Objectives Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI.

Methods Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.

Results The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah.

Conclusion A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

目标 人工智能 (AI)，包括预测分析，在改善高发病率和高死亡率的常见慢性病的护理方面具有巨大潜力。然而，要实现这一愿景，仍然存在许多挑战。该项目的目标是开发和应用使用人工智能加强慢性病护理的方法。

方法 使用 27,904 名糖尿病患者的数据集，开发并验证了一种分析方法，用于生成治疗路径图，该图由预测替代治疗策略实现护理目标的可能性的模型组成。通过将预测模型封装在 OpenCDS Web 服务模块中并通过 FHIR（可替代医疗应用和可重用技术）上的 SMART 提供模型输出，开发了与电子健康记录 (EHR) 集成的 AI 驱动的临床决策支持系统 (CDSS)在快速医疗互操作性资源上）基于 Web 的仪表板。该 CDSS 使临床医生和患者能够查看相关的患者参数、选择治疗目标并根据预测结果查看替代治疗策略。

结果 所提出的分析方法在预测精度方面优于以前的机器学习算法。CDSS 与犹他大学的 Epic EHR 成功集成。

结论 开发了基于预测分析的 CDSS，并通过基于标准的互操作性框架与 EHR 成功集成。所使用的方法可能适用于许多其他慢性病，将 AI 驱动的 CDSS 带到护理点。