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      How an electronic health record became a real-world research resource: comparison between London’s Whole Systems Integrated Care database and the Clinical Practice Research Datalink

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          Abstract

          Background

          In the UK, several initiatives have resulted in the creation of local data warehouses of electronic patient records. Originally developed for commissioning and direct patient care, they are potentially useful for research, but little is known about them outside their home area. We describe one such local warehouse, the Whole Systems Integrated Care (WSIC) database in NW London, and its potential for research as the “Discover” platform. We compare Discover with the Clinical Practice Research Datalink (CPRD), a popular UK research database also based on linked primary care records.

          Methods

          We describe the key features of the Discover database, including scope, architecture and governance; descriptive analyses compare the population demographics and chronic disease prevalences with those in CPRD.

          Results

          As of June 2019, Discover held records for a total of 2.3 million currently registered patients, or 95% of the NW London population; CPRD held records for over 11 million. The Discover population matches the overall age-sex distribution of the UK and CPRD but is more ethnically diverse. Most Discover chronic disease prevalences were comparable to the national rates. Unlike CPRD, Discover has identifiable care organisations and postcodes, allowing mapping and linkage to healthcare provider variables such as staffing, and includes contacts with social, community and mental health care. Discover also includes a consent-to-contact register of over 3000 volunteers to date for prospective studies.

          Conclusions

          Like CPRD, Discover has been a number of years in the making, is a valuable research tool, and can serve as a model for other areas developing similar data warehouses.

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          Most cited references4

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          Recorded quality of primary care for patients with diabetes in England before and after the introduction of a financial incentive scheme: a longitudinal observational study.

          Evangelos Kontopantelis, Tim Doran, Stephen Campbell … (2012)
          The UK's Quality and Outcomes Framework (QOF) was introduced in 2004/5, linking remuneration for general practices to recorded quality of care for chronic conditions, including diabetes mellitus. We assessed the effect of the incentives on recorded quality of care for diabetes patients and its variation by patient and practice characteristics. Using the General Practice Research Database we selected a stratified sample of 148 English general practices in England, contributing data from 2000/1 to 2006/7, and obtained a random sample of 653,500 patients in which 23,920 diabetes patients identified. We quantified annually recorded quality of care at the patient-level, as measured by the 17 QOF diabetes indicators, in a composite score and analysed it longitudinally using an Interrupted Time Series design. Recorded quality of care improved for all subgroups in the pre-incentive period. In the first year of the incentives, composite quality improved over-and-above this pre-incentive trend by 14.2% (13.7-14.6%). By the third year the improvement above trend was smaller, but still statistically significant, at 7.3% (6.7-8.0%). After 3 years of the incentives, recorded levels of care varied significantly for patient gender, age, years of previous care, number of co-morbid conditions and practice diabetes prevalence. The introduction of financial incentives was associated with improvements in the recorded quality of diabetes care in the first year. These improvements included some measures of disease control, but most captured only documentation of recommended aspects of clinical assessment, not patient management or outcomes of care. Improvements in subsequent years were more modest. Variation in care between population groups diminished under the incentives, but remained substantial in some cases.
          Article 0 comments Cited 41 times – based on 0 reviews     Review now
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            Validity and interpretation of spirometric recordings to diagnose COPD in UK primary care

            Kieran Rothnie, Joht Chandan, Harry G Goss … (2017)
            Background The diagnosis of COPD is dependent upon clinical judgment and confirmation of the presence of airflow obstruction using spirometry. Spirometry is now routinely available; however, spirometry incorrectly performed or interpreted can lead to misdiagnosis. We aimed to determine whether spirometry undertaken in primary care for patients suspected to have COPD was of sufficient quality and whether their spirometry was correctly interpreted. Methods Two chest physicians re-read all spirometric readings for both quality of the procedure and interpretation, received as a part of COPD validation studies using data from the Clinical Practice Research Datalink (CPRD). We then used logistic regression to investigate predictors of correct interpretation. Results Spirometry traces were obtained for 306 patients, of which 221 (72.2%) were conducted in primary care. Of those conducted in primary care, 98.6% (n=218) of spirometry traces were of adequate quality. Of those traces that were of adequate quality and conducted in primary care, and in whom a general practitioner (GP) diagnosis of COPD had been made, 72.5% (n=218) were consistent with obstruction. Historical records for asthma diagnosis significantly decreased odds of correct interpretation. Conclusion The quality of the spirometry procedure undertaken in primary care is high. However, this was not reflected in the quality of interpretation, suggesting an unmet training in primary care. The quality of the spirometry procedure as demonstrated by spirometric tracings provides a re-assurance for the use of spirometric values available in the electronic health care record databases for research purposes.
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              Identifying Patients at High Risk of Emergency Hospital Admissions: A Logistic Regression Analysis

              Alex Bottle, Paul Aylin, Azeem Majeed (2017)
              Article 0 comments Cited 15 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Alex Bottle:
                ORCID: http://orcid.org/0000-0001-9978-2011
                robert.bottle@imperial.ac.uk
                Journal
                Journal ID (nlm-ta): BMC Med Inform Decis Mak
                Journal ID (iso-abbrev): BMC Med Inform Decis Mak
                Title: BMC Medical Informatics and Decision Making
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1472-6947
                Publication date (Electronic): 20 April 2020
                Publication date PMC-release: 20 April 2020
                Publication date Collection: 2020
                Volume: 20
                Electronic Location Identifier: 71
                Affiliations
                [1 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Dr Foster Unit, Imperial College London, School of Public Health, , Imperial College London, ; 3 Dorset Rise, London, EC4Y 8EN UK
                [2 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Imperial College Health Partners, ; London, UK
                Author information
                http://orcid.org/0000-0001-9978-2011
                Article
                Publisher ID: 1082
                DOI: 10.1186/s12911-020-1082-7
                PMC ID: 7171852
                PubMed ID: 32312259
                SO-VID: 2155841a-0777-48fd-a7cb-96958c66e712
                Copyright © © The Author(s). 2020
                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 : 6 September 2019
                Date accepted : 29 March 2020
                Categories
                Subject: Research Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: data warehousing,electronic medical records,integrated care,real world evidence,clinical practice research datalink
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: data warehousing, electronic medical records, integrated care, real world evidence, clinical practice research datalink

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