Factors Impacting Clinicians' Adoption of a Clinical Photo Documentation App and its Implications for Clinical Workflows and Quality of Care: Qualitative Case Study

A sociotechnical framework to assess patient-facing eHealth tools: results of a modified Delphi process

Among the thousands of eHealth tools available, the vast majority do not get past pilot phases because they cannot prove value, and only a few have been systematically assessed. Although multiple eHealth assessment frameworks have been developed, these efforts face multiple challenges. This study aimed to address some of these challenges by validating and refining an initial list of 55 assessment criteria based on previous frameworks through a two-round modified Delphi process with in-between rounds of interviews. The expert panel (n = 57) included participants from 18 countries and 9 concerned parties. A consensus was reached on 46 criteria that were classified into foundational and contextual criteria. The 36 foundational criteria focus on evaluating the eHealth tool itself and were grouped into nine clusters: technical aspects, clinical utility and safety, usability and human centricity, functionality, content, data management, endorsement, maintenance, and developer. The 10 contextual criteria focus on evaluating the factors that vary depending on the context the tool is being evaluated for and were grouped into seven clusters: data-protection compliance, safety regulatory compliance, interoperability and data integration, cultural requirements, affordability, cost-benefit, and implementability. The classification of criteria into foundational and contextual helps us assess not only the quality of an isolated tool, but also its potential fit in a specific setting. Criteria subscales may be particularly relevant when determining the strengths and weaknesses of the tool being evaluated. This granularity enables different concerned parties to make informed decisions about which tools to consider according to their specific needs and priorities.

Medical Personnel Behavior Preferences for Providing mHealth Service in China: A Discrete Choice Experiment

Purpose

This study aims to investigate factors affecting medical personnel behavioral preferences for providing mHealth in China, so as to provide decision-making basis for mHealth providers and managers to encourage more doctors to participate in mHealth service delivery.

Methods

Typical case sampling techniques were applied in a hospital setting to conduct a discrete choice experimental questionnaire survey of doctors (n=216) concerning mHealth preferences between July and October 2022. A conditional logit model was used to assess medical personnel preferences for each attribute and level of mHealth services.

Results

Length of service, information security, subjects of treatment and financial compensation all have a significant effect on medical staff's preference for providing mHealth services (p < 0.05). In terms of service duration and financial compensation, medical staff preferred mHealth services that provided shorter service duration and higher financial compensation; in terms of information security, medical staff preferred mHealth services with confidentiality of diagnostic and therapeutic information compared to information disclosure; and in terms of treatment targets, medical staff preferred the general population compared to key populations, such as pregnant women, the elderly, infants and children.

Conclusion

The preference of medical professionals to provide mHealth services is affected by a variety of factors. By enhancing the confidentiality of information in mHealth services, providing more options for service recipients, increasing their financial compensation, and shortening the duration of the service or increasing the number of service hours that can be adjusted can guide improvement of mHealth services and promoting of its adoption among medical professionals.

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

Assessing the Quality and Impact of eHealth Tools: Systematic Literature Review and Narrative Synthesis

BACKGROUND

Technological advancements have opened the path for many technology providers to easily develop and introduce eHealth tools to the public. The use of these tools is increasingly recognized as a critical quality driver in health care; however, choosing a quality tool from the myriad of tools available for a specific health need does not come without challenges.

OBJECTIVE

This review aimed to systematically investigate the literature to understand the different approaches and criteria used to assess the quality and impact of eHealth tools by considering sociotechnical factors (from technical, social, and organizational perspectives).

METHODS

A structured search was completed following the participants, intervention, comparators, and outcomes framework. We searched the PubMed, Cochrane, Web of Science, Scopus, and ProQuest databases for studies published between January 2012 and January 2022 in English, which yielded 675 results, of which 40 (5.9%) studies met the inclusion criteria. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions were followed to ensure a systematic process. Extracted data were analyzed using NVivo (QSR International), with a thematic analysis and narrative synthesis of emergent themes.

RESULTS

Similar measures from the different papers, frameworks, and initiatives were aggregated into 36 unique criteria grouped into 13 clusters. Using the sociotechnical approach, we classified the relevant criteria into technical, social, and organizational assessment criteria. Technical assessment criteria were grouped into 5 clusters: technical aspects, functionality, content, data management, and design. Social assessment criteria were grouped into 4 clusters: human centricity, health outcomes, visible popularity metrics, and social aspects. Organizational assessment criteria were grouped into 4 clusters: sustainability and scalability, health care organization, health care context, and developer.

CONCLUSIONS

This review builds on the growing body of research that investigates the criteria used to assess the quality and impact of eHealth tools and highlights the complexity and challenges facing these initiatives. It demonstrates that there is no single framework that is used uniformly to assess the quality and impact of eHealth tools. It also highlights the need for a more comprehensive approach that balances the social, organizational, and technical assessment criteria in a way that reflects the complexity and interdependence of the health care ecosystem and is aligned with the factors affecting users' adoption to ensure uptake and adherence in the long term.

Evaluating the effectiveness of a mobile application to improve the quality, collection, and usability of forensic documentation of sexual violence

BACKGROUND

Survivors of sexual violence deserve timely and high-quality forensic examination, evidence collection, and documentation as part of comprehensive care. However, in many countries, the quality of medical-legal documentation is severely limited. MediCapt is an innovative digital application that enables clinicians to document forensic medical evidence as well as capture and securely store forensic photographs of injuries. This study evaluated the effectiveness and usability of MediCapt to document forensic medical evidence of sexual violence.

METHODS

This mixed-methods evaluation involved key-informant interviews, usability questionnaires, and forensic record reviews. Participants included clinicians, medical records personnel, information technology personnel, and health facility administrators, as well as law enforcement and legal professionals in Kenya.

RESULTS

The Physicians for Human Rights (PHR) data quality checklist found that using MediCapt led to significantly higher data-quality scores compared to paper-based forms. MediCapt forms scored higher on 23 of 26 checklist items. While a wide difference in quality was seen among paper-based forms, MediCapt appeared to both standardize and improve quality of documentation across sites. MediCapt strengths included data security and confidentiality, accuracy and efficiency, and supplemental documentation with photography. Weaknesses included infrastructure challenges, required technological proficiencies, and time to learn the new system. Although it is early to assess the impact of MediCapt on prosecutions, providers and law and justice sector professionals were optimistic about its usefulness. They identified MediCapt as appropriate for use with survivors of sexual violence and reported MediCapt's legibility and photography features had already been commended by the court.

CONCLUSION

MediCapt was well-received across all sectors, its use was perceived as feasible and sustainable, and it significantly improved the quality of collected forensic data. It is anticipated this improvement in forensic documentation will increase successful prosecutions, strengthen accountability for perpetrators, and improve justice for survivors.

Emerging Trends in Smartphone Photo Documentation of Child Physical Abuse

ABSTRACT

Photo documentation of injuries on children is universally recommended in cases of suspected child physical abuse. As technology improves, the ability to document physical examination findings through smartphone photography is increasingly accessible and practical. The quality of images captured on smartphones now rivals traditional photography and the integration of photo capture within the electronic medical record has led to a variety of fields adopting smartphone photo documentation for diagnosis, consult, and follow-up. However, in cases of child physical abuse, practitioners have been hesitant to adopt smartphones as a primary means of photo documentation because of concerns around image quality, privacy, and security. In this article, we discuss the technology of available smartphone cameras and current evidence regarding their use for photo documentation, use existing guidelines to propose a workflow to improve the yield of smartphone photo documentation in child physical abuse, and discuss common medicolegal concerns.

DICODerma: A Practical Approach for Metadata Management of Images in Dermatology

Clinical images are vital for diagnosing and monitoring skin diseases, and their importance has increased with the growing popularity of machine learning. Lack of standards has stifled innovation in dermatological imaging, unlike other image-intensive specialties such as radiology. We investigate the meta-requirements for utilizing the popular DICOM standard for metadata management of images in dermatology. We propose practical design solutions and provide open-source tools to integrate dermatologists’ workflow with enterprise imaging systems. Using the tool, dermatologists can tag, search, organize and convert clinical images to the DICOM format. We believe that our less disruptive approach will improve the adoption of standards in the specialty.

Recommendations for Better Adoption of Medical Photography as a Clinical Tool: Viewpoint (Preprint)

The use of photography in routine clinical practice has the potential to increase the efficiency of overall patient care as well as improve clinical documentation and provider-to-provider communication. This is particularly important in the setting of provider burnout in the electronic health record era and during the COVID-19 pandemic. Despite the potential of photographs to enhance workflows and patient care, challenges remain that hinder the successful incorporation of medical photography into clinical practice, often because of inconsistent structure and implementation. Our proposed consolidated framework for clinical photography consists of five key aspects: appropriate informed consent; proper preparation and positioning; image acquisition with consideration of the field of view, orientation, focus, resolution, scale, and color calibration; streamlined and secure image storage and documentation; and interoperable file exchange. Overall, this viewpoint is a forward-looking paper on leveraging medical photography as an electronic health record tool for clinical care, research, and education.

Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker

The number of people suffering from chronic wounds is increasing due to demographic changes and the global epidemics of obesity and diabetes. Innovative imaging techniques within the field of chronic wound diagnostics are required to improve wound care by predicting and detecting wound infections to accelerate the application of treatments. For this reason, the infection probability index (IPI) is introduced as a novel infection marker based on thermal wound imaging. To improve usability, the IPI was implemented to automate scoring. Visual and thermal image pairs of 60 wounds were acquired to test the implemented algorithms on clinical data. The proposed process consists of (1) determining various parameters of the IPI based on medical hypotheses, (2) acquiring data, (3) extracting camera distortions using camera calibration, and (4) preprocessing and (5) automating segmentation of the wound to calculate (6) the IPI. Wound segmentation is reviewed by user input, whereas the segmented area can be refined manually. Furthermore, in addition to proof of concept, IPIs’ correlation with C-reactive protein (CRP) levels as a clinical infection marker was evaluated. Based on average CRP levels, the patients were clustered into two groups, on the basis of the separation value of an averaged CRP level of 100. We calculated the IPIs of the 60 wound images based on automated wound segmentation. Average runtime was less than a minute. In the group with lower average CRP, a correlation between IPI and CRP was evident.

Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First Results

Cancer is a very distressing disease, not only for the patients themselves, but also for their family members and relatives. Therefore, patients are regularly monitored to decide whether psychological treatment is necessary and applicable. However, such monitoring processes are costly in terms of required staff and time. Mobile data collection is an emerging trend in various domains. The medical and psychological field benefits from such an approach, which enables experts to quickly collect a large amount of individual health data. Mobile data collection applications enable a more holistic view of patients and assist psychologists in taking proper actions. We developed a mobile application, FeelBack, which is designed to support data collection that is based on well-known and approved psychological instruments. A controlled pilot evaluation with 60 participants provides insights into the feasibility of the developed platform and it shows the initial results. 31 of these participants received paper-based questionnaire and 29 followed the digital approach. The results reveal an increase of the overall acceptance by 58.5% in the mean when using a digital screening as compared to the paper-based. We believe that such a platform may significantly improve cancer patients’ and relatives’ psychological treatment, as available data can be used to optimize treatment.