Evidence map›Paper›PMID 30755205›Full record

ArticleBMC medical informatics and decision making2019

A basic model for assessing primary health care electronic medical record data quality.

Amanda L Terry, Moira Stewart, Sonny Cejic, J Neil Marshall, Simon de Lusignan, Bert M Chesworth, Vijaya Chevendra, Heather Maddocks, Joshua Shadd, Fred Burge and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 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

31 citing papers in PubMed, 3 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
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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

11 authors.

Amanda L TerryDepartment of Family Medicine, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada. aterry4@uwo.ca.ORCID 0000-0002-8157-8298
Moira StewartDepartment of Family Medicine, Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
Sonny CejicDepartment of Family Medicine, Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
J Neil MarshallDepartment of Family Medicine, Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
Simon de LusignanDepartment of Clinical and Experimental Medicine, University of Surrey, Guildford, Surrey, GU2 7XH, UK.
Bert M ChesworthSchool of Physical Therapy, Faculty of Health Sciences, Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
Vijaya ChevendraScience and Software Educator and Consultant, 58 Moraine Walk, London, Ontario, N6G 4Y8, Canada.
Heather MaddocksDepartment of Family Medicine, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
Joshua ShaddDepartment of Family Medicine, McMaster University, 100 Main Street West, 6th Floor, Hamilton, Ontario, L8P 1H6, Canada.
Fred BurgeDepartment of Family Medicine, Dalhousie University, 5909 Veterans Memorial Lane, Abbie J Lane Building, Room 8101B, Halifax, Nova Scotia, B3H 2E2, Canada.
Amardeep ThindDepartment of Family Medicine, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.

Funding

CIHR 105431
6 · The paper itself

Abstract

backgroundThe increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs.

methodsUsing an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products.

resultsA set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight).

conclusionsThis paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Indexed as

Electronic Health RecordsModels, TheoreticalPrimary Health CareQuality Assurance, Health CareAdultCanadaData AccuracyDatasets as TopicFemaleHumansMaleMiddle AgedComputerized medical recordsData qualityElectronic medical recordsPrimary health care

Identifiers

PMID30755205
PMCPMC6373085

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.