Caveats for the use of operational electronic health record data in comparative effectiveness research

Medical Care

Volumn 51, Issue 8 SUPPL.3, 2013, Pages

  Hersh, William R ^a   Weiner, Mark G ^b   Embi, Peter J ^c   Logan, Judith R ^a   Payne, Philip R O ^c   Bernstam, Elmer V ^d   Lehmann, Harold P ^e   Hripcsak, George ^f   Hartzog, Timothy H ^g   Cimino, James J ^h   Saltz, Joel H ⁱ  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^b ASTRAZENECA   (United Kingdom)

^c THE OHIO STATE UNIVERSITY WEXNER MEDICAL CENTER   (United States)

^d UNIVERSITY OF TEXAS   (United States)

^e JOHNS HOPKINS UNIVERSITY   (United States)

^f Och Spine at New York Presbyterian Hospitals   (United States)

^g MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^h NATIONAL INSTITUTES OF HEALTH   (United States)

ⁱ EMORY UNIVERSITY   (United States)

Author keywords

biomedical informatics; clinical data; coded (claims) data; comparative effectiveness research; electronic health records

Indexed keywords

ARTICLE; CLINICAL RESEARCH; CLINICAL STUDY; COMPARATIVE EFFECTIVENESS; DATA ANALYSIS; ELECTRONIC MEDICAL RECORD; HEALTH CARE; HUMAN; MEDICAL INFORMATICS; PRIORITY JOURNAL; TOTAL QUALITY MANAGEMENT;

COMPARATIVE EFFECTIVENESS RESEARCH; DATA COLLECTION; DATA INTERPRETATION, STATISTICAL; ELECTRONIC HEALTH RECORDS; HUMANS; INSURANCE CLAIM REVIEW; RESEARCH DESIGN;

EID: 84879885267     PISSN: 00257079     EISSN: 15371948     Source Type: Journal    
DOI: 10.1097/MLR.0b013e31829b1dbd     Document Type: Article

References (72)

1. Blumenthal D, Tavenner M. The "meaningful use" regulation for electronic health records. N Engl J Med. 2010;363:501-504.
2. Safran C, Bloomrosen M, Hammond WE, et al. Toward a national framework for the secondary use of health data: an American Medical Informatics Association white paper. J AmMed Inform Assoc. 2007;14:1-9. (Pubitemid 44909487)
3. Weiner MG, Embi PJ. Toward reuse of clinical data for research and quality improvement: the end of the beginning. Ann Intern Med. 2009;151:359-360.
4. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2012;20:117-121.
5. Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med. 2010;2:57cm29. Available at: http://stm. sciencemag.org/content/2/57/57cm29.full. Accessed May 28, 2013.
6. Smith M, Saunders R, Stuckhardt L, et al. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: National Academies Press; 2012.
7. Sox HC, Goodman SN. The methods of comparative effectiveness research. Annu Rev Public Health. 2012;33:425-445.
8. Collins FS. Reengineering translational science: the time is right. Sci Transl Med. 2011;3:90cm17. Available at: http://stm.sciencemag.org/content/3/90/ 90cm17.full. Accessed May 28, 2013.
9. MacKenzie SL, Wyatt MC, Schuff R, et al. Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey. J Am Med Inform Assoc. 2012;19(e1):e119-e124.
10. Rea S, Pathak J, Savova G, et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project. J Biomed Inform. 2012;45:763-771.
11. McCarty CA, Chisholm RL, Chute CG, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Genomics. 2010;4:13. Available at: http://www.biomedcentral.com/1755-8794/4/13. Accessed May 28, 2013.
12. Denny JC. In: Kann M, Lewitter F, eds. Mining Electronic Health Records in the Genomics Era, in PLOS Computational Biology: Translational Bioinformatics. San Francisco, CA: Public Library of Science; 2012.
13. Denny JC, Ritchie MD, Crawford DC, et al. Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation. 2010;122:2016-2021.
14. Kullo LJ, Ding K, Jouni H, et al. A genome-wide association study of red blood cell traits using the electronic medical record. PLoS One. 2010;5:e13011.
15. Crosslin DR, McDavid A, Weston N, et al. Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network. Hum Genet. 2012;131:639-652.
16. Denny JC, Crawford DC, Ritchie MD, et al. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. Am J Hum Genet. 2011;89:529-542.
17. Hornbrook MC, Hart G, Ellis JL, et al. Building a virtual cancer research organization. J Natl Cancer Inst Monogr. 2005;35:12-25.
18. Hillier TA, Pedula KL, Schmidt MM, et al. Childhood obesity and metabolic imprinting: the ongoing effects of maternal hyperglycemia. Diabetes Care. 2007;30:2287-2292. (Pubitemid 47359033)
19. Tannen RL, Weiner MG, Xie D, et al. A simulation using data from a primary care practice database closely replicated the Women's Health Initiative trial. J Clin Epidemiol. 2007;60:686-695. (Pubitemid 46901002)
20. Weiner MG, Barnhart K, Xie D, et al. Hormone therapy and coronary heart disease in young women. Menopause. 2008;15:86-93.
21. Tannen RL, Weiner MG, Xie D. Replicated studies of two randomized trials of angiotensin-converting enzyme inhibitors: further empiric validation of the'prior event rate ratio' to adjust for unmeasured confounding by indication. Pharmacoepidemiol Drug Saf. 2008;17: 671-685.
22. Tannen RL, Weiner MG, Xie D. Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings. BMJ. 2009;338:b81. Available at: http://www.bmj.com/cgi/content/full/338/jan27-1/b81. Accessed May 28, 2013.
23. Danaei G, Rodriguez LA, Cantero OF, et al. Observational data for comparative effectiveness research: An emulation of randomised trials of statins and primary prevention of coronary heart disease. Stat Methods Med Res. 2011;22:70-96.
24. Ryan PB, Madigan D, Stang SE, et al. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med. 2012;31: 4401-4415.
25. Edinger T, Cohen AM, Bedrick S, et al. Barriers to Retrieving Patient Information From Electronic Health Record Data: Failure Analysis From the TREC Medical Records Track. Chicago, IL: AMIA 2012 Annual Symposium; 2012.
26. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2012;20:144-151.
27. Overhage JM, Overhage LM. Sensible use of observational clinical data. Stat Methods Med Res. 2011;22:7-13.
28. Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58:323-337. (Pubitemid 40601980)
29. de Lusignan S, vanWeel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Fam Pract. 2005;23:253-263.
30. Brennan L, Watson M, Klaber R, et al. The importance of knowing context of hospital episode statistics when reconfiguring the NHS. BMJ. 2012;344:e2432. Available at: http://www.bmj.com/content/344/bmj.e2432. Accessed May 28, 2013.
31. Roebuck C. The Importance of Knowing Context of Hospital Episode Statistics When Reconfiguring the NHS. BMJ. 2012;344. Available at: http://www.bmj.com/content/344/bmj.e2432/rr/578977. Accessed May 28, 2013.
32. Green SM. Congruence of disposition after emergency department intubation in the National Hospital Ambulatory Medical Care Survey. Ann Emerg Med. 2013;61:423-426.
33. Chan KS, Fowles JB, Weiner JP. Electronic health records and reliability and validity of quality measures: a review of the literature. Med Care Res Rev. 2010;67:503-527.
34. Savitz L, Bayley KB, Masica A, et al. Challenges in using electronic health record data for CER: experience of four learning organizations. J Am Med Inform Assoc. 2012. (In review).
35. Bourgeois FC, Olson KL, Mandl KD. Patients treated at multiple acute health care facilities: quantifying information fragmentation. Arch Intern Med. 2010;170:1989-1995.
36. Finnell JT, Overhage JM, Grannis S. All Health Care is not Local: An Evaluation of the Distribution of Emergency Department Care Delivered in Indiana. Washington, DC: AMIA Annual Symposium Proceedings; 2011:409-416.
37. Wei WQ, Leibson CL, Ransom JE, et al. Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus. J Am Med Inform Assoc. 2012;19:219-224.
38. Wei WQ, Leibson CL, Ransom JE, et al. The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects. Int J Med Inf. 2013;82:239-247.
39. Thiru K, Hassey A, Sullivan F. Systematic review of scope and quality of electronic patient record data in primary care. BMJ. 2003;326:1070.
40. Dean BB, Lam J, Natoli JL, et al. Review: use of electronic medical records for health outcomes research: a literature review. Med Care Res Rev. 2009;66:611-638.
41. Botsis T, Hartvigsen G, Chen F, et al. Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. San Francisco, CA: AMIA Summits on Translational Science Proceedings. Available at: http://www.ncbi.nlm.nih.gov/ pmc/articles/PMC3041534/; 2010. Accessed May 28, 2013.
42. Lau EC, Mowat FS, Kelsh MA, et al. Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data. Clin Epidemiol. 2011;3:259-272.
43. Parsons A, McCullough C, Wang J, et al. Validity of electronic health record-derived quality measurement for performance monitoring. J Am Med Inform Assoc. 2012;19:604-609.
44. Kahn MG, Ranade D. The impact of electronic medical records data sources on an adverse drug event quality measure. J Am Med Inform Assoc. 2010;17:185-191.
45. Benin AL, Fenick A, Herrin J, et al. How good are the data? Feasible approach to validation of metrics of quality derived from an outpatient electronic health record. Am J Med Qual. 2011;26:441-451.
46. Hripcsak G, Knirsch C, Zhou L, et al. Bias associated with mining electronic health records. J Biomed Discov Collab. 2011;6:48-52.
47. Available at: http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/jbdc/ article/view/3581/2997. Accessed May 28, 2013.
48. Zhang Z, Sun J. Interval censoring. Stat Methods Med Res. 2010;19: 53-70.
49. O'Malley KJ, Cook KF, Price MD, et al. Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005;40:1620-1639. (Pubitemid 41434351)
50. Jollis JG, Ancukiewicz M, DeLong ER, et al. Discordance of databases designed for claims payment versus clinical information systems: implications for outcomes research. Ann Intern Med. 1993;119:844-850. (Pubitemid 24012651)
51. Iezzoni LI. Assessing quality using administrative data. Ann Intern Med. 1997;127:666-674.
52. Lawson EH, Louie R, Zingmond DS, et al. A comparison of clinical registry versus administrative claims data for reporting of 30-day surgical complications. Ann Surg. 2012;256:973-981.
53. Benin AL, Vitkauskas G, Thornquist E, et al. Validity of using an electronic medical record for assessing quality of care in an outpatient setting. Med Care. 2005;43:691-698. (Pubitemid 40993764)
54. Gorelick MH, Knight S, Alessandrini EA, et al. Lack of agreement in pediatric emergency department discharge diagnoses from clinical and administrative data sources. Acad Emerg Med. 2007;14:646-652. (Pubitemid 46929088)
55. Harris SB, Glazier RH, Tompkins JW, et al. Investigating concordance in diabetes diagnosis between primary care charts (electronic medical records) and health administrative data: a retrospective cohort study. BMC Health Serv Res. 2010;10:347. Available at: http://www.biomed central.com/1472-6963/10/347. Accessed May 28, 2013.
56. Meddings JA, Reichert H, Rogers MA, et al. Effect of nonpayment for hospital-acquired, catheter-associated urinary tract infection: a statewide analysis. Ann Intern Med. 2012;157:305-312.
57. Bernstam EV, Herskovic JR, Reeder P, et al. Oncology research using electronic medical record data. J Clin Oncol. 2010;28(suppl). abstr e16501. Available at: http://www.asco.org/ascov2/Meetings/Ab stracts? &vmview = abst-detail-view&confID = 74&abstractID = 42963. Accessed May 28, 2013.
58. Wahl PM, Rodgers K, Schneeweiss S, et al. Validation of claims-based diagnostic and procedure codes for cardiovascular and gastrointestinal serious adverse events in a commercially-insured population. Pharmacoepidemiol Drug Saf. 2010;19:596-603.
59. Nordstrom BL, Whyte JL, Stolar M, et al. Identification of metastatic cancer in claims data. Pharmacoepidemiol Drug Saf. 2012;21(suppl 2): 21-28.
60. Bayliss EA, Ellis JL, Shoup JA, et al. Association of patient-centered outcomes with patient-reported and ICD-9-based morbidity measures. Ann Fam Med. 2012;10:126-133.
61. Lindenauer PK, Lagu T, Shieh MS, et al. Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003-2009. JAMA. 2012;307:1405-1413.
62. Devoe JE, Gold R, McIntire P, et al. Electronic health records vs Medicaid claims: completeness of diabetes preventive care data in community health centers. Ann Fam Med. 2011;9:351-358.
63. Hu G, Baker SP. An explanation for the recent increase in the fall death rate among older Americans: a subgroup analysis. Public Health Rep. 2012;127:275-281.
64. Cimino JJ. Desiderata for controlled medical vocabularies in the twentyfirst century. Methods Inf Med. 1998;37:394-403. (Pubitemid 29008630)
65. Yu AC, Cimino JJ. A comparison of two methods for retrieving ICD-9- CM data: the effect of using an ontology-based method for handling terminology changes. J Biomed Inform. 2011;44:289-298.
66. Hripcsak G, Friedman C, Anderson PO, et al. Unlocking clinical data from narrative reports: a study of natural language processing. Ann Intern Med. 1995;122:681-688.
67. D'Amore JD, Sittig DF, Ness RB. How the continuity of care document can advance medical research and public health. Am J Public Health. 2012;102:e1-e4.
68. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18: 544-551.
69. Stanfill MH, Williams M, Fenton SH, et al. A systematic literature review of automated clinical coding and classification systems. J Am Med Inform Assoc. 2010;17:646-651.
70. Hersh W. Evaluation of biomedical text mining systems: lessons learned from information retrieval. Brief Bioinform. 2005;6:344-356. (Pubitemid 43091940)
71. Seiler KP, Bodycombe NE, Hawkins T, et al. Master data management: getting your house in order. Comb Chem High Throughput Screen. 2011;14:749-756.
72. de Lusignan S, Liaw ST, Krause P, et al. Key concepts to assess the readiness of data for international research: data quality, lineage and provenance, extraction and processing errors, traceability, and curation. In: Haux R, Kulikowski CA, Geissbuhler A, eds. IMIA Yearbook of Medical Informatics 2011. Stuttgart, Germany: Schattauer; 2011: 112-120. Science Science Science Science Science Science