Cited by 8Electronic Health Records SystemsMedical Coding and Health InformationBone health and osteoporosis research

The DOI matches its Crossref record.

Can patients with osteoporosis, who should benefit from implementation of the national service framework for older people, be identified from general practice computer records? A pilot study that illustrates the variability of computerized medical records and problems with searching them

de Lusignan, S, Chan, T, Wells, S, Cooper, A, Harvey, M, Brew, S, Wright, M·2003·Public Health

Abstract

Although UK general practice is highly computerized, comprehensive use of these computers is often limited to registration data and the issue of repeat prescriptions. The recording of diagnostic data is patchy. This study examines whether patients with, or at risk of, osteoporosis can be readily identified from general practice computer records. It reports the findings of a pilot study designed to show the variability of recording the diagnosis of osteoporosis and osteopenia, as well as how useful surrogate markers might be to identify these patients. The study also illustrates the difficulties that even skilled practitioners in a primary care research network experience in extracting clinical data from practice information systems. Computer searches were carried out across six practices in a general practice research network in the south-east of England. Two of these practices had previously undertaken research projects in osteoporosis and were consequently expected to have excellent data quality in osteoporosis. These two practices had a combined list size of 27,500 and the remaining practices had a combined practice population of 43,000 patients. The data were found to be variable with over 10-fold differences between practices in the recorded prevalence of osteoporosis diagnosis as well as its surrogate markers-such as fragility fractures, long-term steroid prescription, etc. There was no difference in data quality between the two practices that had conducted osteoporosis…

Overview

Integrity signals

Registry

Crossref · 10.1016/s0033-3506(03)00129-x

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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”

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 12

  1. Marshall, 2002, The new contract: renaissance or requiem for general practice?, Br J Gen Prac, 52, 531
  2. de Lusignan, 2001, Use of computer systems in a primary care research network: implications for the NHS Information Strategy, Clin Mgmt, 10, 32
  3. Thiru, 1999, Have the completeness and accuracy of computer medical records in general practice improved in the last five years? The report of a two-practice pilot study, Health Informatics J, 5, 233, 10.1177/146045829900500410 · DOI · Paper page
  4. Kanis, 2002, Diagnosis of osteoporosis and assessment of fracture risk, Lancet, 359, 1929, 10.1016/S0140-6736(02)08761-5 · DOI
  5. Cooper, 2002, The effectiveness of blood tests in detecting secondary osteoporosis or mimicking conditions in postmenopausal women, Br J Gen Prac, 52, 311
  6. Harvey, 2002, Osteoporosis in postmenopausal women, Br J Gen Prac, 52, 496
  7. Mitchell, 2001, A descriptive feast but an evaluative famine: systematic review of published articles on primary care computing during 1980–97, BMJ, 322, 279, 10.1136/bmj.322.7281.279 · DOI
  8. Ballard, 1998, Prevalence of osteoporosis and related risk factors in UK women in the seventh decade: osteoporosis case finding by clinical referral criteria or predictive model?, Osteoporos Int, 8, 535, 10.1007/s001980050095 · DOI
  9. Versluis, 2001, Clinical risk factors as predictors of postmenopausal osteoporosis in general practice, Br J Gen Prac, 51, 806
  10. Siris, 2001, Identification and fracture outcomes of undiagnosed low bone mineral density in postmenopausal women: results from the National Osteoporosis Risk Assessment, JAMA, 286, 2815, 10.1001/jama.286.22.2815 · DOI
  11. de Lusignan, 2000, Alignment of information for health with The NHS plan—a case for substantial investment and reform, Br J Healthcare Comput Info Mgmt, 17, 28
  12. Black, 2001, An assessment tool for predicting fracture risk in postmenopausal women, Osteoporos Int, 12, 519, 10.1007/s001980170072 · DOI

Cited by

Sorted by citations

Metadata

Authors
de Lusignan, S., Chan, T., Wells, S., Cooper, A., Harvey, M., Brew, S., & Wright, M.
Corresponding author
de Lusignan, S
Venue
Public Health
Publisher
Elsevier BV
Published
2003-11
Volume
117
Issue
6
Pages
438-445
ISSN
0033-3506 (ISSN-L)
OpenAlex
W2081954013
Cited by
8
References
0
Citations by year
2024: 1 · 2015: 1 · 2012: 1
FWCI
2.94
Type
journal article
Language
en
Open access
closed
Locations
  • Public Health (publishedVersion)
  • PubMed (publishedVersion)
  • Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
Keywords
osteoporosis, osteopenia, electronic health records, data quality, surrogate markers, fragility fractures, long-term corticosteroid therapy, read classification, National Service Framework for Older People, falls, patient identification, primary care research networks
MeSH terms
Aged, England, Family Practice, Female, Health Services Accessibility, Health Services Research, Humans, Male, Middle Aged, Osteoporosis, Pilot Projects, Population Surveillance, State Medicine, Prevalence, Information Storage and Retrieval, Medical Records Systems, Computerized, Public Health Informatics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review