Evidence map›Paper›PMID 24145818›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

Is the quality of data in an electronic medical record sufficient for assessing the quality of primary care?

Pashiera Barkhuysen, Wim de Grauw, Reinier Akkermans, José Donkers, Henk Schers, Marion Biermans

Abstract readComparative Study
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Pooled it
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Improving the quality of EHR recording in primary care: a data quality feedback tool.Journal of the American Medical Informatics Association : JAMIA · 2017
    Article
  12. Article
  13. Article
  14. Effect of pooled comparative information on judgments of quality.IEEE transactions on human-machine systems · 2015
    Article
  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pashiera BarkhuysenDepartment of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands.
Wim de GrauwDepartment of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands.
Reinier AkkermansDepartment of Primary and Community Care/Scientific Institute for Quality of Healthcare, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands.
José DonkersDepartment of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands.
Henk SchersDepartment of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands.
Marion BiermansDepartment of Primary and Community Care, Radboud University Nijmegen Medical Centre (RUNMC), Nijmegen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveQuality indicators for the treatment of type 2 diabetes are often retrieved from a chronic disease registry (CDR). This study investigates the quality of recording in a general practitioner's (GP) electronic medical record (EMR) compared to a simple, web-based CDR.

methodsThe GPs entered data directly in the CDR and in their own EMR during the study period (2011). We extracted data from 58 general practices (8235 patients) with type 2 diabetes and compared the occurrence and value of seven process indicators and 12 outcome indicators in both systems. The CDR, specifically designed for monitoring type 2 diabetes and reporting to health insurers, was used as the reference standard. For process indicators we examined the presence or absence of recordings on the patient level in both systems, for outcome indicators we examined the number of compliant or non-compliant values of recordings present in both systems. The diagnostic OR (DOR) was calculated for all indicators.

resultsWe found less concordance for process indicators than for outcome indicators. HbA1c testing was the process indicator with the highest DOR. Blood pressure measurement, urine albumin test, BMI recorded and eye assessment showed low DOR. For outcome indicators, the highest DOR was creatinine clearance <30 mL/min or mL/min/1.73 m(2) and the lowest DOR was systolic blood pressure <140 mm Hg.

conclusionsClinical items are not always adequately recorded in an EMR for retrieving indicators, but there is good concordance for the values of these items. If the quality of recording improves, indicators can be reported from the EMR, which will reduce the workload of GPs and enable GPs to maintain a good patient overview.

Indexed as

Quality Indicators, Health CareChronic DiseaseDiabetes Mellitus, Type 2Electronic Health RecordsGeneral PracticeGlycated HemoglobinHumansOutcome and Process Assessment, Health CarePrimary Health CareRegistriesGlycated Hemoglobinhemoglobin A1c protein, humanData QualityElectronic Medical RecordQuality IndicatorsQuality of CareType 2 Diabetes Mellitus

Identifiers

PMID24145818
PMCPMC4078269

What OpenQuestion holds

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Registered trials

None linked

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.