Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

F. FitzHenry; F.S. Resnic; S.L. Robbins; J. Denton; L. Nookala; D. Meeker; L. Ohno-Machado; M.E. Matheny

doi:10.4338/ACI-2014-12-CR-0121

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2015; 06(03): 536-547
DOI: 10.4338/ACI-2014-12-CR-0121

Case Report

Schattauer GmbH

Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

F. FitzHenry

¹Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN

²Department of Biomedical Informatics; Vanderbilt University, Nashville, TN

,

F.S. Resnic

³Division of Cardiology, Brigham and Women’s Hospital, Boston, MA

,

S.L. Robbins

³Division of Cardiology, Brigham and Women’s Hospital, Boston, MA

,

J. Denton

¹Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN

⁴Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN

,

L. Nookala

¹Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN

⁴Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN

,

D. Meeker

⁵Department of Health, RAND Corporation, Santa Monica, CA

,

L. Ohno-Machado

⁶Division of Biomedical Informatics, University of California, San Diego, CA

,

M.E. Matheny

¹Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN

²Department of Biomedical Informatics; Vanderbilt University, Nashville, TN

⁴Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN

⁷Department of Biostatistics, Vanderbilt University, Nashville, TN

› Author Affiliations

Abstract

Summary

Background: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs.

Objective: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM.

Methods: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules.

Results: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system‘s medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM.

Conclusion: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.

FitzHenry F, Resnic FS, Robbins SL, Denton J, Nookala L, Meeker D, Ohno-Machado L, Matheny ME. A Case Report on Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 6: 536–547

http://dx.doi.org/10.4338/ACI-2014-12-CR-0121

Keywords

Common data model - big data - comparative effectiveness

Full Text

References

References
1 Mayer-Schönberger V, Cukier K. Big Data: a revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt; 2013
2 Toh S, Platt R. Is size the next big thing in epidemiology?. Epidemiology 2013; 24 (03) 349-351.
3 Grossman C, Powers B, Sanders J. [Rapporteurs]. Roundtable on value and science-driven health care, medicine Io: Digital data improvement priorities for continuous learning in health and healthcare: Workshop summary. Washington, DC: National Academies Press; 2013
4 Olsen LA, McGinnis JM. Redesigning the clinical effectiveness research paradigm: Innovation and practice-based approaches: Workshop summary. Edited by Medicine Io. Washington, DC: National Academies Press; 2010
5 Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salamé G, Catania MA, Salvo F, David A, Moore N, Caputi AP, Sturkenboom M, Molokhia M, Hippisley-Cox J, Acedo CD, van der Lei J, Four-rier-Reglat A. Data mining on electronic health record databases for signal detection in pharmacovigi-lance: which events to monitor?. Pharmacoepidemiology and Drug Safety 2009; 18 (12) 1176-1184.
6 Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. Journal of Clinical Epidemiology 2005; 58 (04) 323-337.
7 Suissa S, Garbe E. Primer: administrative health databases in observational studies of drug effects–advantages and disadvantages. Nature Clinical Practice Rheumatology 2007; 3 (12) 725-732.
8 Lohr KN. Emerging methods in comparative effectiveness and safety: symposium overview and summary. Medical Care 2007; 45 10 Supl 2 S5-8.
9 American Recovery and Reinvestment Act of 2009. In. USA; 2009: 123.
10 Berger ML, Doban V. Big data, advanced analytics and the future of comparative effectiveness research. Journal of Comparative Effectiveness Research 2014; 3 (02) 167-176.
11 Herland M, Khoshgoftaar T, Wald R. A review of data mining using big data in health informatics. Journal Of Big Data 2014; 1 (01) 2.
12 Van Poucke S, Thomeer M, Hadzic A. 2015, big data in healthcare: for whom the bell tolls?. Critical Care 2015; 19 (01) 171.
13 Adams CP, Brantner VV. Estimating the cost of new drug development: Is it really $802 million?. Health Affairs 2006; 25 (02) 420-428.
14 Food and Drug Administration: The sentinel initiative, National strategy for monitoring medical product safety. In.; 2010.
15 Food and Drug Administration: FDA Mini-Sentinel Assessment Reinforces Safety Data of Pradaxa® (dabigatran etexilate mesylate). In: PRNewswire; New York, NY: PRNewswire 2012
16 Rijnbeek P. Converting to a common data model: what is lost in translation?: Commentary on „fidelity assessment of a clinical practice research datalink conversion to the OMOP common data model“.[Erratum appears in Drug Saf. 2014; 37(12): 1073]. Drug Safety 2014; 37 (11) 893-896.
17 Hernandez-Diaz S, Varas-Lorenzo C, Garcia Rodriguez LA. Non-steroidal antiinflammatory drugs and the risk of acute myocardial infarction. Basic & Clinical Pharmacology & Toxicology 2006; 98 (03) 266-274.
18 Fortier I, Doiron D, Burton P, Raina P. Invited commentary: consolidating data harmonization–how to obtain quality and applicability?. American Journal of Epidemiology 2011; 174 (Suppl. 03) 261-264. author reply 265-266.
19 Bero CL, Lee TH. Achieving meaningful use: a health system perspective. American Journal of Managed Care 2010; 16 12 Suppl HIT SP9-12.
20 Ogunyemi OI, Meeker D, Kim H-E, Ashish N, Farzaneh S, Boxwala A. Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems. Medical Care 2013; 51 8 Suppl 3 S45-52.
21 Reich C, Ryan P, Torok D, Vereshagin S, Khayter M, Welebob E. OMOP Implementation Specification Standard Vocabularies in Observational Data Analysis Version 4.0. In.: Foundation for the National Institutes of Health. 2012
22 Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. Journal of the American Medical Informatics Association 2012; 19 (01) 54-60.
23 Zhou X, Murugesan S, Bhullar H, Liu Q, Cai B, Wentworth C, Bate A. An evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance. Drug Safety 2013; 36 (02) 119-134.
24 Matcho A, Ryan P, Fife D, Reich C. Fidelity assessment of a clinical practice research datalink conversion to the OMOP common data model. Drug Safety 2014; 37 (11) 945-959.
25 DeFalco F, Ryan P, Soledad Cepeda M. Applying standardized drug terminologies to observational health-care databases: a case study on opioid exposure. Health Serv Outcomes Res Method 2013; 13 (01) 58-67.
26 Veterans Health Administration: Patient care data capture: VHA Directive. 2009–002. In. Edited by Affairs USDoV. Washington, DC: Veterans Health Administration; 2009
27 Agha Z, Lofgren RP, VanRuiswyk JV, Layde PM. Are patients at Veterans Affairs medical centers sicker? A comparative analysis of health status and medical resource use. Archives of Internal Medicine 2000; 160 (21) 3252-3257.
28 Food and Drug Administration: FDA Drug Safety Communication: Update on the risk for serious bleeding events with the anticoagulant Pradaxa. In. Silver Springs, MD; Drug Safety Communications 2012
29 Southworth MR, Reichman ME, Unger EF. Dabigatran and postmarketing reports of bleeding. New England Journal of Medicine 2013; 368 (14) 1272-1274.
30 Avorn J. The promise of pharmacoepidemiology in helping clinicians assess drug risk. Circulation 2013; 128 (07) 745-748.
31 Dabigatran (Pradaxa), warfarin & GI bleed, intracerebral hemorrhage (Modular Program) [ http://www.mini-sentinel.org/work_products/Assessments/Mini-Sentinel_Modular-Program-Report_MSY3_MPR41_Dabigatran-Warfarin-GIH-ICH_Part-1.pdf ]
32 Psaty BM, Breckenridge AM. Mini-Sentinel and regulatory science-big data rendered fit and functional. New England Journal of Medicine 2014; 370 (23) 2165-2167.
33 Madigan D, Ryan PB, Schuemie M, Stang PE, Overhage JM, Hartzema AG, Suchard MA, Dumouchel W, Berlin JA. Evaluating the impact of database heterogeneity on observational study results. Am J Epidemiol 2013; 178 (04) 645-651.
34 Vaughan-Sarrazin MS, Wakefield B, Rosenthal GE. Mortality of Department of Veterans Affairs Patients Undergoing Coronary Revascularization in Private Sector Hospitals. Health Services Research 2007; 42 (05) 1802-1821.
35 Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE. Prevalence of diagnosed atrial fibrillation in adults: National implications for rhythm management and stroke prevention: the anticoagulation and risk factors in atrial fibrillation (atria) study. JAMA 2001; 285 (18) 2370-2375.
36 Nielsen KM, Foldspang A, Larsen ML, Gerdes LU, Rasmussen S, Faergeman O. Estimating the incidence of the acute coronary syndrome: data from a Danish cohort of 138290 persons. European Journal of Cardiovascular Prevention & Rehabilitation 2007; 14 (05) 608-614.
37 White RH. The Epidemiology of Venous Thromboembolism. Circulation 2003; 107 23 suppl 1 I4-I8.
38 Osterberg L, Blaschke T. Adherence to Medication. New England Journal of Medicine 2005; 353 (05) 487-497.
39 FitzHenry F, Doran J, Lobo B, Sullivan TM, Potts A, Feldott CC, Matheny ME, McCulloch G, Deppen S, Doulis J. Medication-error alerts for warfarin orders detected by a bar-code-assisted medication administration system. American Journal of Health-System Pharmacy 2011; 68 (05) 434-441.
40 Zhou L, Mahoney L, Shakurova A, Goss F, Chang F, Bates D, Rocha R. How many medication orders are entered through free-text in EHRs? A study on hypoglycemic agents American Medical Informatics Association Annual Meeting. Chicago, IL: 2012: 1079-1088.
41 FitzHenry F, Dietrich M, Littlejohn S, Gregory D, Matheny M, Ehrenfeld J, Wells N. Postoperative pain time from severe to mild: effect of frequent and multimodal interventions American Pain Society National Meeting 2013. New Orleans, LA: 2013
42 Cunningham PJ, Kohn L. Health plan switching: choice or circumstance?. Health Affairs 2000; 19 (03) 158-164.
43 Lavarreda SA, Gatchell M, Ponce N, Brown ER, Chia YJ. Switching health insurance and its effects on access to physician services. Medical Care 2008; 46 (10) 1055-1063.
44 Reed M. Why people change their health care providers. Data Bull (Cent Stud Health Syst Change) 2000; 16: 1-2.
45 Lederle F, Parenti C. Prescription drug costs as a reason for changing physicians. Journal of General Internal Medicine 1994; 9 (03) 162-163.
46 Walker J, Pan E, Johnston D, Adler-Milstein J, Bates DW, Middleton B. The value of health care information exchange and interoperability Health Affairs. 2005 Suppl Web Exclusives: W5–10, W15-18.
47 Ohno-Machado L, Agha Z, Bell DS, Dahm L, Day ME, Doctor JN, Gabriel D, Kahlon MK, Kim KK, Hogarth M, Matheny ME, Meeker D, Nebeker JR. pSCANNER team. pSCANNER: patient-centered scalable national network for effectiveness research. Journal of the American Medical Informatics Association 2014; 21 (04) 621-626.
48 European Medicines Agency: Guideline on good pharmacovigilance practices: Module VIII –Post authorisation safety studies (Rev 1). In.; London: United Kingdom: European Medicines Agency; 2013