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Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool for skin lesion assessment. We report prospective real-world performance from its deployment within skin cancer pathways at two National Health Service hospitals (UK). Between July 2021 and October 2022, 14,500 cases were seen, including patients 18–100 years old with Fitzpatrick skin types I–VI represented. Based on 8,571 lesions assessed by DERM with confirmed outcomes, versions A and B demonstrated very high sensitivity for detecting melanoma (95.0–100.0%) or malignancy (96.0–100.0%). Benign lesion specificity was 40.7–49.4% (DERM-vA) and 70.1–73.4% (DERM-vB). DERM identified 15.0–31.0% of cases as eligible for discharge. We show DERM performance in-line with sensitivity targets and pre-marketing authorisation research, and it reduced the caseload for hospital specialists in two pathways. Based on our experience we offer suggestions on key elements of post-market surveillance for AIaMDs.

Background The current classification for alopecia areata (AA) does not provide a consistent assessment of disease severity. Objective To develop an AA severity scale based on expert experience. Methods A modified Delphi process was utilized. An advisory group of 22 AA clinical experts from the United States was formed to develop this AA scale. Representatives from the pharmaceutical industry provided feedback during its development. Results Survey responses were used to draft severity criteria, aspiring to develop a simple scale that may be easily applied in clinical practice. A consensus vote was held to determine the final AA severity statement, with all AA experts agreeing to adopt the proposed scale. Limitations The scale is a static assessment intended to be used in clinical practice and not clinical trials. Conclusion The final AA disease severity scale, anchored in the extent of hair loss, captures key features commonly used by AA experts in clinical practice. This scale will better aid clinicians in appropriately assessing severity in patients with this common disease.

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.

Genome-wide association studies (GWAS) of hair and skin disease have identified many genetic variants associated with disease phenotypes, but identifying causal variants and interpreting their function requires deciphering gene-regulatory networks in disease-relevant cell types. To this end, we generated matched scRNA- and scATAC-seq profiles of human scalp biopsies, identifying diverse cell types of the hair follicle niche. By interrogating the integrated datasets across multiple levels of cellular resolution, we infer 50-100% more enhancer-gene links than prior approaches, and show that the aggregate accessibility at linked enhancers 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 GWAS signals are strongly and specifically enriched in dermal papilla cell open chromatin regions, supporting the role of these cells as key drivers of AGA pathogenesis. Finally, we trained machine-learning models to nominate SNPs that affect gene expression through disruption of specific transcription factor binding motifs, identifying candidate functional SNPs linked to expression of WNT10A in AGA and IL18RAP in eczema. Together, this work reveals principles of gene regulation and identifies gene regulatory consequences of natural genetic variation in complex skin and hair diseases.

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.To pilot a new model of dermatologic care to overcome barriers for senior living communities during the COVID-19 pandemic and assess patient satisfaction.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.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.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.

Previous QOL and disease burden studies have not captured all relevant aspects of living with alopecia areata (AA). To better understand the burden and everyday experience of living with moderate-to-severe AA, a cross-sectional, online, quantitative-qualitative survey was developed to assess symptoms, relationships, productivity, treatments, and financial burden. Adult patients were recruited from the National Alopecia Areata Foundation database. Data were analyzed descriptively. A total of 216 patients completed the survey. Most were female (83%), aged ≥45 years (59%), and white (78%). Nearly 2 of 3 respondents (62%) made different major life decisions (regarding relationships, education, or career) owing to AA. Most respondents (85%) stated coping with AA as a daily challenge, citing mental health issues, concealing hair loss, and others' reactions; 47% reported anxiety and/or depression. Many patients (75%) persistently concealed hair loss (mean time spent, 10.3 h/wk). Treatment discontinuation was common owing to lack of efficacy, side effects, and cost. Associated expenditures included buying wigs or hairpieces and psychotherapy (mean ∼$2,000/y each). Survey respondents comprised a self-selected sample, which may not reflect the entire population. The impact of AA extends beyond cosmetic concerns and carries a considerable psychosocial burden. Efficacious, less burdensome AA treatments are needed to regrow hair and alleviate psychosocial sequelae.

There are no tools to evaluate eyebrow involvement in patients with alopecia areata. We developed and assessed the reliability of the Brigham Eyebrow Tool for Alopecia (BETA) as a quantitative evaluation of eyebrow alopecia areata. BETA uses facial landmarks of eyebrow anatomy and is calculated using surface area and density. A total of 50 eyebrow images with varying levels of hair loss were distributed to six board-certified dermatologists at three academic medical centers with standardized instructions and examples. Interrater and intrarater reliability were calculated using intraclass correlation coefficients (ICCs). BETA demonstrated high interrater (ICC = 0.88, confidence interval = 0.83-0.92 right eyebrow scores and ICC = 0.90, confidence interval = 0.85-0.94 left eyebrow scores) and intrarater (ICC = 0.90, confidence interval = 0.85-0.93 right eyebrow scores and ICC = 0.91, confidence interval = 0.87-0.94 left eyebrow scores) reliability. When measured in the same patient with varying degrees of hair loss over time, BETA demonstrated sensitivity to change. BETA is a simple and reliable objective assessment of eyebrow alopecia areata. BETA is easy-to-use and quick to calculate, making it feasible for a variety of clinical and research settings. Although developed for alopecia areata, we hope that BETA will be investigated in other etiologies of eyebrow alopecia to serve as a universal tool for monitoring disease progression, improvement, and response to treatment.

Alopecia areata affects not only scalp hair but also other sites of body hair, including eyebrows. Our objective was to investigate the importance of eyebrows in the treatment goals of patients with alopecia areata. Through an online questionnaire, subjects were asked to assess satisfaction with the visually depicted level of response to treatment, using edited photographs depicting a range of eyebrows and scalp hair growth. The questionnaire was completed by 1,741 adults. Absent or partial growth of eyebrows and scalp hair elicited25% satisfaction. Images depicting either complete eyebrows or complete scalp hair achieved satisfaction in50% of participants. More participants were satisfied with complete eyebrows and no scalp hair (69%) than complete eyebrows and partial scalp hair (51%). Only when both eyebrows and scalp hair were completely regrown did extreme satisfaction levels reach 90.4%. Limitations include the online nature of the survey, lack of control group, and self-reported severity of alopecia areata in participants. These results suggest that eyebrows may be as important as scalp hair for patients assessing theoretical responses to treatment for alopecia areata. Future clinical studies should consider growth of eyebrows as an outcome measure on par with scalp hair growth.

Medical innovation is crucial to advancing our field, and physicians have the potential to play a leading role due to their daily patient care experiences. The objective of this study was to evaluate the interest in, and barriers to participating in innovation. Two surveys were conducted; the first cross-sectional survey was conducted among attendees of the Advancing Innovation in Dermatology Forum in Feburary 2019. The second survey was conducted among trainees (resident/fellows) and faculty dermatologists at Brown, Emory, Iowa, Stanford, and Vanderbilt Universities between June and November 2019. Demographic data were collected, as well as factors involved with identifying problems, developing solutions, training in innovation, and perceived barriers to innovation. In the first survey, the greatest perceived benefits include bringing joy to one's work and increasing professional fulfillment with work. Innovation was also perceived to decrease burnout. In the second survey of academic centers, faculty more commonly expressed interest in identifying problems (p = 0.04), and was also more confident in their ability to generate solutions to these problems as compared to trainees (p

Summary Background Valid patient‐reported outcome (PRO) measures are required to evaluate alopecia areata (AA) treatments. Objectives To develop a content‐valid and clinically meaningful PRO measure to assess AA scalp hair loss with scores comparable with the five‐response‐level Alopecia Areata Investigator Global Assessment (AA‐IGA™). Methods A draft PRO measure was developed based on input from 10 clinical experts in AA. The PRO measure was cognitively debriefed, modified and finalized through two rounds of qualitative semistructured interviews with patients with AA who had experienced ≥ 50% scalp hair loss. Data were thematically analysed. Results Adults (round 1: n = 25; round 2: n = 15) and adolescents aged 15–17 years (round 1: n = 5) in North America participated. All patients named scalp hair loss as a key AA sign or symptom. Patients demonstrated the ability to self‐report their current amount of scalp hair using percentages. In round 1 not all patients interpreted the measurement concept consistently; therefore, the PRO was modified to clarify the measurement concept to improve usability. Following modifications, patients in round 2 responded without difficulty to the PRO measure. Patients confirmed that they could use the five‐level response scale to rate their scalp hair loss: no missing hair, 0%; limited, 1–20%; moderate, 21–49%; large, 50–94%; nearly all or all, 95–100%. Almost all patients deemed hair regrowth resulting in ≤ 20% scalp hair loss a treatment success. Conclusions The Scalp Hair Assessment PRO™ is a content‐valid, clinically meaningful assessment of distinct gradations of scalp hair loss for evaluating AA treatment for patients with ≥ 50% hair loss at baseline., What is already known about this topic? Assessing patient‐reported outcomes is critical in patient‐focused drug development.The patient perspective on their own signs, symptoms and unmet needs provides a key clinical trial data source for evaluating treatment outcomes.Clinically meaningful and content‐valid patient‐reported outcome (PRO) measures are required to collect these data in alopecia areata (AA) clinical trials. What does this study add? This study developed a content‐valid PRO measure for patients with AA to self‐report the status of their scalp hair loss.This novel PRO measure allows scalp hair loss to be characterized into clinically meaningful gradations across the range of 0–100% missing scalp hair. What are the clinical implications of this work? The Scalp Hair Assessment PRO™ provides a new clinical outcome assessment of AA scalp hair loss for use in both clinical trials to evaluate the efficacy of novel treatments and in clinical practice to obtain a patient‐centred perspective on the key sign and symptom of AA.With corresponding response options to the newly developed Alopecia Areata Investigator Global Assessment (AA‐IGA™), clinician and patient assessments are comparable to elucidate any perceived differences between respondents. Linked Comment: Rencz. Br J Dermatol 2020; 183:986–987.

Author(s): Wyrwich, KW; Kitchen, H; Knight, S; Aldhouse, NVJ; Macey, J; Nunes, FP; Dutronc, Y; Mesinkovska, N; Ko, JM; King, BA | Abstract: BackgroundContent-valid and clinically meaningful instruments are required to evaluate outcomes of therapeutic interventions in alopecia areata (AA).ObjectivesTo develop an Investigator's Global Assessment (IGA) to interpret treatment response in AA treatment studies.MethodsQualitative interviews were conducted in the USA with expert dermatologists and with patients with AA who had experienced ≥ 50% scalp-hair loss. Thematic data analysis identified critical outcomes and evaluated the content validity of the new IGA.ResultsExpert clinicians (n = 10) judged AA treatment success by the amount of scalp-hair growth (median 80% scalp hair). Adult (n = 25) and adolescent (n = 5) patients participated. Scalp-hair loss was the most bothersome AA sign/symptom for most patients. Perceived treatment success - short of 100% scalp hair - was the presence of ~ 70-90% scalp hair (median 80%). Using additional clinician and patient insights, the Alopecia Areata Investigator Global Assessment (AA-IGA™) was developed. This clinician-reported outcome assessment is an ordinal, static measure comprising five severity categories of scalp-hair loss. Nearly all clinicians and patients in this study agreed that, for patients with ≥ 50% scalp-hair loss, successful treatment would be hair regrowth resulting in ≤ 20% scalp-hair loss.ConclusionsWe recommend using the Severity of Alopecia Tool to assess the extent (0-100%) of scalp-hair loss. The AA-IGA is a robust ordinal measure providing distinct and clinically meaningful gradations of scalp-hair loss that reflects patients' and expert clinicians' perspectives and treatment expectations. What is already known about this topic? The Severity of Alopecia Tool is widely used to assess the extent of scalp-hair loss in patients with alopecia areata. Guidelines define treatment success as a 50% improvement in scalp hair, and clinical trials have used dynamic thresholds of 50% and 90%. However, there is no clinical consensus on these endpoints, and patient perspectives on treatment success are unknown. What does this study add? Through qualitative interviews with 10 expert dermatologists and 30 patients with alopecia areata who had experienced ≥ 50% scalp-hair loss, we developed the Alopecia Areata Investigator Global Assessment (AA-IGA™) to measure five clinically meaningful gradations of alopecia areata scalp-hair loss that reflects patients' and clinicians' perspectives and expectations of treatment success in alopecia areata treatment studies. What are the clinical implications of this work? The AA-IGA is a robust ordinal measure that can inform clinical evaluation of alopecia areata treatment outcomes. The AA-IGA can be used to determine clinically meaningful treatment success for alopecia areata, with success defined by patients and clinicians as reaching ≤ 20% scalp-hair loss. Linked Comment: Blome. Br J Dermatol 2020; 183:609.

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.

BACKGROUND Both clinicians and patients have increasingly turned to telemedicine to improve access to care, even in physical exam dependent specialties like dermatology. However, little is known whether teledermatology supports effective and timely transitions from inpatient to outpatient care, a common care coordination gap. OBJECTIVE Using mixed methods, we sought to evaluate how teledermatology use 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-up within 90 and 14 days post-discharge across three phases: (1) June-September 2019 (pre-teledermatology), (2) June-September 2020 (early teledermatology), (3) February-May 2021 (sustained teledermatology). Time from discharge to scheduling and completion of patient’s follow-up visit for each care modality was also compared. Dermatology clinicians and schedulers were also interviewed between April and May 2021 to assess their perceptions on teledermatology for post-discharge patients. RESULTS The clinic’s capacity to provide follow-up to patients transitioning from inpatient care increased by 36% when teledermatology services were introduced; more patients completed follow-up within 90 days post-discharge during early (n=101) and sustained (n=100) teledermatology use than at baseline (n=74). During early teledermatology use, 61% (n=62) of follow-ups were conducted via video. This decreased significantly to 47% (n=47) 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 followed up within the recommended 14 days post-discharge did not differ significantly between video and in-clinic visits during the early (53% vs. 38%; P = .15) or sustained (51% vs. 49%; P = .29) teledermatology periods. Interviewees agreed that teledermatology will continue to be offered. Most considered post-discharge follow-up patients to be ideal candidates for teledermatology since they had had a recent in-person assessment and might have difficulty attending in-clinic visits due to 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. CONCLUSIONS Teledermatology could be an important tool in maintaining accessible, flexible, and convenient care for recently discharged patients needing follow-up care. Teledermatology increased clinic capacity, even during a pandemic, although timeliness of care transitions were not improved. Ultimately, the care modality should be determined in communication with patients to incorporate their and their family/caregivers’ preferences.

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.

Dupilumab is a monoclonal antibody targeting interleukin-4 and interleukin-13 for the treatment of moderate-to-severe atopic dermatitis.1 We report a case of occupational irritant hand dermatitis in a nonatopic individual that improved on dupilumab.

Importance: Managing chronic skin disease is often frustrating for both providers and patients, sometimes resulting in delayed diagnosis, inadequate therapy, and inconsistent care.Objective: This study performs stakeholder analyses to identify unmet clinical needs in chronic skin disease management. Methods: Survey of 33 providers and 25 patients at a Stanford Health Care Dermatology department.Results: When evaluating a chronic skin condition such as psoriasis, 79% of dermatologists rely solely on subjective documentation (gestalt, body surface area, descriptive exam). Objective documentation (photographs or scoring assessment tools) is used by 21% of providers upon initial assessment and by 7% of providers to assess change in disease between office visits. While 83% of providers were comfortable assessing change in disease severity based on prior document by oneself, only 31% were comfortable assessing change based on prior documentation by another provider (p

Introduction: Currently there are no portable solutions to robustly document and longitudinally monitor dynamically changing chronic skin conditions. This study set out to engineer and test a mobile-based 3D imaging solution for chronic skin diseases to enhance clinical workflow and patient care.Methods: SkinSpecs uses smartphone-captured videos of patients’ skin disease and renders 3D true-to-life models that were evaluated by Stanford Health Care dermatologists.Results: We utilized video input to accurately reconstruct interactive 3D models of 16 different skin conditions from 31 patients. Assessment of SkinSpecs 3D reconstruction is faster (p

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.

Introduction Alopecia areata (AA) is an autoimmune disorder causing sudden, non-scarring hair loss. There are currently no drugs approved for AA treatment. This study assessed prevalence of comorbidities, treatments, and healthcare costs and resource utilization among patients with AA in the USA. Methods Patients diagnosed with AA between January 2011 and December 2018 were identified in IBM MarketScan® Research Databases. Eligible patients had no other hair loss-related disorders and were continuously enrolled with medical and pharmacy benefits at least 12 months before and after AA diagnosis. Descriptive statistics were used to summarize comorbid conditions, treatments related to AA or other autoimmune/inflammatory conditions, and all-cause and AA-specific healthcare costs and resource utilization identified from claims data. Results A total of 68,121 patients with AA were identified. Mean (SD) age was 40.3 (17.8) years and 61.0% were female. The most common comorbidities included hyperlipidemia (22.4%), hypertension (21.8%), thyroid disorders (13.1%), contact dermatitis or eczema (10.8%), depression (9.5%), and anxiety (8.4%). Comorbid autoimmune diseases included atopic dermatitis (2.8%), psoriasis (2.1%), chronic urticaria (1.5%), and rheumatoid arthritis (1.1%). During the 12-month follow-up period, 37,995 patients (55.8%) were prescribed treatment for their AA or other comorbid autoimmune/inflammatory disease; 44.9% of treated patients were prescribed therapy within 7 days of AA diagnosis. Of patients receiving treatment, 80.3% received topical steroids and 30.0% received oral steroids. Mean (SD) total healthcare costs were $11,241.21 ($43,839.69) for all-causes and $419.12 ($1534.99) for AA. AA-related expenses were driven by outpatient and prescription costs. Conclusion Patients with AA have a high comorbidity burden and lack of treatment. Current AA treatments, including systemic therapies other than oral steroids, were not frequently utilized in this study population. Healthcare costs incurred by patients with AA went beyond AA-related expenses. Longitudinal data are needed to better understand treatment trajectories and the disease burden in patients with AA. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-021-01845-0.

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 can additionally 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 the treatment of AA, and 30 adult and adolescent patients with AA and a history of ≥50% scalp hair loss were recruited. Semi-structured interview questions explored thresholds that represented treatment success to clinicians and patients with AA. Findings were analyzed using thematic methods to identify treatment success thresholds.Results: Expert clinicians considered a static threshold of 80% (n=5) or 75% (n=3) of the scalp hair as a treatment success. Patient responses ranged from 70 - 90% (median 80% of the scalp hair). Subsequently, queried patients confirmed that achieving SALT score ≤20 with treatment would be a success. Reflections on the methodology include: clinician perceptions can be informed by current clinical practice and knowledge of existing treatments and research and clinicians easily identified thresholds through qualitative discussions; patients were able to understand and identify thresholds for improvement less than complete absence of disease. Conclusions: This qualitative investigation of expert clinicians and 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 is possible through well-designed interview questions for the indication and patient population. Both clinician and patient input plays a critical role in understanding the clinical benefit meaningful to patients.

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.

Background There are no treatments approved by the Food and Drug Administration for alopecia areata. Objective To evaluate the efficacy and safety of baricitinib in patients with ≥50% scalp hair loss in a phase 2 study of adults with alopecia areata (BRAVE-AA1). Methods Patients were randomized 1:1:1:1 to receive placebo or baricitinib 1 mg, 2 mg, or 4 mg once daily. Two consecutive interim analyses were performed after all patients completed weeks 12 and 36 or had discontinued treatment prior to these time points. The primary endpoint was the proportion of patients achieving a Severity of Alopecia Tool (SALT) score ≤20 at week 36. Logistic regression was used with nonresponder imputation for missing data. Results A total of 110 patients were randomized (placebo, 28; baricitinib 1-mg, 28; 2-mg, 27; 4-mg, 27). The baricitinib 1-mg dose was dropped after the first interim analysis based on lower SALT30 response rate. At week 36, the proportion of patients achieving a SALT score of ≤20 was significantly greater in baricitinib 2-mg (33.3%, P = .016) and 4-mg (51.9%, P = .001) groups versus placebo (3.6%). Baricitinib was well tolerated with no new safety findings. Limitations Small sample size limits generalizability of results. Conclusion These results support the efficacy and safety of baricitinib in patients with ≥50% scalp hair loss.

Choline geranate deep eutectic solvent/ionic liquid (CAGE) has shown several desirable therapeutic properties including antimicrobial activity and ability to deliver drugs transdermally in research laboratories. Here, we describe the first report of clinical translation of CAGE from the lab into the clinic for the treatment of rosacea, a common chronic inflammatory skin disorder that affects the face. We describe the seven steps of clinical translation including (a) scale‐up, (b) characterization, (c) stability analysis, (d) mechanism of action, (e) dose determination, (f) GLP toxicity study, and (g) human clinical study. We describe the challenges and outcomes in these steps, especially those that uniquely arise from the deep eutectic nature of CAGE. Our translational efforts led to a 12‐week open‐label phase 1b cosmetic study with CAGE1:2 gel (CGB400) in mild–moderate facial rosacea in 26 patients where CGB400 exhibited a marked reduction in the number of inflammatory lesions. These results demonstrate the therapeutic potential of CGB400 for treating rosacea as well as it provides insights into the translational journey of deep eutectic solvents, in particular CAGE, for dermatological applications.

Meaningful patient input to understand disease experience and patient expectations for improvement with treatment is essential for the selection and development of outcome measures for alopecia areata (AA) clinical trials. This study explored the physical signs and symptoms of AA through 30 semistructured interviews with adult (n = 25) and adolescent (n = 5) patients experienced with severe or very severe AA. Scalp hair loss was overwhelmingly the most important sign and symptom of AA. Nearly all patients (90%) considered scalp hair loss in their top three most bothersome physical signs and symptoms of AA, with 77% (n = 23) naming scalp hair loss as the most bothersome symptom. Other identified signs and symptoms in the top three most bothersome included eyebrow, eyelash, nose, body, and facial hair loss, as well as eye irritation and nail damage and/or appearance. Eyebrow (16%, n = 4), eyelash (4%, n = 1), nasal (4%, n = 1), and body (4%, n = 1) hair loss were identified by seven adult patients as the most bothersome signs and symptoms of AA. Conceptual saturation confirmed that a comprehensive understanding of this patient population's physical AA-related signs and symptoms was obtained. These findings indicate that the primary objective for new AA treatments for this patient population should be meaningful improvement in scalp hair growth to address the most troubling unmet need.

Artificial intelligence (AI)-based applications have the potential to improve the quality and efficiency of patient care in dermatology. Unique challenges in the development and validation of these technologies may limit their generalizability and real-world applicability. Before the widespread adoption of AI interventions, randomized trials should be conducted to evaluate their efficacy, safety, and cost effectiveness in clinical settings. The recent Standard Protocol Items: Recommendations for Interventional Trials–AI extension and Consolidated Standards of Reporting Trials–AI extension guidelines provide recommendations for reporting the methods and results of trials involving AI interventions. High-quality trials will provide gold standard evidence to support the adoption of AI for the benefit of patient care.

Background Eyebrow and eyelash hair loss and nail damage—in addition to scalp hair loss—are important signs/symptoms of alopecia areata (AA) to patients and deserve assessment in AA clinical trials. Objectives Our objective was to develop clinician-reported outcome (ClinRO) and patient-reported outcome (PRO) measures and accompanying photoguides to aid in the assessment of AA-related eyebrow, eyelash and nail signs/symptoms. Methods Iterative rounds of qualitative, semi-structured interviews were conducted with US expert dermatologists and North American patients with AA. Patients with eyebrow, eyelash and nail involvement were purposefully sampled. Interview transcripts were qualitatively analyzed. Results Dermatologists (n = 10) described eyebrow and eyelash loss as concerning for affected patients and, along with nail appearance, as deserving assessment. Dermatologist data informed the development of single item, 4-point Likert-type ClinRO and PRO measures of current eyebrow loss, eyelash loss and nail appearance and a PRO measure of eye irritation. Patients (n = 45, age 15–72 years) confirmed the importance and relevance of these signs/symptoms. Interim revision resulted in measures that were understood by and relevant to patients. Dermatologists (n = 5) and patients (n = 10, age 21–54 years) participated in the development of the eyebrow, eyelash and nail photoguides and confirmed that they included photos that appropriately represented different severity levels and were helpful to derive and standardize ratings across raters. Conclusions The ClinRO and PRO measures for eyebrow, eyelash and nail appearance, with their accompanying photoguides and the PRO Measure for Eye Irritation provide clear and meaningful assessments of outcomes important to patients with AA. Electronic supplementary material The online version of this article (10.1007/s40257-020-00545-9) contains supplementary material, which is available to authorized users.

Introduction Teledermatology services that function separately from patients’ primary electronic health record (EHR) can lead to fragmented care, poor provider communication, privacy concerns and billing challenges. This study addresses these challenges by developing PhotoCareMD, a store-and-forward (SAF) teledermatology consultation workflow built entirely within an existing Epic-based EHR. Methods Thirty-six primary care physicians (PCPs) from eight outpatient clinics submitted 215 electronic consults (eConsults) for 211 patients to a Stanford Health Care dermatologist via PhotoCareMD. Comparisons were made with in-person referrals for this same dermatologist prior to initiation of PhotoCareMD. Results Compared to traditional in-person dermatology clinic visits, eConsults decreased the time to diagnosis and treatment from 23 days to 16 hours. The majority (73%) of eConsults were resolved electronically. In-person referrals from PhotoCareMD (27%) had a 50% lower cancellation rate compared with traditional referrals (11% versus 22%). The average in-person visit and documentation was 25 minutes compared with 8 minutes for an eConsult. PhotoCareMD saved 13 additional clinic hours to be made available to the dermatologist over the course of the pilot. At four patients per hour, this opens 52 dermatology clinic slots. Over 96% of patients had a favourable experience and 95% felt this service saved them time. Among PCPs, 100% would recommend PhotoCareMD to their colleagues and 95% said PhotoCareMD was a helpful educational tool. Discussion An internal SAF teledermatology workflow can be effectively implemented to increase access to and quality of dermatologic care. Our workflow can serve as a successful model for other hospitals and specialties.

Dupilumab, the first monoclonal antibody approved by the US Food and Drug Administration for the treatment of moderate-to-severe atopic dermatitis (AD), inhibits interleukin (IL)-4 and IL-13 via blockade of IL-4Ra, inhibiting helper T cell (Th)2 signaling. Allergic contact dermatitis (ACD) has a complex and dynamic pathophysiology, in which at least 1 or more of the Th1, Th2, and Th17/22 pathways may mediate reactions. Patients with AD are at risk of ACD. Here we present a patient with AD and ACD who was patch tested before starting dupilumab and during therapy.

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., Comment: 12 pages, 5 figures, Preprint of an article published in Pacific Symposium on Biocomputing \c{opyright} 2020 World Scientific Publishing Co., Singapore, http://psb.stanford.edu/