Smart Pharmaceutical Monitoring System With Personalized Medication Schedules and Self-Management Programs for Patients With Diabetes: Development and Evaluation Study
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| Publicado en: | Journal of Medical Internet Research vol. 27 (2025), p. e56737 |
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| Autor principal: | |
| Otros Autores: | , , , , , , , , |
| Publicado: |
Gunther Eysenbach MD MPH, Associate Professor
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3222368304 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1438-8871 | ||
| 024 | 7 | |a 10.2196/56737 |2 doi | |
| 035 | |a 3222368304 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Xiao, Jian | |
| 245 | 1 | |a Smart Pharmaceutical Monitoring System With Personalized Medication Schedules and Self-Management Programs for Patients With Diabetes: Development and Evaluation Study | |
| 260 | |b Gunther Eysenbach MD MPH, Associate Professor |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background:With the climbing incidence of type 2 diabetes, the health care system is under pressure to manage patients with this condition properly. Particularly, pharmacological therapy constitutes the most fundamental means of controlling blood glucose levels and preventing the progression of complications. However, its effectiveness is often hindered by factors such as treatment complexity, polypharmacy, and poor patient adherence. As new technologies, artificial intelligence and digital technologies are covering all aspects of the medical and health care field, but their application and evaluation in the domain of diabetes research remain limited.Objective:This study aims to develop and establish a stand-alone diabetes management service system designed to enhance self-management support for patients, as well as to assess its performance with experienced health care professionals.Methods:Diabetes Universal Medication Schedule (DUMS) system is grounded in official medicine instructions and evidence-based data to establish medication constraints and drug-drug interaction profiles. Individualized medication schedules and self-management programs were generated based on patient-specific conditions and needs, using an app framework to build patient-side contact pathways. The system’s ability to provide medication guidance and health management was assessed by senior health care professionals using a 5-point Likert scale across 3 groups: outputs generated by the system (DUMS group), outputs refined by pharmacists (intervention group), and outputs generated by ChatGPT-4 (GPT-4 group).Results:We constructed a cloud-based drug information management system loaded with 475 diabetes treatment–related medications; 684 medication constraints; and 12,351 drug-drug interactions and theoretical supports. The generated personalized medication plan and self-management program included recommended dosing times, disease education, dietary considerations, and lifestyle recommendations to help patients with diabetes achieve correct medication use and active disease management. Reliability analysis demonstrated that the DUMS group outperformed the GPT-4 group in medication schedule accuracy and safety, as well as comprehensiveness and richness of the self-management program (P<.001). The intervention group outperformed the DUMS and GPT-4 groups on all indicator scores.Conclusions:DUMS’s treatment monitoring service can provide reliable self-management support for patients with diabetes. ChatGPT-4, powered by artificial intelligence, can act as a collaborative assistant to health care professionals in clinical contexts, although its performance still requires further training and optimization. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Language | ||
| 653 | |a Intervention | ||
| 653 | |a Diabetes | ||
| 653 | |a Accuracy | ||
| 653 | |a Drug interactions | ||
| 653 | |a Medical personnel | ||
| 653 | |a Selfmanagement | ||
| 653 | |a Chronic illnesses | ||
| 653 | |a Type 2 diabetes mellitus | ||
| 653 | |a Disease management | ||
| 653 | |a Glucose | ||
| 653 | |a Prescription drugs | ||
| 653 | |a Professional ethics | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Chatbots | ||
| 653 | |a Schedules | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Evidence-based medicine | ||
| 653 | |a Patients | ||
| 653 | |a Health care | ||
| 653 | |a Medicine | ||
| 653 | |a Professional training | ||
| 653 | |a Pharmaceuticals | ||
| 653 | |a Climbing | ||
| 653 | |a Optimization | ||
| 653 | |a Reliability | ||
| 653 | |a Professionals | ||
| 653 | |a Large language models | ||
| 653 | |a Pharmacists | ||
| 653 | |a Information management | ||
| 653 | |a Education | ||
| 653 | |a Adults | ||
| 653 | |a Customization | ||
| 653 | |a Interfaces | ||
| 653 | |a Dosage | ||
| 653 | |a Self evaluation | ||
| 653 | |a Human-computer interaction | ||
| 653 | |a Treatment outcomes | ||
| 653 | |a Drugs | ||
| 653 | |a Treatment compliance | ||
| 653 | |a Health services | ||
| 653 | |a Academic achievement | ||
| 653 | |a Disease | ||
| 653 | |a Medical treatment | ||
| 653 | |a Constraints | ||
| 653 | |a Groups | ||
| 700 | 1 | |a Li, Mengyao | |
| 700 | 1 | |a Cai, Ruwen | |
| 700 | 1 | |a Huang, Hangxing | |
| 700 | 1 | |a Yu, Huimin | |
| 700 | 1 | |a Huang, Ling | |
| 700 | 1 | |a Li, Jingyang | |
| 700 | 1 | |a Yu, Ting | |
| 700 | 1 | |a Zhang, Jiani | |
| 700 | 1 | |a Cheng, Shuqiao | |
| 773 | 0 | |t Journal of Medical Internet Research |g vol. 27 (2025), p. e56737 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3222368304/abstract/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3222368304/fulltextwithgraphics/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3222368304/fulltextPDF/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |