

Integrated single-cell chromatin and transcriptomic analyses of human scalp identify gene-regulatory programs and critical cell types for hair and skin diseases.

Genome-wide association studies have identified many loci associated with hair and skin disease, but identification of causal variants requires deciphering of gene-regulatory networks in relevant cell types. We generated matched single-cell chromatin profiles and transcriptomes from scalp tissue from healthy controls and patients with alopecia areata, identifying diverse cell types of the hair follicle niche. By interrogating these datasets at multiple levels of cellular resolution, we infer 50-100% more enhancer-gene links than previous approaches and show that aggregate enhancer accessibility for highly regulated genes predicts expression. We use these gene-regulatory maps to prioritize cell types, genes and causal variants implicated in the pathobiology of androgenetic alopecia (AGA), eczema and other complex traits. AGA genome-wide association studies signals are enriched in dermal papilla regulatory regions, supporting the role of these cells as drivers of AGA pathogenesis. Finally, we train machine learning models to nominate single-nucleotide polymorphisms that affect gene expression through disruption of transcription factor binding, predicting candidate functional single-nucleotide polymorphism for AGA and eczema.

Using qualitative methods to establish the clinically meaningful threshold for treatment success in alopecia areata.

PURPOSE: Traditionally, appropriate anchors are used to investigate the amount of change on a clinician-reported outcome assessment that is meaningful to individual patients. However, novel qualitative methods involving input from disease state experts together with patients may better inform the individual improvement threshold for demonstrating the clinical benefit of new treatments. This study aimed to establish a clinically meaningful threshold for treatment success for the clinician-reported Severity of Alopecia Tool (SALT) score for patients with alopecia areata (AA). METHODS: A purposive sample of 10 dermatologists expert in AA and 30 adult and adolescent patients with AA and a history of ≥ 50% scalp hair loss were recruited. Semi-structured interview questions explored the outcome that represented treatment success to clinicians and patients. Findings were analyzed using thematic methods to identify treatment success thresholds. RESULTS: Both informant groups confirmed scalp hair amount as the outcome of priority. Most expert clinicians considered a static threshold of 80% (n = 5) or 75% (n = 3) of the scalp hair as a treatment success. Most patient responses ranged from 70 to 90% (median: 80% of the scalp hair). Subsequently, queried patients confirmed that achieving SALT score ≤ 20 with treatment would be a success, as reflected in the Alopecia Areata Investigator Global Assessment (AA-IGA™). The novel qualitative processes used to inform this meaningful threshold reflects a clinician-then-patient process for: (a) confirmation of the patient outcome of priority; and (b) clinician input on a preliminary treatment success level for independent understanding among patients. CONCLUSION: This qualitative investigation of expert clinicians-then-patients with AA confirmed that achieving an amount of 80% or more scalp hair (SALT score ≤ 20) was an appropriate individual treatment success threshold indicating clinically meaningful improvement for patients with ≥ 50% scalp hair loss. A qualitative investigation of a quantifiable treatment success threshold is possible through a well-designed interview process with expert clinicians and the appropriate patient population.

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.

Artificial intelligence in the detection of skin cancer.

Recent advances in artificial intelligence (AI) in dermatology have demonstrated the potential to improve the accuracy of skin cancer detection. These capabilities may augment current diagnostic processes and improve the approach to the management of skin cancer. To explain this technology, we discuss fundamental terminology, potential benefits, and limitations of AI, and commercial applications relevant to dermatologists. A clear understanding of the technology may help to reduce physician concerns about AI and promote its use in the clinical setting. Ultimately, the development and validation of AI technologies, their approval by regulatory agencies, and widespread adoption by dermatologists and other clinicians may enhance patient care. Technology-augmented detection of skin cancer has the potential to improve quality of life, reduce health care costs by reducing unnecessary procedures, and promote greater access to high-quality skin assessment. Dermatologists play a critical role in the responsible development and deployment of AI capabilities applied to skin cancer.

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.

Defining Severity in Alopecia Areata: Current Perspectives and a Multidimensional Framework.

Alopecia areata (AA) is an autoimmune disease characterized by nonscarring hair loss. As a clinically heterogeneous disease, various classification systems have evolved for defining its severity. In this high-level review of the literature, we discuss the traditional classification systems for AA severity and their strengths and weaknesses. Most recent classifications have focused on the extent of scalp hair loss as a defining feature, but additional clinical aspects of the disease, including location, pattern, and duration of hair loss as well as impact on the patient's quality of life, are also relevant. These various components have typically been used unidimensionally to classify patients. We propose a multidimensional framework to define AA severity that incorporates multiple patient- and illness-related domains. Using such a framework, dermatologists may better assess the severity of the disease for the individual patient beyond the extent of hair loss.

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

IMPORTANCE: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for consideration by regulatory agencies and other stakeholders around the globe. OBJECTIVES: To initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders. EVIDENCE REVIEW: The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterward in the working group. FINDINGS: Artificial intelligence has the potential to improve health care access and patient outcome fundamentally while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are essential to attain this potential. Resolution impacts all levels of the design, validation, and implementation of AI in medicine. Design, validation, and implementation of AI warrant meticulous attention. CONCLUSIONS AND RELEVANCE: The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine before consideration by regulatory agencies. Important improvements in accessibility and quality of health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.

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.