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Electronic Medical Records and Quality of Cancer Care

  • Innovations in Information Technology in Cancer Medicine (RB Jones, Section Editor)
  • Published:
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Abstract

The implementation of electronic medical records (EMR) systems was mandated by the U.S. federal government in large part due to research indicating that difficulty accessing clinical data was one of the most common causes of preventable deaths. Several assumptions were implicit in this mandate, including the assumption that the implementation of EMR would indeed improve clinicians’ access to clinical data, that implementation of EMR would pose little to no risk to patients, and that the clinical benefit of improved access to clinical data would outweigh any risks that might arise. As detailed in this review, both formal research and extensive experiential observation have called all three assumptions into question. Specifically, as detailed below, there is clear evidence that EMR systems are associated with multiple specific risks to patients, whereas few, if any, scientifically rigorous outcomes-based studies have demonstrated that the potential benefits of EMR outweigh the known risks. In addition, there is currently little to no scientifically rigorous evidence that EMR systems constitute a cost-effective methodology for improving patient outcomes.

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References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

  1. •• Kohn LT, Corrigan JM, Donaldson MS. To Err is human: Building a safer healthcare system. Washington, DC: National Academy Press; 1999. p. 1–15. A substantial fraction of preventable inpatient deaths are attributable to processes and systems that predispose to medical errors.

    Google Scholar 

  2. Blumenthal D. Wiring the health system – origins and provisions of a new federal program. N Engl J Med. 2011;365:2323–9.

    Article  PubMed  CAS  Google Scholar 

  3. Burstin H. The journey to electronic performance measurement. Ann Intern Med. 2013;158:131–2.

    Article  PubMed  Google Scholar 

  4. Cimino JJ. Improving the electronic health record – are clinicians getting what they wished for? JAMA. 2013;309:991–2.

    Article  PubMed  CAS  Google Scholar 

  5. Crosson JC, Bazemore AW, Phillips RL. EHR implementation without meaningful use can lead to worse outcomes. Am Fam Physician. 2011;84:1220.

    PubMed  Google Scholar 

  6. DesRoches CM, Audet A-M, Painter M, Donelan K. Meeting meaningful use criteria and managing patient populations. Ann Intern Med. 2013;158:791–9.

    Article  PubMed  Google Scholar 

  7. Fackler JC. Stop the noise and get to the point. Crit Care Med. 2013;41:656–7.

    Google Scholar 

  8. Flodgren G, Eccles MP, Shepperd S, et al. An overview of reviews evaluating the effectiveness of financial incentives in changing healthcare professional behaviours and patient outcomes. Cochrane Database Syst Rev. 2011;CD009255:1–94.

    Google Scholar 

  9. Han YY, Carcillo JA, Venkataraman ST, et al. Unexpected increase in mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics. 2005;116:1506–12.

    Article  PubMed  Google Scholar 

  10. Jones SS, Heaton PS, Rudin RS, Schneider EC. Unraveling the IT productivity paradox – lessons for health care. N Engl J Med. 2012;366:2243–5.

    Article  PubMed  CAS  Google Scholar 

  11. •• Kern LM, Malhotra S, Barron Y, et al. Accuracy of electronically reported “meaningful use” clinical quality measures. Ann Intern Med. 2013;158:77–83. EMR systems generate quality metrics of highly questionable accuracy, which undermines both the validity and efficacy of EMR-based pay-for-performance programs.

    Article  PubMed  Google Scholar 

  12. Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medical errors. JAMA. 2005;293:1197–203.

    Article  PubMed  CAS  Google Scholar 

  13. Koppel R. Demanding utility from health information technology. Ann Intern Med. 2013;158:845–6.

    Article  PubMed  Google Scholar 

  14. Lin C-T, McKenzie M, Pell J, Caplan L. Health care provider satisfaction with a new electronic progress note format: SOAP vs APSO format [letter]. JAMA Intern Med. 2013;173:160–2.

    Article  PubMed  Google Scholar 

  15. Love JS, Simon SR, Jenter CA, et al. Are physicians’ perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use? J Am Med Inform Assoc. 2012;19:610–4.

    Article  PubMed  Google Scholar 

  16. Mandl KD, Kohane IS. Escaping the EHR trap – the future of health IT. N Engl J Med. 2012;366:2240–2.

    Article  PubMed  CAS  Google Scholar 

  17. Mangalmurti SS, Murtagh L, Mello MM. Medical malpractice liability in the age of electronic health records. N Engl J Med. 2010;363:2060–7.

    Article  PubMed  CAS  Google Scholar 

  18. Parsons A, McCullough C, Wang J, Shih S. Validity of electronic health record-derived quality measurement for performance monitoring. J Am Med Inform Assoc. 2012;19:604–9.

    Article  PubMed  Google Scholar 

  19. Rosof B. The importance of accurate data in quality-of-care measurement. Ann Intern Med. 2012;157:379–80.

    Article  PubMed  Google Scholar 

  20. Thornton JD, Schold JD, Venkateshaiah L, Lander B. Prevalence of copied information by attendings and residents in critical care progress notes. Crit Care Med. 2013;41:382–8.

    Article  PubMed  Google Scholar 

  21. Villier JA. Dispensing of electronically discontinued medications [letter]. Ann Intern Med. 2013;158:571.

    Article  PubMed  Google Scholar 

  22. Yasnoff WA, Sweeney L, Shortliffe EH. Putting health IT on the path to success. JAMA. 2013;309:989–90.

    Article  PubMed  CAS  Google Scholar 

  23. Needleman J, Buerhaus P, Pankratz VS, et al. Nurse staffing and inpatient hospital mortality. N Engl J Med. 2011;364:1037–45.

    Article  PubMed  CAS  Google Scholar 

  24. Baker DW, Qaseem A. Evidence-based performance measures: preventing unintended consequences of quality measurement. Ann Intern Med 2011;155:638–40.

    Google Scholar 

  25. Bray BD. Hospital pay for performance in England [letter]. N Engl J Med. 2013;368:968.

    Article  PubMed  Google Scholar 

  26. Farmer SA, Black B, Bonow RO. Tension between quality measurement, public quality reporting, and pay for performance. JAMA. 2013;309:349–50.

    Article  PubMed  CAS  Google Scholar 

  27. •• Fonarow GC, Pan W, Saver JL, et al. Comparison of 30-day mortality models for profiling hospital performance in acute ischemic stroke with vs without adjustment for stroke severity. JAMA. 2012;308:257–64.

  28. Ayanian JZ, Kohler BA, Abe T, Epstein AM. The relationship between health insurance coverage and clinical outcomes among women with breast cancer. N Engl J Med. 1993;329:326–31.

    Article  PubMed  CAS  Google Scholar 

  29. Baker KS, Davies SM, Majhail NS, et al. Race and socioeconomic status influence outcomes of unrelated donor hematopoietic cell transplantation. Biol Blood Marrow Transplant. 2009;15:1543–54.

    Google Scholar 

  30. Berkman ND, Sheridan SL, Donahue KE, et al. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155:97–107.

    Google Scholar 

  31. Greiver M, Keshavjee K, Martin K, Aliarzadeh B. Who are your patients with diabetes? Can Fam Physician. 2012;58:804.

    PubMed  Google Scholar 

  32. Peterson PN, Shetterly SM, Clarke CL, et al. Health literacy and outcomes among patients with heart failure. JAMA. 2011;305:1695–701.

    Google Scholar 

  33. Roetzheim RG, Gonzalez EC, Ferrante JM, Pal N, et al. Effects of health insurance and race on breast carcinoma treatments and outcomes. Cancer. 2000;89:2202–13.

    Google Scholar 

  34. Selby GB, Ali LI, Carter TH, et al. The influence of health insurance on outcomes of related-donor hematopoietic stem cell transplantation for AML and CML [letter]. Biol Blood Marrow Transplant. 2001;7:576–7.

    Article  PubMed  CAS  Google Scholar 

  35. Siegel R, Ward E, Brawley O, Jemal A. Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin. 2011;61:212–36.

    Article  PubMed  Google Scholar 

  36. Werner RM. Will using medicare data to rate doctors benefit patients? Ann Intern Med. 2012;156:532–3.

    Article  PubMed  Google Scholar 

  37. Glickman SW, Ou F-S, DeLong ER, et al. Pay for performance, quality of care, and outcomes in acute myocardial infarction. JAMA. 2007;297:2373–80.

    Article  PubMed  CAS  Google Scholar 

  38. Houle SKD, McAlister FA, Jackevicius CA, et al. Does performance-based remuneration for individual health care practitioners affect patient care? Ann Intern Med. 2012;157:889–99.

    Article  PubMed  Google Scholar 

  39. Jha AK, Joynt KE, Orav EJ, Epstein AM. The long-term effect of premier pay for performance on patient outcomes. N Engl J Med 2012;366:1606–15.

    Google Scholar 

  40. Jha AK. Time to get serious about pay for performance. JAMA. 2013;309:347–8.

    Article  PubMed  CAS  Google Scholar 

  41. Landrigan CP, Parry GJ, Bones CB, et al. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;363:2124–34.

    Article  PubMed  CAS  Google Scholar 

  42. Lee GM, Kleinman K, Soumerai SB, et al. Effect of nonpayment for preventable infections in U.S. hospitals. N Engl J Med. 2012;367:1428–37.

    Article  PubMed  CAS  Google Scholar 

  43. Tu JV, Donovan LR, Lee DS, et al. Effectiveness of public report cards for improving the quality of cardiac care. JAMA. 2009;302:2330–7.

    Article  PubMed  CAS  Google Scholar 

  44. Burstein HJ. A “shot heard ‘round the world” on cancer drug costs? J Natl Compr Cancer Netw. 2012;10:1315–6.

    Google Scholar 

  45. Goss E, Lopez AM, Brown CL, et al. American Society of Clinical Oncology policy statement: disparities in cancer care. J Clin Oncol. 2009;27:2881–5.

    Article  PubMed  Google Scholar 

  46. Kantarjian H. The price of drugs for chronic myelogenous leukemia (CML) is a reflection of unsustainable prices of cancer drugs: from the perspective of a large group of CML experts. Blood. 2013;121:4439–42.

    Article  Google Scholar 

  47. Kumar P, Moy B. Cancer health disparities and the cost of cancer care: payment model issues. J Natl Compr Cancer Netw. 2013;11:633–6.

    Google Scholar 

  48. Meropol NJ, Schulman KA. Cost of cancer care: issues and implications. J Clin Oncol. 2007;25:180–6.

    Article  PubMed  Google Scholar 

  49. Snyder L. American College of Physicians Ethics Manual. Ann Intern Med. 2012;156:73–104.

    Article  PubMed  Google Scholar 

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Thomas R. Klumpp declares that he has no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

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Correspondence to Thomas R. Klumpp.

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Klumpp, T.R. Electronic Medical Records and Quality of Cancer Care. Curr Oncol Rep 15, 588–594 (2013). https://doi.org/10.1007/s11912-013-0347-z

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