SynthesisJournal of medical Internet research2023
Digital Health Data Quality Issues: Systematic Review.
Synthesis in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 6 of them syntheses that pooled 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.
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
64 citing papers in PubMed, 6 syntheses or guidelines pooled it, 81 citations in OpenAlex.
- Reporting Gaps in mHealth Intervention Studies for Adults With Diabetes: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- The User Experience of Ambulatory Assessment and Mood Monitoring in Bipolar Disorder: Systematic Review and Meta-Synthesis of Qualitative Studies.Journal of medical Internet research · 2025Pooled it
- Developing the Digital Health Communication Maturity Model: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Architectural patterns for health information systems: a systematic review.Frontiers in digital health · 2025Pooled it
- Data Quality-Driven Improvement in Health Care: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study.JMIR AI · 2026Article
- Data standards and interoperability for health.BMC proceedings · 2026Article
- Manifestations and Potential Consequences of Information Distortion in Electronic Nursing Records: Qualitative Study.JMIR nursing · 2026Article
- Temporal Variability in Diagnosis Code Distributions Across Extraction Time Points in a Multicenter Integrated EHR Database: A Snapshot Comparison Study.Journal of medical systems · 2026Observational
- Artificial Intelligence in Pharmaceutical Care:Saudi medical journal · 2026Review
- Health Data Quality Skill Gaps and Training Needs Among European Health Data Stakeholders: Cross-Sectional Survey.Journal of medical Internet research · 2026Article
- Automated Processes and Artificial Intelligence in Generating Candidates for Oncology Drug Repurposing: Three-Year Scoping Review of Data.Pharmacy (Basel, Switzerland) · 2026Review
- Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.Journal of medical Internet research · 2026Article
- Implementing a quality assurance and maintenance framework for standardized clinical terminology data in a multi-institutional setting.BMC medical informatics and decision making · 2026Article
- Article
- Addressing Data Quality Challenges in Lung Cancer Data Within the Observational Medical Outcomes Partnership Common Data Model: Observational Study.Journal of medical Internet research · 2026Observational
- A generative approach for semantic auditing of electronic health records.NPJ digital medicine · 2026Article
- Exploring Technological Solutions for Interoperability Between Patient Electronic Medical Records and Clinical Registries: Scoping Review.Journal of medical Internet research · 2026Article
- Bridging the Digital Divide: A Multi-Method Evaluation of Nursing Readiness for Digital Health Technology.Journal of advanced nursing · 2026Observational
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.
objectiveThe aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?
methodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.
resultsThe digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.
conclusionsThe DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.
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.