Segmenting High-Need, High-Cost Veterans into Potentially Actionable Subgroups

将高需求、高成本的退伍军人细分为潜在可行的亚组

• 批准号: 10176376
• 负责人: Amol S Navathe
• 金额: --
• 依托单位: PHILADELPHIA VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10176376
• 关键词: Acute; Algorithms; Care given by nurses; Caring; Case Manager; Cessation of life; Characteristics; Chronic; Classification; Clinical; Clinical Data; Code; Collaborations; Data; Databases; Dementia; Development; Diagnosis; Disease; Disseminated Malignant Neoplasm; Elements; Emergency department visit; Evaluation; Event; Expert Opinion; Foundations; Future; Health; Health Care Costs; Healthcare; Heterogeneity; Hospitalization; Informatics; Infrastructure; Intervention; Laboratories; Life; Link; Machine Learning; Measures; Medical; Methods; Minority; Modeling; Natural Language Processing; Needs Assessment; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Pharmacy facility; Primary Health Care; Program Appropriateness; Research; Research Personnel; Resources; Risk; Services; Severities; Social Characteristics; Subgroup; System; Systems Development; Techniques; Testing; Time; Veterans; advanced disease; adverse outcome; base; clinical care; clinically actionable; complex chronic conditions; cost; data infrastructure; data warehouse; density; disability; disadvantaged population; economic impact; experience; frailty; health service use; high risk; hospital readmission; improved; improved outcome; interest; machine learning algorithm; mortality risk; programs; psychosocial; readmission rates; social factors; social health determinants; socioeconomic disadvantage; statistical and machine learning; success

SUMMARY ABSTRACT Background One compelling strategy for improving patient outcomes while reducing healthcare costs is to focus on veterans that account for the vast majority of poor outcomes, health utilization, and VA spending (i.e., HNHC veterans). However, successfully managing HNHC veterans is challenging because these patients are heterogeneous, each requiring a different management strategy. Veterans who intensely use services are of particular interest, especially those with chronic conditions since 20% of them will experience a hospitalization and readmission within 30 days after discharge. Hospitalization, emergency room visits, and re-hospitalization rates are even higher for socioeconomically disadvantaged populations, minorities, and veterans with disability. However, much of this utilization is preventable and could be averted with better longitudinal care. The VA has increased its efforts in identifying HNHC veterans through development of the Care Assessment Needs (CAN) score and care management programs, but without greater detail enabling tailoring of clinical programs HNHC veteran subgroups, linking these scores to strategies to improve care is difficult. Objectives The objectives of this study are to: (1) apply statistical and machine learning clustering methods to classify HNHC veterans into clinically actionable subgroups based on detailed clinical information extending beyond diagnosis codes, (2) compare the HNHC subgroups to veterans with similar diagnoses who were not HNHC, and (3) describe the characteristics of the HNHC subgroups (i.e., CAN Scores) and changes over time. Methods To achieve these objectives, we will analyze patient-level data from the National Patient Care Database (2013- 2015) using the VA Informatics and Computing Infrastructure (VINCI) platform to develop models that cluster HNHC veterans into subgroups based on demographic, clinical, and social characteristics. We will utilize a combination of statistical (latent class analysis) and machine learning clustering (e.g. k-means clustering) algorithms. Our definition of a HNHC veteran will comprise the highest quartiles of predicted risk of death or acute hospitalization (i.e., CAN score > 75). Subgroups and characteristics to compare HNHC and non-HNHC veterans will be constructed using 3 approaches: 1) cluster veterans who are HNHC in 2014, 2) cluster veterans who are HNHC in 2014 and 2015 (persistently HNHC), and 3) cluster all non-HNHC veterans into subgroups. Anticipated Impacts on Veterans Health Care This project aims to identify clinically actionable subgroups of high-need, high-cost (HNHC) veterans using data-driven techniques rather than expert opinion. We hypothesize that distinct clinical characteristics will define subgroups of HNHC veterans and that these subgroups of veterans likely require different management strategies. Thus, the categorization of HNHC veterans into discrete types of patients will support nurse care managers and primary care clinicians in their selection and delivery of appropriate programs to HNHC veterans and more broadly to help the VA to better identify gaps in its clinical and care management programs

摘要摘要 背景 提高患者预后的一种令人信服的策略，同时降低医疗保健成本是专注于 占绝大多数结果，健康利用和VA支出的退伍军人（即HNHC 退伍军人）。但是，成功管理HNHC退伍军人是具有挑战性的，因为这些患者是 异质，每个都需要不同的管理策略。强烈使用服务的退伍军人是 特别的兴趣，尤其是患有慢性病的人，因为其中20％会经历住院 并在出院后30天内再入院。住院，急诊室就诊和重新住院 社会经济处境不利的人群，少数民族和残疾退伍军人的率甚至更高。 但是，这种利用的大部分是可以预防的，并且可以通过更好的纵向护理来避免。 VA有 通过发展护理评估需求（CAN），增加了其在确定HNHC退伍军人方面的努力 得分和护理管理计划，但没有更大的详细信息，可以剪裁临床计划HNHC 资深亚组，将这些分数与改善护理的策略联系起来是困难的。 目标 这项研究的目标是：（1）应用统计和机器学习聚类方法来分类 HNHC退伍军人基于详细的临床信息扩展到临床可行的亚组 诊断代码，（2）将HNHC亚组与没有HNHC的类似诊断的退伍军人进行比较， （3）描述HNHC亚组的特征（即可以得分）和随着时间的变化。 方法 为了实现这些目标，我们将分析来自国家患者护理数据库的患者级数据（2013-- 2015）使用VA信息学和计算基础架构（VINCI）平台来开发聚类的模型 基于人口统计，临床和社会特征，HNHC退伍军人进入亚组。我们将利用一个 统计（潜在类别分析）和机器学习聚类（例如K-均值聚类）的组合 算法。我们对HNHC退伍军人的定义将包括预测死亡风险或 急性住院（即可以得分> 75）。比较HNHC和非HNHC的亚组和特征 退伍军人将使用3种方法来构建：1）2014年HNHC的集群退伍军人，2）集群 2014年和2015年HNHC的退伍军人（持续HNHC）和3）将所有非HNHC退伍军人聚集在 亚组。 对退伍军人卫生保健的预期影响 该项目旨在确定使用临床可行的高需求，高成本（HNHC）退伍军人的亚组 数据驱动的技术而不是专家意见。我们假设不同的临床特征将 定义HNHC退伍军人的亚组，这些退伍军人的这些亚组可能需要不同的管理 策略。因此，将HNHC退伍军人分类为离散类型的患者将支持护理护理 经理和初级保健临床医生在选择和提供适当的计划向HNHC退伍军人 更广泛地帮助VA更好地识别其临床和护理管理计划中的空白

项目成果

A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

• DOI: 10.1371/journal.pone.0247203
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Parikh RB;Linn KA;Yan J;Maciejewski ML;Rosland AM;Volpp KG;Groeneveld PW;Navathe AS
• 通讯作者: Navathe AS