Validity of algorithms for identifying five chronic conditions in MedicineInsight, an Australian national general practice database

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Abstract

Background

MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. It is one of the largest and most widely used primary health care EHR databases in Australia. This study examined the validity of algorithms that use information from various fields in the MedicineInsight data to indicate whether patients have specific health conditions. This study examined the validity of MedicineInsight algorithms for five common chronic conditions: anxiety, asthma, depression, osteoporosis and type 2 diabetes.

Methods

Patients’ disease status according to MedicineInsight algorithms was benchmarked against the recording of diagnoses in the original EHRs. Fifty general practices contributing data to MedicineInsight met the eligibility criteria regarding patient load and location. Five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected from the MedicineInsight database. Trained staff reviewed the original EHR for as many of the selected patients as possible within the time available for data collection in each practice.

Results

A total of 475 patients were included in the analysis. All the evaluated MedicineInsight algorithms had excellent specificity, positive predictive value, and negative predictive value (above 0.9) when benchmarked against the recording of diagnoses in the original EHR. The asthma and osteoporosis algorithms also had excellent sensitivity, while the algorithms for anxiety, depression and type 2 diabetes yielded sensitivities of 0.85, 0.89 and 0.89 respectively.

Conclusions

The MedicineInsight algorithms for asthma and osteoporosis have excellent accuracy and the algorithms for anxiety, depression and type 2 diabetes have good accuracy. This study provides support for the use of these algorithms when using MedicineInsight data for primary health care quality improvement activities, research and health system policymaking and planning.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06593-z.

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Most cited references 21

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Data Resource Profile: Clinical Practice Research Datalink (CPRD)

Emily Herrett, Arlene M. Gallagher, Krishnan Bhaskaran … (2015)

The Clinical Practice Research Datalink (CPRD) is an ongoing primary care database of anonymised medical records from general practitioners, with coverage of over 11.3 million patients from 674 practices in the UK. With 4.4 million active (alive, currently registered) patients meeting quality criteria, approximately 6.9% of the UK population are included and patients are broadly representative of the UK general population in terms of age, sex and ethnicity. General practitioners are the gatekeepers of primary care and specialist referrals in the UK. The CPRD primary care database is therefore a rich source of health data for research, including data on demographics, symptoms, tests, diagnoses, therapies, health-related behaviours and referrals to secondary care. For over half of patients, linkage with datasets from secondary care, disease-specific cohorts and mortality records enhance the range of data available for research. The CPRD is very widely used internationally for epidemiological research and has been used to produce over 1000 research studies, published in peer-reviewed journals across a broad range of health outcomes. However, researchers must be aware of the complexity of routinely collected electronic health records, including ways to manage variable completeness, misclassification and development of disease definitions for research.

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Validation and validity of diagnoses in the General Practice Research Database: a systematic review

Emily Herrett, Sara L Thomas, W Schoonen … (2010)

AIMS To investigate the range of methods used to validate diagnoses in the General Practice Research Database (GPRD), to summarize findings and to assess the quality of these validations. METHODS A systematic literature review was performed by searching PubMed and Embase for publications using GPRD data published between 1987 and April 2008. Additional publications were identified from conference proceedings, back issues of relevant journals, bibliographies of retrieved publications and relevant websites. Publications that reported attempts to validate disease diagnoses recorded in the GPRD were included. RESULTS We identified 212 publications, often validating more than one diagnosis. In total, 357 validations investigating 183 different diagnoses met our inclusion criteria. Of these, 303 (85%) utilized data from outside the GPRD to validate diagnoses. The remainder utilized only data recorded in the database. The median proportion of cases with a confirmed diagnosis was 89% (range 24–100%). Details of validation methods and results were often incomplete. CONCLUSIONS A number of methods have been used to assess validity. Overall, estimates of validity were high. However, the quality of reporting of the validations was often inadequate to permit a clear interpretation. Not all methods provided a quantitative estimate of validity and most methods considered only the positive predictive value of a set of diagnostic codes in a highly selected group of cases. We make recommendations for methodology and reporting to strengthen further the use of the GPRD in research.

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Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates

Betina Blak, Mary Thompson, Hassy Dattani … (2011)

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Author and article information

Contributors

Alys Havard: ahavard@nps.org.au

Journal

Journal ID (nlm-ta): BMC Health Serv Res

Journal ID (iso-abbrev): BMC Health Serv Res

Title: BMC Health Services Research

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6963

Publication date (Electronic): 5 June 2021

Publication date PMC-release: 5 June 2021

Publication date Collection: 2021

Volume: 21

Electronic Location Identifier: 551

Affiliations

[1 ]Alys Havard, NPS MedicineWise, PO Box 1147 , Strawberry Hills, NSW 2012 Sydney, Australia

[2 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, Medicines Policy Research Unit, Centre for Big Data Research in Health, Faculty of Medicine, , UNSW Sydney, ; Sydney, NSW Australia

[3 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of General Practice, , University of Melbourne, ; Melbourne, VIC Australia

[4 ]GRID grid.117476.2, ISNI 0000 0004 1936 7611, University of Technology Sydney, ; Sydney, NSW Australia

[5 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Family and Community Medicine, , University of Toronto, ; Toronto, Ontario Canada

[6 ]GRID grid.416529.d, ISNI 0000 0004 0485 2091, North York General Hospital, ; Toronto, Ontario Canada

Article

Publisher ID: 6593

DOI: 10.1186/s12913-021-06593-z

PMC ID: 8178900

PubMed ID: 34090424

SO-VID: 1a2b53d3-3d71-45c2-aab3-7df36a495fc8

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 14 December 2020

Date accepted : 28 May 2021

Funding

Funded by: Australian Government Department of Health

Funded by: FundRef http://dx.doi.org/10.13039/501100008097, Department of Family and Community Medicine, University of Toronto;

Custom metadata

ScienceOpen disciplines: Health & Social care

Keywords: electronic health records,primary health care,chronic disease,validation study

Data availability:

ScienceOpen disciplines: Health & Social care

Keywords: electronic health records, primary health care, chronic disease, validation study

Validity of algorithms for identifying five chronic conditions in MedicineInsight, an Australian national general practice database

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Abstract

Background

Methods

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Supplementary Information

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Tobacco Prevention & Cessation

Most cited references 21

Data Resource Profile: Clinical Practice Research Datalink (CPRD)

Validation and validity of diagnoses in the General Practice Research Database: a systematic review

Generalisability of The Health Improvement Network (THIN) database: demographics, chronic disease prevalence and mortality rates

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