Examining chronic kidney disease screening frequency among diabetics: a POMDP approach.

IF 2.3 3区医学 Q2 HEALTH POLICY & SERVICES Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-06-05 DOI:10.1007/s10729-024-09677-4

Chou-Chun Wu, Yiwen Cao, Sze-Chuan Suen, Eugene Lin

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Abstract

Forty percent of diabetics will develop chronic kidney disease (CKD) in their lifetimes. However, as many as 50% of these CKD cases may go undiagnosed. We developed screening recommendations stratified by age and previous test history for individuals with diagnosed diabetes and unknown proteinuria status by race and gender groups. To do this, we used a Partially Observed Markov Decision Process (POMDP) to identify whether a patient should be screened at every three-month interval from ages 30-85. Model inputs were drawn from nationally-representative datasets, the medical literature, and a microsimulation that integrates this information into group-specific disease progression rates. We implement the POMDP solution policy in the microsimulation to understand how this policy may impact health outcomes and generate an easily-implementable, non-belief-based approximate policy for easier clinical interpretability. We found that the status quo policy, which is to screen annually for all ages and races, is suboptimal for maximizing expected discounted future net monetary benefits (NMB). The POMDP policy suggests more frequent screening after age 40 in all race and gender groups, with screenings 2-4 times a year for ages 61-70. Black individuals are recommended for screening more frequently than their White counterparts. This policy would increase NMB from the status quo policy between $1,000 to $8,000 per diabetic patient at a willingness-to-pay of $150,000 per quality-adjusted life year (QALY).

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研究糖尿病患者的慢性肾病筛查频率：一种 POMDP 方法。

40%的糖尿病患者一生中会患上慢性肾病（CKD）。然而，多达 50% 的 CKD 病例可能未得到诊断。我们针对已确诊的糖尿病患者和蛋白尿状况不明的患者，按年龄和既往检查史，分种族和性别组别制定了筛查建议。为此，我们使用了部分观测马尔可夫决策过程（POMDP）来确定患者是否应在 30-85 岁之间每三个月进行一次筛查。模型输入来自具有全国代表性的数据集、医学文献以及将这些信息整合到特定群体疾病进展率中的微观模拟。我们在微观模拟中实施了 POMDP 解决方案政策，以了解该政策如何影响健康结果，并生成了一个易于实施的、非基于信念的近似政策，以方便临床解释。我们发现，维持现状的政策，即每年对所有年龄段和种族的人群进行筛查，对于最大化预期贴现未来净货币收益（NMB）而言是次优的。POMDP 政策建议，在所有种族和性别群体中，40 岁后筛查频率更高，61-70 岁每年筛查 2-4 次。建议黑人比白人更频繁地接受筛查。该政策将使每名糖尿病患者的 NMB 从现状政策的 1,000 美元增加到 8,000 美元，每质量调整生命年（QALY）的支付意愿为 150,000 美元。

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来源期刊

Health Care Management Science HEALTH POLICY & SERVICES-

CiteScore

7.20

自引率

5.60%

发文量

期刊介绍： Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged. Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate. Editorial statements for the individual departments are provided below. Health Care Analytics Departmental Editors: Margrét Bjarnadóttir, University of Maryland Nan Kong, Purdue University With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes. The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics. Health Care Operations Management Departmental Editors: Nilay Tanik Argon, University of North Carolina at Chapel Hill Bob Batt, University of Wisconsin The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society. Health Care Management Science Practice Departmental Editor: Vikram Tiwari, Vanderbilt University Medical Center The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful. Health Care Productivity Analysis Departmental Editor: Jonas Schreyögg, University of Hamburg The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity. Public Health Policy and Medical Decision Making Departmental Editors: Ebru Bish, University of Alabama Julie L. Higle, University of Southern California The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems. The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that: Study high-impact problems involving health policy, treatment planning and design, and clinical applications; Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines; Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations. Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies. Emerging Topics Departmental Editor: Alec Morton, University of Strathclyde Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.