ClinicalCodes: An Online Clinical Codes Repository to Improve the Validity and Reproducibility of Research Using Electronic Medical Records

Lists of clinical codes are the foundation for research undertaken using electronic medical records (EMRs). If clinical code lists are not available, reviewers are unable to determine the validity of research, full study replication is impossible, researchers are unable to make effective comparisons between studies, and the construction of new code lists is subject to much duplication of effort. Despite this, the publication of clinical codes is rarely if ever a requirement for obtaining grants, validating protocols, or publishing research. In a representative sample of 450 EMR primary research articles indexed on PubMed, we found that only 19 (5.1%) were accompanied by a full set of published clinical codes and 32 (8.6%) stated that code lists were available on request. To help address these problems, we have built an online repository where researchers using EMRs can upload and download lists of clinical codes. The repository will enable clinical researchers to better validate EMR studies, build on previous code lists and compare disease definitions across studies. It will also assist health informaticians in replicating database studies, tracking changes in disease definitions or clinical coding practice through time and sharing clinical code information across platforms and data sources as research objects.

[1]  The paperless general practice , 1996, BMJ.

[2]  H. Guess,et al.  All-cause mortality and vascular events among patients with rheumatoid arthritis, osteoarthritis, or no arthritis in the UK General Practice Research Database. , 2003, The Journal of rheumatology.

[3]  Peter Croft,et al.  Quality of morbidity coding in general practice computerized medical records: a systematic review. , 2004, Family practice.

[4]  S. Mayor.  Angioplasty is cheaper than surgery and is just as effective , 2006, BMJ : British Medical Journal.

[5]  S. Soedamah-Muthu,et al.  Mortality in people with Type 2 diabetes in the UK , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[6]  J. Tanne.  Diabetes, not obesity, increases risk of death in middle age , 2006, BMJ : British Medical Journal.

[7]  S. Pocock,et al.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. , 2007, Preventive medicine.

[8]  J. Hippisley-Cox,et al.  Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study , 2007, Heart.

[9]  M. Gulliford,et al.  Selection of Medical Diagnostic Codes for Analysis of Electronic Patient Records. Application to Stroke in a Primary Care Database , 2009, PloS one.

[10]  Irene Petersen,et al.  Creating medical and drug code lists to identify cases in primary care databases , 2009, Pharmacoepidemiology and drug safety.

[11]  Sowmya R. Rao,et al.  Use of electronic health records in U.S. hospitals. , 2009, The New England journal of medicine.

[12]  M. Wallander,et al.  Rheumatoid arthritis in UK primary care: incidence and prior morbidity , 2009, Scandinavian journal of rheumatology.

[13]  Sean Bechhofer,et al.  Research Objects: Towards Exchange and Reuse of Digital Knowledge , 2010 .

[14]  T. Stukel,et al.  Importance of accurately identifying disease in studies using electronic health records , 2010, BMJ : British Medical Journal.

[15]  L. Smeeth,et al.  Validation and validity of diagnoses in the General Practice Research Database: a systematic review , 2010, British journal of clinical pharmacology.

[16]  Carole A. Goble,et al.  Why Linked Data is Not Enough for Scientists , 2010, 2010 IEEE Sixth International Conference on e-Science.

[17]  Rob Koeling,et al.  What does validation of cases in electronic record databases mean? The potential contribution of free text† , 2011, Pharmacoepidemiology and drug safety.

[18]  Evangelos Kontopantelis,et al.  Effect of financial incentives on incentivised and non-incentivised clinical activities: longitudinal analysis of data from the UK Quality and Outcomes Framework , 2011, BMJ : British Medical Journal.

[19]  Aziz Sheikh,et al.  Incidence, prevalence, and trends of general practitioner-recorded diagnosis of peanut allergy in England, 2001 to 2005. , 2011, The Journal of allergy and clinical immunology.

[20]  M. Gulliford,et al.  Coding, Recording and Incidence of Different Forms of Coronary Heart Disease in Primary Care , 2012, PloS one.

[21]  Jennifer M. Urban,et al.  Shining Light into Black Boxes , 2012, Science.

[22]  Stephen D. Wang.  Opportunities and challenges of clinical research in the big-data era: from RCT to BCT. , 2013, Journal of thoracic disease.

[23]  David Moher,et al.  Setting the RECORD straight: developing a guideline for the REporting of studies Conducted using Observational Routinely collected Data , 2013, Clinical epidemiology.

[24]  Peter Schirmbacher,et al.  Making Research Data Repositories Visible: The re3data.org Registry , 2013, PloS one.

[25]  Greta Rait,et al.  Optimising Use of Electronic Health Records to Describe the Presentation of Rheumatoid Arthritis in Primary Care: A Strategy for Developing Code Lists , 2013, PloS one.

[26]  J. Hippisley-Cox,et al.  Exposure to bisphosphonates and risk of gastrointestinal cancers: series of nested case-control studies with QResearch and CPRD data , 2013, BMJ.

[27]  T. Murdoch,et al.  The inevitable application of big data to health care. , 2013, JAMA.

[28]  Evangelos Kontopantelis,et al.  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 , 2012, BMJ quality & safety.

[29]  V. Stodden,et al.  Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals , 2013, PloS one.

[30]  Evangelos Kontopantelis,et al.  Withdrawing performance indicators: retrospective analysis of general practice performance under UK Quality and Outcomes Framework , 2014, BMJ : British Medical Journal.

[31]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[32]  Evangelos Kontopantelis,et al.  Can analyses of electronic patient records be independently and externally validated? The effect of statins on the mortality of patients with ischaemic heart disease: a cohort study with nested case–control analysis , 2014, BMJ Open.

[33]  Kei Koizumi,et al.  Increasing Access to the Results of Federally Funded Scientific Research , 2016 .