Business intelligence systems for population health management: a scoping review.

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
JAMIA Open Pub Date : 2024-11-27 eCollection Date: 2024-12-01 DOI:10.1093/jamiaopen/ooae122
Els Roorda, Marc Bruijnzeels, Jeroen Struijs, Marco Spruit
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引用次数: 0

Abstract

Objective: 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.

Materials and methods: 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.

Results: 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.

Discussion and conclusion: 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.

用于人口健康管理的商业智能系统:范围审查。
目的:人口健康管理(PHM)是一种有前途的数据驱动方法,可用于应对全球医疗保健系统面临的挑战。虽然众所周知商业智能(BI)系统与数据驱动方法相关,但其在人口健康管理方面的应用却很有限。为了探索现有的科学出版物,我们在 PRISMA 的指导下对成熟的商业智能计划进行了系统性回顾,以调查其决策背景和商业智能能力:在 PubMed、Embase 和 Web of Science 上搜索了 2012 年 1 月至 2023 年 11 月期间发表的文章。如果文章描述了针对公共健康管理目标的(潜在)商业智能系统,则会被收录。此外,还通过 "滚雪球 "的方式确定了其他相关出版物。对技术就绪程度进行评估,以便从找到的 29 项计划中选择成熟的计划。从 11 个最成熟的系统中提取了决策背景(如患者识别、风险分层)和商业智能功能(如数据仓库、链接生物库):结果:所发现的倡议在决策背景和商业智能能力方面非常分散。所使用的术语多种多样,许多信息缺失。大多数计划目前对人口健康的影响有限。目前,Care Link、CommunityRx 和 Gesundes Kinzigtal 在将商业智能能力与其决策背景相结合方面表现突出:PHM 是一种数据驱动的方法,需要协调一致的数据战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,同时促进知识的快速发展至关重要。建议为系统性知识建设制定一个框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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