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全生命周期发病模式:一个用于分类各年龄段医疗保健需求的人群细分框架。

Patterns of Morbidity Across the Lifespan: A Population Segmentation Framework for Classifying Health Care Needs for All Ages.

作者信息

Lemke Klaus W, Forrest Christopher B, Leff Bruce A, Boyd Cynthia M, Gudzune Kimberly A, Pollack Craig E, Pandya Chintan J, Weiner Jonathan P

机构信息

Center for Population Health Informatics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

出版信息

Med Care. 2024 Nov 1;62(11):732-740. doi: 10.1097/MLR.0000000000001898. Epub 2023 Nov 7.

Abstract

BACKGROUND

Classification systems to segment such patients into subgroups for purposes of care management and population analytics should balance administrative simplicity with clinical meaning and measurement precision.

OBJECTIVE

To describe and empirically apply a new clinically relevant population segmentation framework applicable to all payers and all ages across the lifespan.

RESEARCH DESIGN AND SUBJECTS

Cross-sectional analyses using insurance claims database for 3.31 Million commercially insured and 1.05 Million Medicaid enrollees under 65 years old; and 5.27 Million Medicare fee-for-service beneficiaries aged 65 and older.

MEASURES

The "Patient Need Groups" (PNGs) framework, we developed, classifies each person within the entire 0-100+ aged population into one of 11 mutually exclusive need-based categories. For each PNG segment, we documented a range of clinical and resource endpoints, including health care resource use, avoidable emergency department visits, hospitalizations, behavioral health conditions, and social need factors.

RESULTS

The PNG categories included: (1) nonuser; (2) low-need child; (3) low-need adult; (4) low-complexity multimorbidity; (5) medium-complexity multimorbidity; (6) low-complexity pregnancy; (7) high-complexity pregnancy; (8) dominant psychiatric/behavioral condition; (9) dominant major chronic condition; (10) high-complexity multimorbidity; and (11) frailty. Each PNG evidenced a characteristic age-related trajectory across the full lifespan. In addition to offering clinically cogent groupings, large percentages (29%-62%) of patients in two pregnancy and high-complexity multimorbidity and frailty PNGs were in a high-risk subgroup (upper 10%) of potential future health care utilization.

CONCLUSIONS

The PNG population segmentation approach represents a comprehensive measurement framework that captures and categorizes available electronic health care data to characterize individuals of all ages based on their needs.

摘要

背景

为了进行护理管理和人群分析,将此类患者分为不同亚组的分类系统应在管理简便性与临床意义和测量精度之间取得平衡。

目的

描述并实证应用一种新的适用于所有支付方和全生命周期各年龄段的具有临床相关性的人群细分框架。

研究设计与对象

使用保险理赔数据库进行横断面分析,涉及331万商业保险参保者和105万65岁以下的医疗补助参保者;以及527万65岁及以上的医疗保险按服务付费受益人群。

测量指标

我们开发的“患者需求组”(PNG)框架将整个年龄范围从0至100多岁的人群中的每个人分为11个基于需求的相互排斥的类别之一。对于每个PNG细分群体,我们记录了一系列临床和资源终点指标,包括医疗资源使用情况、可避免的急诊科就诊次数、住院情况、行为健康状况和社会需求因素。

结果

PNG类别包括:(1)非使用者;(2)低需求儿童;(3)低需求成人;(4)低复杂性多种慢性病;(5)中等复杂性多种慢性病;(6)低复杂性妊娠;(7)高复杂性妊娠;(8)主要精神/行为疾病;(9)主要重大慢性病;(10)高复杂性多种慢性病;以及(11)虚弱。每个PNG在整个生命周期中都呈现出与年龄相关的特征轨迹。除了提供具有临床说服力的分组外,两个妊娠组、高复杂性多种慢性病组和虚弱PNG组中很大比例(29%-62%)的患者属于未来潜在医疗保健利用的高风险亚组(前10%)。

结论

PNG人群细分方法代表了一种全面的测量框架,该框架捕获并分类可用的电子医疗保健数据,以根据需求对各年龄段的个体进行特征描述。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/313b/11462874/46bbae50ecee/mlr-62-732-g001.jpg

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