Cited by 19Medical Coding and Health InformationElectronic Health Records SystemsBone health and osteoporosis research

Quality and variability of osteoporosis data in general practice computer records: implications for disease registers

de Lusignan, S., Chan, T., Wood, O., Hague, N., Valentin, T., Van Vlymen, J.·2005·Public Health

Abstract

Objective To determine the extent to which routinely collected general practitioner computer data could be used to create disease registers of patients with osteoporosis, and to report any improvement in data quality since previous studies. Study design Audit using anonymized data extracted from general practice computer records from across England. Methods Morbidity Query Information and Export Syntax (MIQUEST) software was used to extract structured data from the 78 volunteer practices that participated in the study. The data were aggregated and analysed. Results There were 100-fold differences in the rates of recording of relevant data. Many patients receiving treatment had no diagnostic codes. Data about secondary causes of osteoporosis and fractures were more consistently recorded than data relating to falls. There were no data to indicate whether fractures were low impact. T-scores, the gold-standard measure of bone density, were very infrequently recorded. Conclusions Sufficient data about secondary causes of osteoporosis exist, and these could be searched to identify patients at risk. Meanwhile, fracture recoding could be improved, including likely fragility fractures, and T-scores could be added to computer records. A systematic approach is needed to raise the computer records to a standard where they can be used as valid and reliable disease registers.

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Crossref · 10.1016/j.puhe.2004.10.018

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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”
  • Other researchers compared distributions of data values between practices or with national rates, or looked at agreement between related elements to assess plausibility.

    “Other researchers compared distributions of data values between practices50 101 or with national rates,102 103 or looked at agreement between related elements.38 73”

Cites

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Research works printed but not linked: 24

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Authors
de Lusignan, S., Chan, T., Wood, O., Hague, N., Valentin, T., & Van Vlymen, J.
Affiliations
  • St George's Hospital (GB)
  • St George's, University of London (GB)
Corresponding author
de Lusignan, S.
Venue
Public Health
Publisher
Elsevier BV
Published
2005-09
Volume
119
Issue
9
Pages
771-780
ISSN
0033-3506 (ISSN-L)
OpenAlex
W1967679160
Cited by
19
References
1
Citations by year
2024: 1 · 2020: 2 · 2015: 3 · 2014: 1 · 2012: 1
FWCI
2.10
Type
journal article
Language
en
Open access
closed
Locations
  • Public Health (publishedVersion)
  • PubMed (publishedVersion)
  • Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
Funders
National Science Foundation
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, disease registries, data quality, secondary causes of osteoporosis, fragility fractures, falls, T-score, bone mineral density, electronic health records
MeSH terms
Accidental Falls, Adolescent, Adult, Aged, Aged, 80 and over, Bone Diseases, Metabolic, Child, Child, Preschool, England, Family Practice, Female, Humans, Infant, Male, Medical Audit, Medical Informatics, Medical Records, Problem-Oriented, Middle Aged, Osteoporosis, Registries, Research Design, Risk Factors, Medical Records Systems, Computerized, Risk Assessment, Fractures, Bone

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