An Information Quality Framework for Managed Health Care.

data governance data management data quality information quality information quality framework managed health care managed healthcare

Journal

Journal of healthcare leadership
ISSN: 1179-3201
Titre abrégé: J Healthc Leadersh
Pays: New Zealand
ID NLM: 101614314

Informations de publication

Date de publication:
2024
Historique:
received: 19 06 2024
accepted: 03 09 2024
medline: 3 10 2024
pubmed: 3 10 2024
entrez: 3 10 2024
Statut: epublish

Résumé

Data and information quality play a critical role in the managed healthcare sector, where accurate and reliable information is crucial for optimal decision-making, operations, and patient outcomes. However, managed care organizations face significant challenges in ensuring information quality due to the complexity of data sources, regulatory requirements, and the need for effective data management practices. The goal of this article is to develop and justify an information quality framework for managed healthcare, thereby enabling the sector to better meet its unique information quality challenges. The information quality framework provided here was designed using other information quality frameworks as exemplars, as well as a qualitative survey involving interviews of twenty industry leaders structured around 17 questions. The responses were analyzed and tabulated to obtain insights into the information quality needs of the managed healthcare domain. The novel framework we present herein encompasses strategies for data integration, standardization and validation, and is followed by a justification section that draws upon existing literature and information quality frameworks in addition to the survey of leaders in the industry. Emphasizing objectivity, utility, integrity, and standardization as foundational pillars, the proposed framework provides practical guidelines to empower healthcare organizations in effectively managing information quality within the managed care model.

Identifiants

pubmed: 39359406
doi: 10.2147/JHL.S473833
pii: 473833
pmc: PMC11445674
doi:

Types de publication

Journal Article

Langues

eng

Pagination

343-364

Informations de copyright

© 2024 Crossette-Thambiah et al.

Déclaration de conflit d'intérêts

This paper is based on the dissertation of Grace Crossette-Thambiah, available on a personal professional website at: https://dberleant.github.io/papers/GraceDissertation.pdf. Daniel Berleant reports grants from US National Science Foundation, during the conduct of the study; grants from US National Science Foundation, outside the submitted work. The authors report no other conflicts of interest in this work.

Auteurs

Grace Crossette-Thambiah (G)

Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA.

Daniel Berleant (D)

Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA.

Ahmed AbuHalimeh (A)

Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA.

Classifications MeSH