Development and application of artificial intelligence follow-up system in patients with coronary heart disease
Background Coronary heart disease (CHD) is a leading cause of mortality globally and poses significant public health challenges, particularly in China. Despite advancements in medical treatments, postdischarge management of patients with CHD remains inadequate, often resulting in poor medication adh...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251360858 |
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| author | Liping Zhang Yanan Chen Qiao Yang Lijuan Lu yishuang Cui Miao Zhou Xiaolu Jin Shiyi Zhang Xifei He |
| author_facet | Liping Zhang Yanan Chen Qiao Yang Lijuan Lu yishuang Cui Miao Zhou Xiaolu Jin Shiyi Zhang Xifei He |
| author_sort | Liping Zhang |
| collection | DOAJ |
| description | Background Coronary heart disease (CHD) is a leading cause of mortality globally and poses significant public health challenges, particularly in China. Despite advancements in medical treatments, postdischarge management of patients with CHD remains inadequate, often resulting in poor medication adherence, low self-management, and increased readmission risks. Traditional manual follow-up methods are time-intensive, inefficient, and lack personalization, limiting their ability to address patients’ complex healthcare needs. Objective This study aimed to develop and evaluate an AI-assisted follow-up platform incorporating patient profiling and interactive voice response (IVR) technologies. The platform seeks to improve follow-up efficiency, enhance patient satisfaction, reduce the workload of medical staff, and provide a scalable model for intelligent follow up in clinical settings. Methods This study utilized a prospective cohort design. A total of 4000 patients with CHD discharged from Tongji Hospital were enrolled, with 2000 patients assigned to a manual follow-up group from September 2022 to August 2023 and 2000 from September 2023 to August 2024 to an AI-assisted follow-up group. Key performance indicators, including follow-up rates, feedback collection rates, patient satisfaction, and medical staff workload, were compared between the two groups. Results The follow-up rates were similar between the manual group (82.0%) and the AI-assisted group (81.3%) ( P = 0.567).The feedback collection rates did not significantly differ between the two groups (7.2% vs. 6.1%, P = 0.163). However, the AI-assisted follow up significantly reduced time expenditure, saving an average of 13.2 hours per 100 patients compared to manual follow up. Patient satisfaction was significantly higher in the AI-assisted group than in the manual group (94.5% vs. 74.0%, P < 0.001). Binary logistic regression analysis identified follow-up method as the significant predictor of patient satisfaction (odds ratio (OR) = 5.993, 95% confidence interval (CI): 4.829–7.438, P < 0.001). In addition, the AI-assisted platform significantly reduced the workload of medical staff ( P < 0.01). Conclusions The AI-assisted follow-up platform demonstrated comparable effectiveness to manual methods while significantly improving efficiency and satisfaction for both patients and healthcare providers. By integrating patient profiling and IVR technologies, the platform offers personalized, dynamic, and cost-effective follow-up services. These findings highlight the potential of AI-enabled systems in optimizing chronic disease management and resource utilization. |
| format | Article |
| id | doaj-art-bcf26992554c4a889a0f9fb2efcfad26 |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-bcf26992554c4a889a0f9fb2efcfad262025-08-20T03:30:15ZengSAGE PublishingDigital Health2055-20762025-07-011110.1177/20552076251360858Development and application of artificial intelligence follow-up system in patients with coronary heart diseaseLiping Zhang0Yanan Chen1Qiao Yang2Lijuan Lu3yishuang Cui4Miao Zhou5Xiaolu Jin6Shiyi Zhang7Xifei He8 Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaBackground Coronary heart disease (CHD) is a leading cause of mortality globally and poses significant public health challenges, particularly in China. Despite advancements in medical treatments, postdischarge management of patients with CHD remains inadequate, often resulting in poor medication adherence, low self-management, and increased readmission risks. Traditional manual follow-up methods are time-intensive, inefficient, and lack personalization, limiting their ability to address patients’ complex healthcare needs. Objective This study aimed to develop and evaluate an AI-assisted follow-up platform incorporating patient profiling and interactive voice response (IVR) technologies. The platform seeks to improve follow-up efficiency, enhance patient satisfaction, reduce the workload of medical staff, and provide a scalable model for intelligent follow up in clinical settings. Methods This study utilized a prospective cohort design. A total of 4000 patients with CHD discharged from Tongji Hospital were enrolled, with 2000 patients assigned to a manual follow-up group from September 2022 to August 2023 and 2000 from September 2023 to August 2024 to an AI-assisted follow-up group. Key performance indicators, including follow-up rates, feedback collection rates, patient satisfaction, and medical staff workload, were compared between the two groups. Results The follow-up rates were similar between the manual group (82.0%) and the AI-assisted group (81.3%) ( P = 0.567).The feedback collection rates did not significantly differ between the two groups (7.2% vs. 6.1%, P = 0.163). However, the AI-assisted follow up significantly reduced time expenditure, saving an average of 13.2 hours per 100 patients compared to manual follow up. Patient satisfaction was significantly higher in the AI-assisted group than in the manual group (94.5% vs. 74.0%, P < 0.001). Binary logistic regression analysis identified follow-up method as the significant predictor of patient satisfaction (odds ratio (OR) = 5.993, 95% confidence interval (CI): 4.829–7.438, P < 0.001). In addition, the AI-assisted platform significantly reduced the workload of medical staff ( P < 0.01). Conclusions The AI-assisted follow-up platform demonstrated comparable effectiveness to manual methods while significantly improving efficiency and satisfaction for both patients and healthcare providers. By integrating patient profiling and IVR technologies, the platform offers personalized, dynamic, and cost-effective follow-up services. These findings highlight the potential of AI-enabled systems in optimizing chronic disease management and resource utilization.https://doi.org/10.1177/20552076251360858 |
| spellingShingle | Liping Zhang Yanan Chen Qiao Yang Lijuan Lu yishuang Cui Miao Zhou Xiaolu Jin Shiyi Zhang Xifei He Development and application of artificial intelligence follow-up system in patients with coronary heart disease Digital Health |
| title | Development and application of artificial intelligence follow-up system in patients with coronary heart disease |
| title_full | Development and application of artificial intelligence follow-up system in patients with coronary heart disease |
| title_fullStr | Development and application of artificial intelligence follow-up system in patients with coronary heart disease |
| title_full_unstemmed | Development and application of artificial intelligence follow-up system in patients with coronary heart disease |
| title_short | Development and application of artificial intelligence follow-up system in patients with coronary heart disease |
| title_sort | development and application of artificial intelligence follow up system in patients with coronary heart disease |
| url | https://doi.org/10.1177/20552076251360858 |
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