SynthesisBMC medical informatics and decision making2024
Common data quality elements for health information systems: a systematic review.
Synthesis in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed.
- Driving Revisit Intentions Through Medical Information and Service Quality in General Hospitals: An Extended Technology Acceptance Model Approach.Healthcare (Basel, Switzerland) · 2026Article
- Article
- Developing a framework for improved data use in health management at the district level in Malawi health management information system.Health policy OPEN · 2026Article
- Generative AI vs web search for patient education: a comparative evaluation of OSA information quality.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Article
- Design and evaluation of an automated pediatric acute lymphoblastic leukemia registry from clinical data warehouses.BMC medical informatics and decision making · 2026Article
- Variability in Perceived Healthcare Data Quality Across Tanzanian Regional Referral Hospitals: A Hospital-Based Cross-Sectional Study.Health science reports · 2026Article
- Artificial intelligence in acute and critical care: current challenges and strategic solutions.Frontiers in public health · 2026Review
- Assessing Data Quality in Heterogeneous Health Care Integration: Simulation Study of the AIDAVA Framework.JMIR medical informatics · 2025Article
- Quality and Reliability of Transarterial Chemoembolization Videos on TikTok and Bilibili: Cross-Sectional Content Analysis Study.JMIR formative research · 2025Article
- Population health management fit lifecycles in analytics.Frontiers in artificial intelligence · 2025Article
- Development and application of a healthcare data quality indicator framework from the perspective of data-as-a-product.Digital healthArticle
- Article
- Bridging digital health gaps in South Africa: A qualitative study of the digital divide, interoperability and health equity.Digital healthArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundData quality in health information systems has a complex structure and consists of several dimensions. This research conducted for identify Common data quality elements for health information systems.
methodsA literature review was conducted and search strategies run in Web of Knowledge, Science Direct, Emerald, PubMed, Scopus and Google Scholar search engine as an additional source for tracing references. We found 760 papers, excluded 314 duplicates, 339 on abstract review and 167 on full-text review; leaving 58 papers for critical appraisal.
resultsCurrent review shown that 14 criteria are categorized as the main dimensions for data quality for health information system include: Accuracy, Consistency, Security, Timeliness, Completeness, Reliability, Accessibility, Objectivity, Relevancy, Understandability, Navigation, Reputation, Efficiency and Value- added. Accuracy, Completeness, and Timeliness, were the three most-used dimensions in literature.
conclusionsAt present, there is a lack of uniformity and potential applicability in the dimensions employed to evaluate the data quality of health information system. Typically, different approaches (qualitative, quantitative and mixed methods) were utilized to evaluate data quality for health information system in the publications that were reviewed. Consequently, due to the inconsistency in defining dimensions and assessing methods, it became imperative to categorize the dimensions of data quality into a limited set of primary dimensions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.