ArticleJournal of the American Medical Informatics Association : JAMIA2017
Improving the quality of EHR recording in primary care: a data quality feedback tool.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Primary health care quality indicators: An umbrella review.PloS one · 2019Pooled it
- Insights into the proportion of consultations for musculoskeletal disorders in Dutch general practice: a register-based retrospective cohort study.BMC primary care · 2026Article
- How to ensure effective and sustainable ILI surveillance in Belgian general practices? : protocol.Archives of public health = Archives belges de sante publique · 2026Article
- Defining quality indicators for atherosclerotic cardiovascular diseases in primary care, extractable from the electronic health record: a RAND-modified Delphi method.BMC primary care · 2025Article
- Quality indicators for pharmacist follow-up of diabetes mellitus: A RAND-modified Delphi method.International journal of clinical pharmacy · 2025Article
- Recording practices of body mass index, overweight and obesity by Dutch general practitioners: an observational study.BMC primary care · 2025Observational
- Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making.PloS one · 2025Article
- Enhancing Standardized and Structured Recording by Elderly Care Physicians for Reusing Electronic Health Record Data: Interview Study.JMIR medical informatics · 2024Article
- The challenges and opportunities of continuous data quality improvement for healthcare administration data.JAMIA open · 2024Article
- Electronic Medical Record Data Missingness and Interruption in Antiretroviral Therapy Among Adults and Children Living With HIV in Haiti: Retrospective Longitudinal Study.JMIR pediatrics and parenting · 2024Article
- Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview.Journal of healthcare informatics research · 2024Article
- Data Quality in Health Research: Integrative Literature Review.Journal of medical Internet research · 2023Review
- "A Community-Engaged Approach to Address Collateral Findings in Embedded Research".The American journal of bioethics : AJOB · 2023Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Adherence to coprescribing of laxatives with opioids and associated characteristics in general practices in the Netherlands.BMC primary care · 2022Observational
- Detection of primary Sjögren's syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning.BMC primary care · 2022Article
- Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care.Applied clinical informatics · 2022Article
- Article
- Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?NPJ digital medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveElectronic health record (EHR) data are used to exchange information among health care providers. For this purpose, the quality of the data is essential. We developed a data quality feedback tool that evaluates differences in EHR data quality among practices and software packages as part of a larger intervention.
methodsThe tool was applied in 92 practices in the Netherlands using different software packages. Practices received data quality feedback in 2010 and 2012.
resultsWe observed large differences in the quality of recording. For example, the percentage of episodes of care that had a meaningful diagnostic code ranged from 30% to 100%. Differences were highly related to the software package. A year after the first measurement, the quality of recording had improved significantly and differences decreased, with 67% of the physicians indicating that they had actively changed their recording habits based on the results of the first measurement. About 80% found the feedback helpful in pinpointing recording problems. One of the software vendors made changes in functionality as a result of the feedback.
conclusionsOur EHR data quality feedback tool is capable of highlighting differences among practices and software packages. As such, it also stimulates improvements. As substantial variability in recording is related to the software package, our study strengthens the evidence that data quality can be improved substantially by standardizing the functionalities of EHR software packages.
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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.