Electronic health records (EHRs) are touted as a key strategy for reducing costs and improving quality in our health care system, which are both urgent goals. The shortcomings of our current system are perhaps most apparent in the provision of preventive services. Contrasted with treatment services focused on addressing symptomatic disease, preventive services aim to forestall or decrease future adverse health outcomes and thus improve quality of life, as well as life span. Prevention can potentially stem the tide of high-cost care by reducing later occurrences of expensive and life-threatening diseases. However, numerous barriers inhibit the implementation of proven preventive interventions. Electronic health records, particularly systems providing clinical decision support (CDS), offer an appealing solution to the status quo whereby busy clinicians all too often lose sight of longitudinal prevention targets while prioritizing immediate treatment goals. The US government has thus allocated billions of dollars in the past 3 years to invest in health information technologies to improve health care quality, decrease morbidity and mortality, and reduce costs.1 The study by Holbrook and colleagues,2 however, contributes to a growing body of evidence suggesting that CDS is not the “magic bullet” many proponents envision. While numerous reports from flagship institutions indicate that CDS can have a measurable effect on local health care quality,3,4 most studies with a broader focus suggest that CDS has a limited effect on improving health in more conventional settings.5,6 Furthermore, our analysis of the effects of CDS use on outpatient care quality at a national level in the United States has provided discouraging results.7 While it seems logical that providing physicians with high-quality, longitudinal, and actionable information about their patients should contribute to better care, numerous barriers remain, such as ineffective reimbursement, limited physician skills and knowledge, insufficient patient engagement, a lack of physician time, and a poorly integrated work flow.8 This pragmatic randomized controlled trial by Holbrook et al2 among family physicians in Ontario, Canada, fills important gaps in our understanding of CDS effectiveness. The intervention focused on cardiovascular risk factor management for moderate- to high-risk patients in family physician practices. A color-coded dashboard report within the clinicians' EHR depicted vascular disease risk factors for intervention patients. Holbrook and colleagues simultaneously assessed improvement in risk factor assessment (whether physicians addressed risk factors) and improvement in intermediate patient outcomes (whether vascular disease risk factors changed). The year-long study allowed sufficient time for physicians to adapt to the CDS tool and for patients to modify their risk factors. The intervention led to increased provider monitoring of patient risk factors; however, intervention patients showed no significant improvement in risk factor profiles over control patients. The study lacked statistical power to compare more definitive outcomes (eg, cardiac events). There are important limitations to this study. The intervention was conducted in 2005, a relatively distant time given the rapid changes in health information technology and electronic decision-support tools. The author’s use of the term electronic medical record (ERM), a designation being gradually replaced by EHR, reflects the limited functionality of their system at the time of their study. In addition, the Canadian study's results may not transfer to countries with different health systems. The quantification of process and risk factor improvement relied on simple summation of risk factors, which does not account for interrelated and nonequivalent risks associated with these factors. For example, smoking cessation was counted as numerically equivalent to increased fruit and vegetable intake. An alternative method for risk measurement would be the use of a validated risk scoring system, such as the Framingham Risk Scores, although those tools also have important limitations.9 In addition, several risk factors were reasonably well managed at baseline, so their inclusion added little to the results. Certain prevention goals, such as smoking cessation, may simply be difficult to attain in the context of a busy primary care practice. Holbrook and colleagues3 observed sizable improvements in the control group, which may represent secular trends or the impact of the initial risk factor screening conducted by telephone. This finding reinforces the importance of a control arm in trials of health care process changes. Despite these limitations, this study suggests that providing physicians with accessible and actionable information does not directly improve patient outcomes. The data suggest that health care processes can improve without a corresponding improvement in outcomes. This does not suggest that CDS should be abandoned as a health care improvement tool; rather, it is only one of many existing strategies to improve patient care. Furthermore, it is crucial that enthusiasm for CDS not detract from attention to other strategies, such as reimbursement reform and community-based health promotion. We need to fundamentally reenvision how health care is provided. It is neither clear that physicians can or should be tasked with managing risk factors longitudinally nor clear that a computerized algorithm can easily capture the complexity of risk factor management. In particular, we may do better with a team approach involving health educators, dietitians, physicians, and patients partnered with a public health approach of increasing the availability of healthy foods, decreasing the availability of unhealthy ones, and increasing the opportunities for walking, such as adding sidewalks to neighborhoods lacking them. Addressing prevention issues at a community level may help address factors even further upstream from patients' visits to their doctors' offices.10 We need to realign incentives in our antiquated fee-for-service reimbursement system to reward the complexity and importance of performing prevention activities. As we seek to develop efficacious and cost-effective strategies to improve care, health information technology should be part of a comprehensive solution, but its utility and costs deserve ongoing scrutiny. It is crucial to standardize EHR tools to improve interoperability, provide better training to clinicians as technologies evolve, and proactively plan for changes in work flow that result from CDS use. Health information technologies may offer important disease prevention tools; however, improving the quality of care in practice requires an evidence-based approach that considers the potential costs and benefits of CDS and places them in the context of other efforts to improve quality, reduce costs, and achieve better patient outcomes. Back to top Article Information Correspondence: Dr Stafford, Program on Prevention Outcomes and Practices, Stanford Prevention Research Center, Medical School Office Bldg, Room X312, Stanford, CA 94305 (firstname.lastname@example.org). Financial Disclosure: None reported. References 1. United States Congress, HR1: American Recovery and Reinvestment Act of 2009. 2009. http://thomas.loc.gov/cgi-bin/bdquery/z?d111:H.R.1:. Accessed August 25, 2011 2. Holbrook A, Pullenayegum E, Thabane L, et al. Shared electronic vascular risk decision support in primary care: Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness (COMPETE III) randomized trial. Arch Intern Med. 2011;171(19):1736-1744Google ScholarCrossref 3. Dexter PR, Wolinsky FD, Gramelspacher GP, et al. Effectiveness of computer-generated reminders for increasing discussions about advance directives and completion of advance directive forms: a randomized, controlled trial. Ann Intern Med. 1998;128(2):102-1109441569PubMedGoogle Scholar 4. Overhage JM, Tierney WM, Zhou XH, McDonald CJ. A randomized trial of “corollary orders” to prevent errors of omission. J Am Med Inform Assoc. 1997;4(5):364-3759292842PubMedGoogle ScholarCrossref 5. Black AD, Car J, Pagliari C, et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 2011;8(1):e100038721267058PubMedGoogle ScholarCrossref 6. Zhou L, Soran CS, Jenter CA, et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc. 2009;16(4):457-46419390094PubMedGoogle ScholarCrossref 7. Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med. 2011;171(10):897-90321263077PubMedGoogle ScholarCrossref 8. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581-62915595944PubMedGoogle ScholarCrossref 9. Brindle P, Emberson J, Lampe F, et al. Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study. BMJ. 2003;327(7426):1267-127014644971PubMedGoogle ScholarCrossref 10. Ma J, Berra K, Haskell WL, et al. Case management to reduce risk of cardiovascular disease in a county health care system. Arch Intern Med. 2009;169(21):1988-199519933961PubMedGoogle ScholarCrossref
Archives of Internal Medicine – American Medical Association
Published: Oct 24, 2011
Keywords: primary health care
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