IT-CARES: an interactive tool for case-crossover analyses of electronic medical records for patient safety

Background: The significant risk of adverse events following medical procedures supports a clinical epidemiological approach based on the analyses of collections of electronic medical records. Data analytical tools might help clinical epidemiologists develop more appropriate case-crossover designs for monitoring patient safety. Objective: To develop and assess the methodological quality of an interactive tool for use by clinical epidemiologists to systematically design case-crossover analyses of large electronic medical records databases. Material and Methods: We developed IT-CARES, an analytical tool implementing case-crossover design, to explore the association between exposures and outcomes. The exposures and outcomes are defined by clinical epidemiologists via lists of codes entered via a user interface screen. We tested IT-CARES on data from the French national inpatient stay database, which documents diagnoses and medical procedures for 170 million inpatient stays between 2007 and 2013. We compared the results of our analysis with reference data from the literature on thromboembolic risk after delivery and bleeding risk after total hip replacement. Results: IT-CARES provides a user interface with 3 columns: (i) the outcome criteria in the left-hand column, (ii) the exposure criteria in the right-hand column, and (iii) the estimated risk (odds ratios, presented in both graphical and tabular formats) in the middle column. The estimated odds ratios were consistent with the reference literature data. Discussion: IT-CARES may enhance patient safety by facilitating clinical epidemiological studies of adverse events following medical procedures. The tool’s usability must be evaluated and improved in further research.

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

[2]  Michelle Dunn,et al.  The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data , 2014, J. Am. Medical Informatics Assoc..

[3]  Jesus J. Caban,et al.  Visual analytics in healthcare - opportunities and research challenges , 2015, J. Am. Medical Informatics Assoc..

[4]  Valerie Beral,et al.  Duration and magnitude of the postoperative risk of venous thromboembolism in middle aged women: prospective cohort study , 2009, BMJ : British Medical Journal.

[5]  Gordon H Guyatt,et al.  Executive Summary Antithrombotic Therapy and Prevention of Thrombosis , 9 th ed : American College of Chest Physicians Evidence-Based Clinical Practice Guidelines , 2012 .

[6]  M. Pagano,et al.  Survival analysis. , 1996, Nutrition.

[7]  James J. Thomas,et al.  Challenges for Visual Analytics , 2009, Inf. Vis..

[8]  Spencer S. Jones,et al.  Health Information Technology: An Updated Systematic Review With a Focus on Meaningful Use , 2014, Annals of Internal Medicine.

[9]  Thomas H. Payne,et al.  Report of the AMIA EHR-2020 Task Force on the status and future direction of EHRs , 2015, J. Am. Medical Informatics Assoc..

[10]  Rupert G. Miller,et al.  Survival Analysis , 2022, The SAGE Encyclopedia of Research Design.

[11]  Jarke J. van Wijk,et al.  Challenges for Visual Analytics , 2017, VISIGRAPP.

[12]  Carol Coupland,et al.  Predicting risk of upper gastrointestinal bleed and intracranial bleed with anticoagulants: cohort study to derive and validate the QBleed scores , 2014, BMJ : British Medical Journal.

[13]  P. Albaladejo,et al.  Prévention de la maladie thromboembolique veineuse périopératoire et obstétricale , 2005 .

[14]  J. Heit,et al.  Risk factors for venous thromboembolism. , 2003, Clinics in chest medicine.

[15]  S. Evans,et al.  Moving Along the Yellow Brick (Card) Road? , 2013, Drug Safety.

[16]  Peter Vestergaard,et al.  Risk of Gastrointestinal Bleeding in Patients Undergoing Total Hip or Knee Replacement Compared With Matched Controls: A Nationwide Cohort Study , 2013, The American Journal of Gastroenterology.

[17]  Jean-Luc Bosson,et al.  ICD-10 hospital discharge diagnosis codes were sensitive for identifying pulmonary embolism but not deep vein thrombosis. , 2010, Journal of clinical epidemiology.

[18]  Marlene R. Miller,et al.  Administrative data based patient safety research: a critical review , 2003, Quality & safety in health care.

[19]  Ben Shneiderman,et al.  Improving Healthcare with Interactive Visualization , 2013, Computer.

[20]  Thomas Wilke,et al.  Nonadherence in outpatient thromboprophylaxis after major orthopedic surgery: a systematic review , 2010, Expert review of pharmacoeconomics & outcomes research.

[21]  H. Wickham,et al.  A Grammar of Data Manipulation , 2015 .

[22]  G. Eysenbach Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet , 2009, Journal of medical Internet research.

[23]  Bengt I Eriksson,et al.  Extended duration rivaroxaban versus short-term enoxaparin for the prevention of venous thromboembolism after total hip arthroplasty: a double-blind, randomised controlled trial , 2008, The Lancet.

[24]  C. Martin 2015 , 2015, Les 25 ans de l’OMC: Une rétrospective en photos.

[25]  S. Flanders,et al.  Venous thromboembolism prophylaxis: a path toward more appropriate use , 2015, BMJ Quality & Safety.

[26]  D A Kessler,et al.  Introducing MEDWatch. A new approach to reporting medication and device adverse effects and product problems. , 1993, General hospital psychiatry.

[27]  D. Madigan,et al.  A Comparison of the Empirical Performance of Methods for a Risk Identification System , 2013, Drug Safety.

[28]  G. Agnelli,et al.  Insufficient duration of venous thromboembolism prophylaxis after total hip or knee replacement when compared with the time course of thromboembolic events: findings from the Global Orthopaedic Registry. , 2007, The Journal of bone and joint surgery. British volume.

[29]  L. Melton,et al.  Relative impact of risk factors for deep vein thrombosis and pulmonary embolism: a population-based study. , 2002, Archives of internal medicine.

[30]  Colin P. West,et al.  Time spent on clinical documentation: a survey of internal medicine residents and program directors. , 2010, Archives of internal medicine.

[31]  Devin M Mann,et al.  Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial. , 2013, JAMA internal medicine.

[32]  E. Schimmel The hazards of hospitalization* , 2003 .

[33]  J. Robins,et al.  Control sampling strategies for case-crossover studies: an assessment of relative efficiency. , 1995, American journal of epidemiology.

[34]  Richard B Devereux,et al.  Risk of a thrombotic event after the 6-week postpartum period. , 2014, The New England journal of medicine.

[35]  Tsung O Cheng,et al.  Could elevated C-reactive protein in patients with obstructive sleep apnea be due to obesity per se? , 2003, Circulation.

[36]  Patrick B. Ryan,et al.  Alternative Outcome Definitions and Their Effect on the Performance of Methods for Observational Outcome Studies , 2013, Drug Safety.

[37]  Hadley Wickham,et al.  An Implementation of the Grammar of Graphics , 2015 .

[38]  Joshua R. Vest,et al.  Factors motivating and affecting health information exchange usage , 2011, J. Am. Medical Informatics Assoc..

[39]  Samuel Z Goldhaber,et al.  Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross-sectional study , 2008, The Lancet.

[40]  P. Rothwell,et al.  External validity of randomised controlled trials: “To whom do the results of this trial apply?” , 2005, The Lancet.