%0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e71664 %T Correction: Evaluating Bard Gemini Pro and GPT-4 Vision Against Student Performance in Medical Visual Question Answering: Comparative Case Study %A Roos,Jonas %A Martin,Ron %A Kaczmarczyk,Robert %D 2025 %7 11.2.2025 %9 %J JMIR Form Res %G English %X %R 10.2196/71664 %U https://formative.jmir.org/2025/1/e71664 %U https://doi.org/10.2196/71664 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 12 %N %P e60160 %T Following Up Patients With Chronic Pain Using a Mobile App With a Support Center: Unicenter Prospective Study %A Gómez-González,Marta Antonia %A Cordero Tous,Nicolas %A De la Cruz Sabido,Javier %A Sánchez Corral,Carlos %A Lechuga Carrasco,Beatriz %A López-Vicente,Marta %A Olivares Granados,Gonzalo %K pain management %K mobile health %K mHealth %K eHealth %K chronic pain %K support center %K mobile phone app %K survey %K follow-up %K pain control %K prospective study %D 2025 %7 22.1.2025 %9 %J JMIR Hum Factors %G English %X Background: Chronic pain is among the most common conditions worldwide and requires a multidisciplinary treatment approach. Spinal cord stimulation is a possible treatment option for pain management; however, patients undergoing this intervention require close follow-up, which is not always feasible. eHealth apps offer opportunities for improved patient follow-up, although adherence to these apps tends to decrease over time, with rates dropping to approximately 60%. To improve adherence to remote follow-up, we developed a remote follow-up system consisting of a mobile app for patients, a website for health care professionals, and a remote support center. Objective: Our objective was to evaluate patient adherence to remote follow-up using a system that includes a mobile app and a remote support center. Methods: After review of the literature and approval of the design of the follow-up system by a multidisciplinary committee, a team of experts developed a system based on a mobile app, a website for health care professionals, and a remote support center. The system was developed in collaboration with health care professionals and uses validated scales to capture patients’ clinical data at each stage of treatment (ie, pretreatment phase, trial phase, and implantation phase). Data were collected prospectively between January 2020 to August 2023, including the number of total surveys sent, surveys completed, SMS text message reminders sent, and reminder calls made. Results: A total of 64 patients were included (n=40 women, 62.5%) in the study. By the end of the study, 19 (29.7%) patients remained in the pretreatment phase, 8 (12.5%) patients had completed the trial phase, and 37 (57.8%) reached the implantation phase. The mean follow-up period was 15.30 (SD 9.43) months. A total of 1574 surveys were sent, along with 488 SMS text message reminders and 53 reminder calls. The mean adherence rate decreased from 94.53% (SD 20.63%) during the pretreatment phase to 65.68% (SD 23.49%) in the implantation phase, with an overall mean adherence rate of 87.37% (SD 15.37%) for the app. ANOVA showed that adherence was significantly higher in the earlier phases of treatment (P<.001). Conclusions: Our remote follow-up system, supported by a remote support center improves adherence to follow-up in later phases of treatment, although adherence decreased over time. Further studies are needed to investigate the relationship between adherence to the app and pain management. %R 10.2196/60160 %U https://humanfactors.jmir.org/2025/1/e60160 %U https://doi.org/10.2196/60160 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55779 %T Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care %A Tsafnat,Guy %A Dunscombe,Rachel %A Gabriel,Davera %A Grieve,Grahame %A Reich,Christian %+ Evidentli Pty Ltd, 50 Holt St, Suite 516, Surry Hills, 2010, Australia, 61 415481043, guyt@evidentli.com %K interoperability %K clinical data %K open data standards %K health care %K digital health %K health care data %D 2024 %7 9.4.2024 %9 Editorial %J J Med Internet Res %G English %X Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain. %M 38593431 %R 10.2196/55779 %U https://www.jmir.org/2024/1/e55779 %U https://doi.org/10.2196/55779 %U http://www.ncbi.nlm.nih.gov/pubmed/38593431