ArticleBMJ open2019
Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Information Distortion in Electronic Health Records: A Concept Analysis.Journal of advanced nursing · 2026Review
- Promoting Comprehensive Care for People With Rare Diseases in a Tertiary Care Setting in Brazil: Protocol for a Mixed Methods Implementation Study.JMIR research protocols · 2025Article
- The effect of enhanced recovery protocols on postoperative rehabilitation in orthopedic surgery patients.Medicine · 2025Observational
- Exploring the factors influencing evidence-based approaches to advanced chronic kidney disease: a qualitative study involving nurses and physicians.BMC primary care · 2024Article
- Factors impacting clinical data and documentation quality in Australian aged care and disability services: a user-centred perspective.BMC geriatrics · 2024Article
- Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study.Frontiers in digital health · 2024Article
- Machine learning models to predict surgical case duration compared to current industry standards: scoping review.BJS open · 2023Article
- Data Quality in Health Research: Integrative Literature Review.Journal of medical Internet research · 2023Review
- The data quality and applicability of a Danish prehospital electronic health record: A mixed-methods study.PloS one · 2023Article
- A narrative review on the validity of electronic health record-based research in epidemiology.BMC medical research methodology · 2021Review
- Clinical researchers' lived experiences with data quality monitoring in clinical trials: a qualitative study.BMC medical research methodology · 2021Article
- Using Electronic Medical Record Data for Research in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 Hospital in Beijing: Cross-sectional Study.JMIR medical informatics · 2021Article
- Clinical effect of application of nursing concept of rehabilitation surgery for improvement of quality of postoperative recovery in orthopedics.Journal of orthopaedic surgery and research · 2021Article
- Article
- Leveraging healthcare professionals' insights to enhance data quality in medical big data platforms: A qualitative study.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThere is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals' experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context.
settingFour tertiary hospitals in Beijing, China.
participantsNineteen healthcare professionals with experience in using EHR data for clinical research participated in the study.
methodsA qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.
resultsThe main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation.
conclusionsThese results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.
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