Patient-Reported Outcome Measures and Their Clinical Applications in Dermatology

With more disease- and symptom-specific measures available and research pointing to increased usefulness, patient-reported outcome measures (PROMs) can be routinely used in clinical care. PROMs increase efficiency in healthcare, improve the clinician-patient relationship, and increase patient satisfaction with their care. PROMs can be administered before, during, and after clinic visits using paper-and-pencil, mobile phones, tablets, and computers. Herein, we combine available literature with expert views to discuss overcoming barriers and helping dermatologists incorporate PROMs into routine patient-centered care. We believe dermatology patients will benefit from broader PROM implementation and routine clinical use. However, a few major barriers exist: (1) cost to implement the technology, (2) selecting the right PROMs for each disease, and (3) helping both patients and clinicians understand how PROMs add to and complement their current clinical experience. We provide recommendations to assist dermatologists when considering whether to implement PROMs in their practices.

Organizational Readiness for Patient-Facing Mobile Teledermatology to Care for Established Veteran Patients in the United States

Purpose: To improve patient access to skin care, the Department of Veterans Affairs (VA) developed a patient-facing asynchronous mobile teledermatology application (app), which allows patients to follow up remotely with dermatologists. To understand how the app would be received in VA, we examined Organizational Readiness for Change (ORC), an important prelude to effective implementation, which includes the shared resolve and collective ability of organizational members to implement a change. Methods: We used a mixed-methods multiple case study approach to assess ORC at three VA facilities. Data derived from a site process call, surveys, and semistructured telephone interviews of VA staff, field notes, and administrative data. Results: Participants at all three facilities supported the intervention and recognized the value of using the app to increase patients' access to dermatologists, but expressed concerns largely related to disruption of the pre-existing clinical workflow. Participants at the facility most actively using the app had the highest overall ORC score and reported the most facilitators. Facility leadership support when guided by a clinical champion minimized barriers by recognizing the complexities of health care provision at specialty clinics. Discussion: While provider buy-in remained a barrier, leadership, guided by the clinical champion, played a critical role instituting implementation strategies. The strong association between the ORC survey score and the presence of facilitators and barriers suggests that the ORC survey may be a rapid, convenient, and effective tool for health care systems to identify favorable sites for wider implementation of mobile telehealth care. Clinical Trials Identifier: NCT03241589.

Digital skin imaging applications, part II: a comprehensive survey of post-acquisition image utilization features and technology standards

BACKGROUND

Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications.

MATERIALS AND METHODS

Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post-imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics.

RESULTS

A total of 20 post-image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in-app content to perform a user-directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user-uploaded photos and fewer than 10% provided clear data use and privacy policies.

CONCLUSION

Post-imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed.

Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients' Perception

Simple Summary

Early detection and resection of cutaneous melanoma are crucial for a good prognosis. However, visual distinction of early melanomas from benign nevi remains challenging. New artificial intelligence-based approaches for risk stratification of pigmented skin lesions provide screening methods for laypersons with increasing use of smartphone applications (apps). Our study aims to prospectively investigate the diagnostic accuracy of a CE-certified smartphone app, SkinVision^®, in melanoma recognition. Based on classification into three different risk scores, the app provides a recommendation to consult a dermatologist. In addition, both patients’ and dermatologists’ perspectives towards AI-based mobile health apps were evaluated. We observed that the app classified a significantly higher number of lesions as high-risk than dermatologists, which would have led to a clinically harmful number of unnecessary excisions. The diagnostic performance of the app in dichotomous classification of 1204 pigmented skin lesions (risk classification for nevus vs. melanoma) remained below advertised rates with low sensitivity (41.3–83.3%) and specificity (60.0–82.9%). The confidence in the app was low among both patients and dermatologists, and no patient favored an assessment by the app alone. Although smartphone apps are a potential medium for increasing awareness of melanoma screening in the lay population, they should be evaluated for certification with prospective real-world evidence.

Abstract

The exponential increase in algorithm-based mobile health (mHealth) applications (apps) for melanoma screening is a reaction to a growing market. However, the performance of available apps remains to be investigated. In this prospective study, we investigated the diagnostic accuracy of a class 1 CE-certified smartphone app in melanoma risk stratification and its patient and dermatologist satisfaction. Pigmented skin lesions ≥ 3 mm and any suspicious smaller lesions were assessed by the smartphone app SkinVision^® (SkinVision^® B.V., Amsterdam, the Netherlands, App-Version 6.8.1), 2D FotoFinder ATBM^® master (FotoFinder ATBM^® Systems GmbH, Bad Birnbach, Germany, Version 3.3.1.0), 3D Vectra^® WB360 (Canfield Scientific, Parsippany, NJ, USA, Version 4.7.1) total body photography (TBP) devices, and dermatologists. The high-risk score of the smartphone app was compared with the two gold standards: histological diagnosis, or if not available, the combination of dermatologists’, 2D and 3D risk assessments. A total of 1204 lesions among 114 patients (mean age 59 years; 51% females (55 patients at high-risk for developing a melanoma, 59 melanoma patients)) were included. The smartphone app’s sensitivity, specificity, and area under the receiver operating characteristics (AUROC) varied between 41.3–83.3%, 60.0–82.9%, and 0.62–0.72% according to two study-defined reference standards. Additionally, all patients and dermatologists completed a newly created questionnaire for preference and trust of screening type. The smartphone app was rated as trustworthy by 36% (20/55) of patients at high-risk for melanoma, 49% (29/59) of melanoma patients, and 8.8% (10/114) of dermatologists. Most of the patients rated the 2D TBP imaging (93% (51/55) resp. 88% (52/59)) and the 3D TBP imaging (91% (50/55) resp. 90% (53/59)) as trustworthy. A skin cancer screening by combination of dermatologist and smartphone app was favored by only 1.8% (1/55) resp. 3.4% (2/59) of the patients; no patient preferred an assessment by a smartphone app alone. The diagnostic accuracy in clinical practice was not as reliable as previously advertised and the satisfaction with smartphone apps for melanoma risk stratification was scarce. MHealth apps might be a potential medium to increase awareness for melanoma screening in the lay population, but healthcare professionals and users should be alerted to the potential harm of over-detection and poor performance. In conclusion, we suggest further robust evidence-based evaluation before including market-approved apps in self-examination for public health benefits.

Digital skin imaging applications, part I: Assessment of image acquisition technique features

BACKGROUND

The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality.

MATERIALS AND METHODS

Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), in-app connection to a healthcare provider (yes or no), and user type (patient, provider, or both).

RESULTS

Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching.

CONCLUSION

Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality.

Holistic health record for Hidradenitis suppurativa patients

Hidradenitis suppurativa (HS) is a recurrent inflammatory skin disease with a complex etiopathogenesis whose treatment poses a challenge in the clinical practice. Here, we present a novel integrated pipeline produced by the European consortium BATMAN (Biomolecular Analysis for Tailored Medicine in Acne iNversa) aimed at investigating the molecular pathways involved in HS by developing new diagnosis algorithms and building cellular models to pave the way for personalized treatments. The objectives of our european Consortium are the following: (1) identify genetic variants and alterations in biological pathways associated with HS susceptibility, severity and response to treatment; (2) design in vitro two-dimensional epithelial cell and tri-dimensional skin models to unravel the HS molecular mechanisms; and (3) produce holistic health records HHR to complement medical observations by developing a smartphone application to monitor patients remotely. Dermatologists, geneticists, immunologists, molecular cell biologists, and computer science experts constitute the BATMAN consortium. Using a highly integrated approach, the BATMAN international team will identify novel biomarkers for HS diagnosis and generate new biological and technological tools to be used by the clinical community to assess HS severity, choose the most suitable therapy and follow the outcome.

Deep learning tools for advancing drug discovery and development

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Usefulness of Smartphones in Dermatology: A US-Based Review

(1) Background: As smartphones have become more widely used, they have become an appealing tool for health-related functions. For dermatology alone, hundreds of applications (apps) are available to download for both patients and providers. (2) Methods: The Google Play Store and Apple App Store were searched from the United States using dermatology-related terms. Apps were categorized based on description, and the number of reviews, download cost, target audience, and use of AI were recorded. The top apps from each category by number of reviews were reported. Additionally, literature on the benefits and limitations of using smartphones for dermatology were reviewed. (3) Results: A total of 632 apps were included in the study: 395 (62.5%) were marketed towards patients, 203 (32.1%) towards providers, and 34 (5.4%) towards both; 265 (41.9%) were available only on the Google Play Store, 146 (23.1%) only on the Apple App Store, and 221 (35.0%) were available on both; and 595 (94.1%) were free to download and 37 (5.9%) had a cost to download, ranging from USD 0.99 to USD 349.99 (median USD 37.49). A total of 99 apps (15.7%) reported the use of artificial intelligence. (4) Conclusions: Although there are many benefits of using smartphones for dermatology, lack of regulation and high-quality evidence supporting the efficacy and accuracy of apps hinders their potential.

Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes – A Narrative Review

The role of technology in dermatology is expanding. Telemedicine and eHealth are increasingly being used by doctors and patients in the management of psoriasis. This is a narrative review of the literature relating to the use of digital technology in the management of psoriasis. We divided psoriasis e-health into three areas: mobile phone applications, teledermatology and artificial Intelligence (AI). Literature searches were conducted using the following databases: Pubmed, Google Scholar, Scopus, both app stores using App Annie platform. The following words were used in searches; psoriasis, dermatology, mobile phone application, application, app, smartphone, teledermatology, telemedicine, artificial intelligence, AI, machine learning in various combinations. We defined three key questions, one relating to each of the 3 areas. We then reviewed the relevant papers found in the searches and selected the papers of highest research quality and greatest relevance in order to answer the questions. In addition, for apps, operating systems for IOS and android devices were searched for apps containing the key word “psoriasis” in the title using the app analytic website www.appannie.com on 08/11/21. Research publications linked to these apps were reviewed.

Accuracy of commercially available smartphone applications for the detection of melanoma

Artificial intelligence (AI) has shown promise in the analysis of images for detection of melanoma.¹ The number of available dermatology smartphone applications ("apps") is rapidly growing and there is increasing interest in apps that provide diagnosis or triage of skin lesions.^{2, 3} A 2020 systematic review found that nine studies evaluating six apps had poor study design and high risk of bias.³ To date, no studies have evaluated the accuracy of apps using an independent test set of clinical images comparable to those submitted through smartphones .

AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation

Smartphone applications ("apps") with artificial intelligence (AI) algorithms are increasingly used in healthcare. Widespread adoption of these apps must be supported by a robust evidence-base and app manufacturers' claims appropriately regulated. Current CE marking assessment processes inadequately protect the public against the risks created by using smartphone diagnostic apps.

A smartphone application to improve the precision of biopsy site identification: A proof-of-concept study

Dermatologists rely on skin biopsies to diagnose cutaneous tumors and rashes. Skin biopsy sites should be accurately identified with conventional anatomical site descriptors in the pathology request form. Reliance upon free-text entries to describe these biopsy sites is prone to user error and can cause medical misadventures such as wrong-site follow-up surgery. We sought to determine whether a smartphone application (RightSite) could improve the precision of biopsy site labeling. We conducted a prospective proof-of-concept study of 100 smartphone-assisted skin biopsy site identifiers with matched comparison to 100 historical controls. Student's t-test was used to identify significant differences in the precision of anatomic descriptors before and after adoption of the application. We found a 69% improvement in precision of anatomic site labeling with the RightSite smartphone application (P < 0.0001). These data show smartphone-assisted biopsy site labeling improves the precision of anatomic site descriptors. Integrating graphical user interfaces into the electronic health records system could improve health care by standardizing anatomic site nomenclature and site-specific descriptors.

Eczema apps conformance with clinical guidelines: a systematic assessment of functions, tools and content

BACKGROUND

Eczema is a prevalent complex skin condition requiring active disease monitoring and personalized education. No studies have assessed the quality of apps that aim to support eczema self-management.

OBJECTIVES

To evaluate the quality and comprehensiveness of English, Chinese and Spanish self-management eczema smartphone apps for patients and/or their caregivers.

METHODS

A systematic assessment of eczema apps from July 2018 to November 2018. The assessment criteria were based on conformance with international eczema guidelines. The following domains were assessed: consistency and comprehensiveness of eczema-specific educational information; quality and comprehensiveness of eczema-specific tracking functions; compliance with health information best practice principles.

RESULTS

In total, 98 apps were assessed: 82 (84%) provided educational information; 38 (39%) tracking functions; and 13 (13%) both. We found that 34% (28/82) of apps provided misleading information, particularly regarding aspects of treatment and disease progression of eczema. Only 15% (12/82) provided international guideline supported information on pharmacological therapies and 16% (13/82) on nonpharmacological therapies. Among 38 apps with a tracking function, 82% (31/38) measured specific symptoms, disease severity or current skin condition and 89% (34/38) helped users to record medication usage including application of topicals. Environmental or dietary allergens were recorded by 34% (13/38). None of the included apps complied with all criteria for educational information, tracking functions or health information principles.

CONCLUSIONS

Eczema apps have not yet reached their potential. The large variance in quality of eczema apps highlights the need for quality assurance mechanisms for health apps and guidance for clinicians that would enable them to make personalized recommendations for patients and caregivers. What's already known about this topic? There is limited information about the quality of eczema self-management smartphone apps on the global market. What does this study add? This systematic assessment evaluated all English, Chinese and Spanish language apps that support eczema self-management. The majority did not conform with information in guidelines and insufficiently support evidence-based self-management. The large variance in the quality of eczema apps highlights the need for mechanisms to ensure app quality and to guide personalized app selection for patients, caregivers and doctors.

The Utility of Smartphone Applications and Technology in Wound Healing

Appropriate management of both acute and chronic wounds is a dynamic practice that consumes large amounts of time and financial resources within the health care system. Traditionally, wounds were measured clinically and subjectively, leading to inaccuracies in assessing wound progression and healing over time. The recent development of mobile applications and digital equipment in medicine provides an opportunity for significant improvement in wound care through the incorporation of “smart” technologies in clinical practice. The utility of these technologies has been assessed regarding the treatment of diabetic foot ulcers, burns, and general wounds. The focus of this review is to provide an update on the current status of mobile applications and digital technology in the management of wounds.