Evidence map›Paper›PMID 39359406›Full record

ArticleJournal of healthcare leadership2024

An Information Quality Framework for Managed Health Care.

Grace Crossette-Thambiah, Daniel Berleant, Ahmed AbuHalimeh

Abstract read
In one paragraph

Article in Journal of healthcare leadership, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Grace Crossette-ThambiahDepartment of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA.
Daniel BerleantDepartment of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA.
Ahmed AbuHalimehDepartment of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.

Indexed as

data governancedata managementdata qualityinformation qualityinformation quality frameworkmanaged health caremanaged healthcare

Identifiers

PMID39359406
PMCPMC11445674

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.