这篇论文用CMDP模型和真实患者数据证明，个性化随访间隔比固定方案更省钱、更有效，高成本人群能省三成多。
该研究提出Contextual Markov Decision Process (CMDP)模型，基于22,154名2型糖尿病患者的电子健康记录（EHR）数据，优化不同亚群的随访间隔。通过主成分分析和聚类，识别出低风险和高风险两个亚群。模型建议：未测量实验室值时1个月内随访；指标升高或近期住院时最多3个月；血糖控制稳定时6至12个月，高风险患者间隔更短。与类似美国糖尿病协会的固定策略相比，高合并症亚群成本降低34.8%，低合并症亚群成本降低6.4%。
Context-Aware Optimization of Follow-Up Intervals for Type 2 Diabetes Care Using Markov Decision Processes
Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control. For Type 2 Diabetes (T2D), current guidelines prescribe fixed time intervals between subsequent primary care visits for all patients, overlooking heterogeneity in clinical trajectories and patient characteristics. This study introduces a Contextual Markov Decision Process (CMDP) model to optimize subpopulation-specific follow-up interval decisions using Electronic Health Record (EHR) data from 22,154 T2D patients across 10 primary care clinics. Contexts are identified by: i) dimensionality reduction of variables representing the individual health trajectories utilizing Principal Component Analysis, and ii) assigning patients to contexts via principal components and additional patient-level features using clustering. Two distinct contexts emerged, representing a lower- and a higher-risk subpopulation. CMDP-derived policies recommend: (i) follow-up within 1 month if lab value at current visit is unmeasured; (ii) up to 3 months for elevated lab values or recent hospitalizations; and (iii) 6 to 12 months for sustained glycemic control, with shorter follow-up intervals for patients in high-risk context. The optimal policies achieved lower expected cumulative cost than benchmarks (e.g., in the higher-comorbidity context, the CMDP policy reduced cost by about 34.8%, and in the lower-comorbidity context by about 6.4%, relative to an American Diabetes Association-like fixed interval follow-up policy. These findings demonstrate how context-aware approaches can inform adaptive follow-up strategies, and have the potential to advance chronic care management in primary care by synthesizing machine learning and probabilistic decision models.