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Introduction: Mobile Health (mHealth) applications allow for new possibilities and opportunities in patient care. Their potential throughout the whole patient journey is undisputed. However, the eventual adoption by patients depends on their acceptance of and motivation to use mHealth applications as well as their adherence. Therefore, we investigated the motivation and drivers of acceptance for mHealth and developed an adapted model of the Unified Theory of Acceptance and Use of Technology (UTAUT2). Methods: We evaluated 215 patients with chronic gastroenterological diseases who answered a questionnaire including all model constructs with 7-point Likert scale items. Our model was adapted from the Unified Theory of Acceptance and Use in Technology 2 and includes influencing factors such as facilitating conditions, performance expectancy, hedonic motivation, social influence factors, effort expectancy, as well as personal empowerment and data protection concerns. Model evaluation was performed with structural equation modelling with PLS-SEM. Bootstrapping was performed for hypothesis testing. Results and Conclusion: Patients had a median age of 55.5 years, and the gender ratio was equally distributed. Forty percent received a degree from a university, college, technical academy, or engineering school. The majority of patients suffered from chronic liver disease, but patients with inflammatory bowel diseases, GI cancers, and pancreatic diseases were also included. Patients considered their general technology knowledge as medium to good or very good (78%). Actual usage of mHealth applications in general was rare, while the intention to use them was high. The leading acceptance factor for mHealth applications in our patient group was feasibility, both in terms of technical requirements and the intuitiveness and manageability of the application. Concerns about data privacy did not significantly impact the intention to use mobile devices. Neither the gamification aspect nor social influence factors played a significant role in the intention to use mHealth applications. Interpretation: Most of our patients were willing to spend time on a mHealth application specific to their disease on a regular basis. Acceptance and adherence are ensured by efficient utilization that requires minimum effort and compatible technologies as well as support in case of difficulties. Social influence and hedonic motivation, which were part of UTAUT2, as well as data security concerns, were not significantly influencing our patients' intention to use mHealth applications. A literature review revealed that drivers of acceptance vary considerably among different population and patient groups. Therefore, healthcare and mHealth providers should put effort into understanding their specific target groups' drivers of acceptance. We provided those for a cohort of patients from gastroenterology in this project.
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Background Procedural details of mechanical thrombectomy in patients with ischemic stroke are important predictors of clinical outcome and are collected for prospective studies or national stroke registries. To date, these data are collected manually by human readers, a labor-intensive task that is prone to errors. Purpose To evaluate the use of the large language models (LLMs) GPT-4 and GPT-3.5 to extract data from neuroradiology reports on mechanical thrombectomy in patients with ischemic stroke. Materials and Methods This retrospective study included consecutive reports from patients with ischemic stroke who underwent mechanical thrombectomy between November 2022 and September 2023 at institution 1 and between September 2016 and December 2019 at institution 2. A set of 20 reports was used to optimize the prompt, and the ability of the LLMs to extract procedural data from the reports was compared using the McNemar test. Data manually extracted by an interventional neuroradiologist served as the reference standard. Results A total of 100 internal reports from 100 patients (mean age, 74.7 years ± 13.2 [SD]; 53 female) and 30 external reports from 30 patients (mean age, 72.7 years ± 13.5; 18 male) were included. All reports were successfully processed by GPT-4 and GPT-3.5. Of 2800 data entries, 2631 (94.0% [95% CI: 93.0, 94.8]; range per category, 61%-100%) data points were correctly extracted by GPT-4 without the need for further postprocessing. With 1788 of 2800 correct data entries, GPT-3.5 produced fewer correct data entries than did GPT-4 (63.9% [95% CI: 62.0, 65.6]; range per category, 14%-99%; P < .001). For the external reports, GPT-4 extracted 760 of 840 (90.5% [95% CI: 88.3, 92.4]) correct data entries, while GPT-3.5 extracted 539 of 840 (64.2% [95% CI: 60.8, 67.4]; P < .001). Conclusion Compared with GPT-3.5, GPT-4 more frequently extracted correct procedural data from free-text reports on mechanical thrombectomy performed in patients with ischemic stroke. © RSNA, 2024 Supplemental material is available for this article.
