Roorda Els, Bruijnzeels Marc, Struijs Jeroen, Spruit Marco
Department of Public Health and Primary Care (PHEG), Leiden University Medical Center (LUMC), The Hague, 2511 DP, The Netherlands.
Department of Quality of Care and Health Economics, Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, 3721 MA, The Netherlands.
JAMIA Open. 2024 Nov 27;7(4):ooae122. doi: 10.1093/jamiaopen/ooae122. eCollection 2024 Dec.
Population health management (PHM) is a promising data-driven approach to address the challenges faced by health care systems worldwide. Although Business Intelligence (BI) systems are known to be relevant for a data-driven approach, the usage for PHM is limited in its elaboration. To explore available scientific publications, a systematic review guided by PRISMA was conducted of mature BI initiatives to investigate their decision contexts and BI capabilities.
PubMed, Embase, and Web of Science were searched for articles published from January 2012 through November 2023. Articles were included if they described a (potential) BI system for PHM goals. Additional relevant publications were identified through snowballing. Technological Readiness Levels were evaluated to select mature initiatives from the 29 initiatives found. From the 11 most mature systems the decision context (eg, patient identification, risk stratification) and BI capabilities (eg, data warehouse, linked biobank) were extracted.
The initiatives found are highly fragmented in decision context and BI capabilities. Varied terminology is used and much information is missing. Impact on population's health is currently limited for most initiatives. Care Link, CommunityRx, and Gesundes Kinzigtal currently stand out in aligning BI capabilities with their decision contexts.
PHM is a data-driven approach that requires a coherent data strategy and understanding of decision contexts and user needs. Effective BI capabilities depend on this understanding. Designing public-private partnerships to protect intellectual property while enabling rapid knowledge development is crucial. Development of a framework is proposed for systematic knowledge building.
人群健康管理（PHM）是一种很有前景的数据驱动方法，旨在应对全球医疗保健系统面临的挑战。尽管商业智能（BI）系统与数据驱动方法相关，但在人群健康管理中的应用阐述有限。为了探索现有的科学出版物，我们按照PRISMA指南对成熟的商业智能计划进行了系统综述，以调查其决策背景和商业智能能力。
在PubMed、Embase和科学网中检索2012年1月至2023年11月发表的文章。如果文章描述了用于人群健康管理目标的（潜在）商业智能系统，则将其纳入。通过滚雪球的方式识别其他相关出版物。评估技术就绪水平，从找到的29个计划中选择成熟的计划。从11个最成熟的系统中提取决策背景（如患者识别、风险分层）和商业智能能力（如数据仓库、关联生物样本库）。
所发现的计划在决策背景和商业智能能力方面高度分散。使用的术语各不相同，且缺少很多信息。目前，大多数计划对人群健康的影响有限。Care Link、CommunityRx和Gesundes Kinzigtal目前在使商业智能能力与其决策背景相匹配方面表现突出。
人群健康管理是一种数据驱动的方法，需要连贯的数据策略以及对决策背景和用户需求的理解。有效的商业智能能力取决于这种理解。设计公私合作伙伴关系以保护知识产权同时促进快速知识发展至关重要。建议开发一个框架用于系统的知识构建。
