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Problems with primary care data quality: osteoporosis as an exemplar
Open accessAbstract
Objective To report problems implementing a data quality programme in osteoporosis. Design Analysis of data extracted using Morbidity Information Query and Export Syntax (MIQUEST) from participating general practices' systems and recommendations of practitioners who attended an action research workshop. Setting Computerised general practices using different Read code versions to record structured data. Participants 78 practices predominantly from London and the south east, with representation from north east, north west and south west England. Main outcome measures Patients at risk can be represented in many ways within structured data. Although fracture data exists, it is unclear which are fragility fractures. T-scores, the gold standard for measuring bone density, cannot be extracted using the UK's standard data extraction tool, MIQUEST; instead manual searches had to be implemented. There is a hundredfold variation in data recording levels between practices. Therapy is more frequently recorded than diagnosis. A multidisciplinary forum of experienced practitioners proposed that a limited list of codes should be used. Conclusions There is variability in inter-practice data quality. Some clinically important codes are lacking, and there are multiple ways that the same clinical concept can be represented. Different practice computer systems have different versions of Read code, making some data incompatible. Manual searching is still required to find data. Clinicians with an u…
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Other methods for assessing completeness included agreement between elements from the same source, agreement between the EHR and paper records, agreement between the EHR and another electronic source, and comparing distributions of occurrences between practices or with nationally recorded rates.
“Other methods for assessing completeness included looking at agreement between elements from the same source,49–55 56 agreement between the EHR and paper records,79 87 88 agreement between the EHR and another electronic source of data,79 89 and comparing distributions of occurrences of certain elements between practices97 or with nationally recorded rates.98–100”
From Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research (2013) · cites this paper as [51]
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Metadata
- Authors
- DeLusignan, S., Valentin, T., Chan, T., Hague, N., Wood, O., VanVlymen, J., & Dhoul, N.
- ORCID
- Chan, Tom
- Venue
- Journal of Innovation in Health Informatics
- Publisher
- Portico
- Published
- 2004
- Volume
- 12
- Issue
- 3
- Pages
- 147-156
- ISSN
- 1475-9985 (ISSN-L)
- 2058-4555
- OpenAlex
- W2019804495
- Cited by
- 49
- References
- 18
- Citations by year
- 2023: 4 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 1 · 2017: 1 · 2016: 4 · 2015: 2 · 2014: 3 · 2013: 1 · 2012: 2
- FWCI
- 12.98
- Type
- journal article
- Language
- en
- Open access
- green
- doaj.org
- Licence
- cc-by-sa
- Locations
- Journal of Innovation in Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Journal of Innovation in Health Informatics (BCS, The Chartered Institute for IT) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
- Keywords
- osteoporosis, Read codes, structured clinical data, fragility fractures, T-score, bone mineral density, data extraction, medical coding
- MeSH terms
- Accidental Falls, Adult, Aged, England, Family Practice, Female, Humans, Terminology as Topic, Osteoporosis, Quality Control, Medical Records Systems, Computerized
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