{"title":"基于医疗保健轨迹的人口分层：在中观层面鼓励自适应学习的方法。","authors":"","doi":"10.1016/j.healthpol.2024.105137","DOIUrl":null,"url":null,"abstract":"<div><p>This paper proposes a method to support population management by evaluating population needs using population stratification based on healthcare trajectories.</p><p>Reimbursed healthcare consumption data for the first semester of 2017 contained within the inter-mutualist database were analysed to create healthcare trajectories for a subset of the population aged between 60 and 79 (N = 22,832) to identify (1) the nature of health events, (2) key transitions between lines of care, (3) the relative duration of different events, and (4) the hierarchy of events. These factors were classified using a K-mers approach followed by multinomial mixture modelling.</p><p>Five population groups were identified using this healthcare trajectory approach: “low users”, “high intensity of nursing care”, “transitional care & nursing care”, “transitional care”, and “long time in hospital”.</p><p>This method could be used by loco-regional governing bodies to learn reflectively from the place where care is provided, taking a systems perspective rather than a disease perspective, and avoiding the one-size-fits-all definition. It invites decision makers to make better use of routinely collected data to guide continuous learning and adaptive management of population health needs.</p></div>","PeriodicalId":55067,"journal":{"name":"Health Policy","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Population stratification based on healthcare trajectories: A method for encouraging adaptive learning at meso level\",\"authors\":\"\",\"doi\":\"10.1016/j.healthpol.2024.105137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper proposes a method to support population management by evaluating population needs using population stratification based on healthcare trajectories.</p><p>Reimbursed healthcare consumption data for the first semester of 2017 contained within the inter-mutualist database were analysed to create healthcare trajectories for a subset of the population aged between 60 and 79 (N = 22,832) to identify (1) the nature of health events, (2) key transitions between lines of care, (3) the relative duration of different events, and (4) the hierarchy of events. These factors were classified using a K-mers approach followed by multinomial mixture modelling.</p><p>Five population groups were identified using this healthcare trajectory approach: “low users”, “high intensity of nursing care”, “transitional care & nursing care”, “transitional care”, and “long time in hospital”.</p><p>This method could be used by loco-regional governing bodies to learn reflectively from the place where care is provided, taking a systems perspective rather than a disease perspective, and avoiding the one-size-fits-all definition. It invites decision makers to make better use of routinely collected data to guide continuous learning and adaptive management of population health needs.</p></div>\",\"PeriodicalId\":55067,\"journal\":{\"name\":\"Health Policy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Policy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168851024001477\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Policy","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168851024001477","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Population stratification based on healthcare trajectories: A method for encouraging adaptive learning at meso level
This paper proposes a method to support population management by evaluating population needs using population stratification based on healthcare trajectories.
Reimbursed healthcare consumption data for the first semester of 2017 contained within the inter-mutualist database were analysed to create healthcare trajectories for a subset of the population aged between 60 and 79 (N = 22,832) to identify (1) the nature of health events, (2) key transitions between lines of care, (3) the relative duration of different events, and (4) the hierarchy of events. These factors were classified using a K-mers approach followed by multinomial mixture modelling.
Five population groups were identified using this healthcare trajectory approach: “low users”, “high intensity of nursing care”, “transitional care & nursing care”, “transitional care”, and “long time in hospital”.
This method could be used by loco-regional governing bodies to learn reflectively from the place where care is provided, taking a systems perspective rather than a disease perspective, and avoiding the one-size-fits-all definition. It invites decision makers to make better use of routinely collected data to guide continuous learning and adaptive management of population health needs.
期刊介绍：
Health Policy is intended to be a vehicle for the exploration and discussion of health policy and health system issues and is aimed in particular at enhancing communication between health policy and system researchers, legislators, decision-makers and professionals concerned with developing, implementing, and analysing health policy, health systems and health care reforms, primarily in high-income countries outside the U.S.A.
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