Evidence map›Paper›PMID 32928214›Full record

ArticleBMC medical informatics and decision making2020

Definitions, components and processes of data harmonisation in healthcare: a scoping review.

Bey-Marrié Schmidt, Christopher J Colvin, Ameer Hohlfeld, Natalie Leon

Abstract readScoping Review
In one paragraph

Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

4 authors.

Bey-Marrié SchmidtCochrane South Africa, South African Medical Research Council, Francie Van Zijl Dr, Parow Valley, Cape Town, 7501, South Africa. bey-marrie.schmidt@mrc.ac.za.ORCID 0000-0003-1363-171X
Christopher J ColvinDivision of Social and Behavioural Sciences, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.
Ameer HohlfeldCochrane South Africa, South African Medical Research Council, Francie Van Zijl Dr, Parow Valley, Cape Town, 7501, South Africa.
Natalie LeonDepartment of Public Health Sciences, University of Virginia, Charlottesville, USA.

Funding

Using Information to Align Services and Link and Retain Men in the HIV CascadeR01MH106600 · NIMH · UNIVERSITY OF CAPE TOWN · PI COLVIN, CHRISTOPHER JAMES, LURIE, MARK NATHAN · 2015 to 2019
$2.1M
NIMH NIH HHS R01 MH106600US National Institute of Mental Health 1 R01 MH106600
6 · The paper itself

Abstract

backgroundData harmonisation (DH) has emerged amongst health managers, information technology specialists and researchers as an important intervention for routine health information systems (RHISs). It is important to understand what DH is, how it is defined and conceptualised, and how it can lead to better health management decision-making. This scoping review identifies a range of definitions for DH, its characteristics (in terms of key components and processes), and common explanations of the relationship between DH and health management decision-making.

methodsThis scoping review identified relevant studies from 2000 onwards (date filter), written in English and published in PubMed, Web of Science and CINAHL. Two reviewers independently screened records for potential inclusion for the abstract and full-text screening stages. One reviewer did the data extraction, analysis and synthesis, with built-in reliability checks from the rest of the team. We developed a narrative synthesis of definitions and explanations of the relationship between DH and health management decision-making.

resultsWe sampled 61 of 181 included to synthesis definitions and concepts of DH in detail. We identified six common terms for data harmonisation: record linkage, data linkage, data warehousing, data sharing, data interoperability and health information exchange. We also identified nine key components of data harmonisation: DH involves (a) a process of multiple steps; (b) integrating, harmonising and bringing together different databases (c) two or more databases; (d) electronic data; (e) pooling data using unique patient identifiers; and (f) different types of data; (g) data found within and across different departments and institutions at facility, district, regional and national levels; (h) different types of technical activities; (i) has a specific scope. The relationship between DH and health management decision-making is not well-described in the literature. Several studies mentioned health providers' concerns about data completeness, data quality, terminology and coding of data elements as barriers to data utilisation for clinical decision-making.

conclusionTo our knowledge, this scoping review was the first to synthesise definitions and concepts of DH and address the causal relationship between DH and health management decision-making. Future research is required to assess the effectiveness of data harmonisation on health management decision-making.

Indexed as

Health Information ExchangeHealth Information SystemsDelivery of Health CareHumansInformation Storage and RetrievalReproducibility of ResultsData harmonisationHealth information exchangeHealth information systemScoping review

Identifiers

PMID32928214
PMCPMC7488776

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.