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Published on: April 19, 2019
Development and application of artificial intelligence follow-up system in patients with coronary heart disease
Liping Zhang1, Yanan Chen2, Qiao Yang1
1Nursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Insights
An AI-assisted follow-up platform for coronary heart disease (CHD) patients improved efficiency and patient satisfaction compared to manual methods. This intelligent system offers a scalable solution for chronic disease management, reducing healthcare staff workload.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Cardiovascular Disease Management
Background:
- Coronary heart disease (CHD) is a leading global cause of mortality, with significant challenges in post-discharge patient management.
- Inadequate follow-up care for CHD patients leads to poor adherence, low self-management, and increased readmissions.
- Traditional manual follow-up is inefficient, time-consuming, and lacks personalization for complex patient needs.
Purpose of the Study:
- To develop and evaluate an AI-assisted follow-up platform for CHD patients.
- To assess the platform's impact on follow-up efficiency, patient satisfaction, and healthcare staff workload.
- To establish a scalable model for intelligent chronic disease management.
Main Methods:
- Prospective cohort study involving 4000 CHD patients.
- Comparison between a manual follow-up group (n=2000) and an AI-assisted follow-up group (n=2000).
- Evaluation of key performance indicators: follow-up rates, feedback collection, patient satisfaction, and staff workload.
Main Results:
- AI-assisted follow-up significantly increased patient satisfaction (94.5% vs. 74.0%, P < 0.001).
- The AI platform reduced medical staff workload and saved an average of 13.2 hours per 100 patients.
- Follow-up and feedback collection rates were comparable between AI-assisted and manual groups.
Conclusions:
- AI-assisted follow-up platforms are effective and enhance efficiency and satisfaction for CHD patients and providers.
- Integration of patient profiling and IVR technologies enables personalized, dynamic, and cost-effective chronic disease management.
- AI-enabled systems show potential for optimizing chronic disease care and healthcare resource utilization.
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.
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