Exploring patient-centered design solutions of a telehealth app for HIV - A qualitative study.

BACKGROUND: The Communication and Tracing App HIV (COMTRAC-HIV) project is developing a mobile health (mHealth) app for integrated care of HIV patients in Germany. The complexity of HIV treatment and continuous care necessitates the need for tailored mHealth solutions. This qualitative study explores design solutions and a prototype to enhance the app's functionality and effectiveness. METHODS: A total of eight HIV patients and pre-exposure prophylaxis (PrEP) users, recruited at the HIV Center Frankfurt, participated in focus groups and thinking-aloud tests (TA test). In the focus groups, design solutions were discussed for user-interface clarity, leading to the development of an interactive prototype, the usability of which was evaluated with a TA test. Data collection involved video/audio recordings. Qualitative analysis was conducted using a deductive category system, and focused on app design and usage in focus groups, and layout, navigation, interaction, terminology, comprehension, feedback, and level of satisfaction in TA tests. RESULTS: The app was commended for its simple, clear design, especially its medication reminders and health tracking features. Opinions on the symptom diary varied however, respondents noting it more suitable for HIV users than PrEP users. Privacy concerns suggest avoiding display of HIV-specific information. Suggested improvements include e.g. image uploads, drug interaction checks and prescription tracking. A total of 25 usability issues were identified in the TA test, with most found in the layout (n = 6), navigation (n = 5), interaction (n = 5), and terminology (n = 5) categories. Two examples are non-intuitive controls and illogical button placement. Despite these disadvantages, participants noted positive impressions (n = 5) in the satisfaction category. CONCLUSION: The study emphasizes the need for patient-centered design in mobile HIV care solutions, highlighting to the app's user-friendliness and potential to enhance care. Further research is necessary to refine the app's functionality and to align it with clinical and patients' privacy needs.

Editing Physicians' Responses Using GPT-4 for Academic Research.

The integration of Artificial Intelligence (AI) into digital healthcare, particularly in the anonymisation and processing of health information, holds considerable potential. OBJECTIVES: To develop a methodology using Generative Pre-trained Transformer (GPT) models to preserve the essence of medical advice in doctors' responses, while editing them for use in scientific studies. METHODS: German and English responses from EXABO, a rare respiratory disease platform, were processed using iterative refinement and other prompt engineering techniques, with a focus on removing identifiable and irrelevant content. RESULTS: Of 40 responses tested, 31 were accurately modified according to the developed guidelines. Challenges included misclassification and incomplete removal, with incremental prompting proving more accurate than combined prompting. CONCLUSION: GPT-4 models show promise in medical response editing, but face challenges in accuracy and consistency. Precision in prompt engineering is essential in medical contexts to minimise bias and retain relevant information.

Translation of Ontological Concepts from English into German Using Commercial Translation Software and Expert Evaluation.

Medical ontologies are mostly available in English. This presents a language barrier that is a limitation in research and automated processing of patient data. The manual translation of ontologies is complex and time-consuming. However, there are commercial translation tools that have shown promising results in the field of medical terminology translation. The aim of this study is to translate selected terms of the Human Phenotype Ontology (HPO) from English into German using commercial translators. Six medical experts evaluated the translation candidates in an iterative process. The results show commercial translators, with DeepL in the lead, provide translations that are positively evaluated by experts. With a broader study scope and additional optimization techniques, commercial translators could support and facilitate the process of translating medical ontologies.

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care.

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.