ArticleBMC medical informatics and decision making2021
Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Data quality assessment in healthcare, dimensions, methods and tools: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Insights into the proportion of consultations for musculoskeletal disorders in Dutch general practice: a register-based retrospective cohort study.BMC primary care · 2026Article
- Challenges and Recommendations for Electronic Health Records Data Extraction and Preparation for Dynamic Prediction Modeling in Hospitalized Patients: Practical Guide and Tutorial.Journal of medical Internet research · 2025Article
- Challenges faced by healthcare providers in implementing preconception care for women with diabetes in public health clinics in Malaysia - A qualitative study.BMC primary care · 2025Article
- Comparing the Quality of Primary Care Electronic Health Record Data in Australia and Canada: Case Study in Osteoarthritis.Journal of medical Internet research · 2025Article
- Implementing a rapid-learning health system in early intervention services for psychosis: qualitative evaluation of its feasibility and acceptability.Health research policy and systems · 2025Article
- Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders.Journal of occupational rehabilitation · 2025Article
- Can routinely collected primary healthcare data be used to assess Aboriginal children's health and wellbeing longitudinally? A retrospective analysis of electronic medical records from an Aboriginal community-controlled health service in Central Australia.International journal of population data science · 2025Article
- Electronic Health Record Data Quality and Performance Assessments: Scoping Review.JMIR medical informatics · 2024Article
- Preconception health risk factors documented in general practice electronic medical records.BMJ sexual & reproductive health · 2024Article
- The Alliance for Healthier Communities as a Learning Health System for primary care: A qualitative analysis in Ontario, Canada.Journal of evaluation in clinical practice · 2022Article
- Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?BMC medical informatics and decision making · 2022Article
- Developing and internally validating a prediction model for total knee replacement surgery in patients with osteoarthritis.Osteoarthritis and cartilage open · 2022Article
- DQAgui: a graphical user interface for the MIRACUM data quality assessment tool.BMC medical informatics and decision making · 2022Article
- Describing a complex primary health care population to support future decision support initiatives.International journal of population data science · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of general practice electronic health records (EHRs) for research purposes is in its infancy in Australia. Given these data were collected for clinical purposes, questions remain around data quality and whether these data are suitable for use in prediction model development. In this study we assess the quality of data recorded in 201,462 patient EHRs from 483 Australian general practices to determine its usefulness in the development of a clinical prediction model for total knee replacement (TKR) surgery in patients with osteoarthritis (OA).
methodsVariables to be used in model development were assessed for completeness and plausibility. Accuracy for the outcome and competing risk were assessed through record level linkage with two gold standard national registries, Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) and National Death Index (NDI). The validity of the EHR data was tested using participant characteristics from the 2014-15 Australian National Health Survey (NHS).
resultsThere were substantial missing data for body mass index and weight gain between early adulthood and middle age. TKR and death were recorded with good accuracy, however, year of TKR, year of death and side of TKR were poorly recorded. Patient characteristics recorded in the EHR were comparable to participant characteristics from the NHS, except for OA medication and metastatic solid tumour.
conclusionsIn this study, data relating to the outcome, competing risk and two predictors were unfit for prediction model development. This study highlights the need for more accurate and complete recording of patient data within EHRs if these data are to be used to develop clinical prediction models. Data linkage with other gold standard data sets/registries may in the meantime help overcome some of the current data quality challenges in general practice EHRs when developing prediction models.
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