Electronic Health Records and Quality of Care: Mixed ...

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In the United States (US) and many other countries, Electronic Health Records ... made compulsory by the American Recovery and Reinvestment Act (2009) that ...
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Electronic Health Records and Quality of Care: Mixed Results and Emerging Debates Achieving Meaningful Use in Research with Information Technology Column by Maxim Topaz, RN, MA Doctoral Student and Kathryn H. Bowles, PhD, RN, FAAN Associate Prof essor, University of Pennsylvania School of Nursing This column was made possible by an educational grant from Chamberlain College of Nursing

CITATION Topaz, M. & Bowles, K. H. (February, 2012). Electronic Health Records and Quality of Care: Mixed Results and Emerging Debates. Achieving Meaningf ul Use in Research with Inf ormation Technology Column. Online Journal of Nursing Informatics (OJNI), 16 (1). Available at http://ojni.org/issues/?p=1262

COLUMN

In the United States (US) and many other countries, Electronic Health Records (EHRs) have been proposed as a sustainable solution f or improving the quality of medical care. Recently, the implementation of EHRs was made compulsory by the American Recovery and Reinvestment Act (2009) that allocated 27 billion dollars to the implementation of EHRs in clinical settings and introduced the concept of Meaningful Use (Blumenthal & Tavenner, 2010; Hillestad et al., 2005). By the end of 2015, healthcare providers across the US are expected to prove they are meaningful users of “certif ied EHR technology in ways that can be signif icantly measured in quality and in quantity” (US Department of Health and Human services, 2011), otherwise, be f inancially penalized when providing services f or Medicare and Medicaid clients. On the other hand, providers who do meaningf ully implement EHRs in their practices are eligible f or f inancial incentives of as much as $44,000 (through Medicare) and $63,750 (through Medicaid) per clinician (Blumenthal & Tavenner, 2010).

Based on the compelling assumption “EHRs will improve caregivers’ decisions and patients’ outcomes” (Blumenthal & Tavenner, 2010. p 501), these legislative acts and financial incentives create a strong urge to implement EHRs in clinical settings across the US. Surprisingly, mixed evidence exists about the effect of EHRs on the actual quality of care. This editorial briefly reviews some of the shortcomings related to the emerging body of research evaluating the impact of EHRs on the quality of ambulatory care. Suggestions for future research are also provide

Based on the f indings of several recent studies examining the impact of EHRs on quality of care, several authors have raised concerns about the ability of health inf ormation technology to improve the quality of outpatient care (Linder, Ma, Bates, Middleton, & Staf f ord, 2007; Romano & Staf f ord, 2011). Although the results of these studies have inf luenced the policy and scientif ic discourses (Classen & Bates, 2011; Radecki & Sittig, 2011; Shih, McCullough, Wang, Singer, & Parsons, 2011), they also provoked almost immediate controversy among other researchers who attempted to understand the validity and generalizability of these mixed f indings (McDonald & Abhyankar, 2011; Mohan & Hersh, 2011; Oetgen, Mullen, & Mirro, 2011). T he debate centers on study design f laws that weaken the credibility of the study f inding One example is a critique of a recent study examining the impact of EHRs and Clinical Decision Support (CDS) on quality of care (Romano & Staf f ord, 2011). T he investigators analyzed a nationally representative sample of ambulatory patients’ visits during 2005-2007 (N=50,554). T he data was taken f rom the National Ambulatory Medical Care Survey conducted by the National Center f or Health Statistics (CDC, 2011). T he investigators f ound only two of the 20 assessed Quality of Care Indicators (CIs) were greater in EHR-assisted visits or EHR- and CDS-assisted visits (“diet counseling in high-risk adults” and “lack of routine electrocardiographic ordering in low-risk patients”) than in non-EHR-assisted visits. T he authors concluded “these results raise concerns about the ability of health inf ormation technology to f undamentally alter outpatient care quality” (p. 897). However, others questioned some of the study methods. McDonald and Abhyankar (2011) and Mohan and Hersh (2011) expressed concern about how the investigators def ined the presence of an EHR. T he study respondents were simply asked, if they have an EHR, and does it include CDS. T his approach weakens the study because a positive response to this question does not provide any inf ormation on the characteristics and application of the EHRs or CDS. Because of the lack of standards in construction and f unctionality of EHRs and CDS, these tools may dif f er signif icantly f rom one eligible healthcare provider to another and across dif f erent clinical settings. For example, some EHRs include advanced CDS tools allowing clinicians to track detailed patient inf ormation in real time while others are used only f or billing purposes. T he lack of detailed inf ormation on EHRs and CDS used by the reporting eligible health care providers limits the ability of the study to make generalizable conclusions (McDonald & Abhyankar, 2011; Mohan & Hersh, 2011). T he second signif icant study limitation lies in its measurement of the quality f or care (McDonald & Abhyankar, 2011; Mohan & Hersh, 2011). Romano and Staf f ord (2011) examined process quality indicators (QIs) (f or example: “ACE inhibitors use f or CHF”) that were computed as the percentage of applicable visits receiving appropriate care. However, the National Ambulatory Medical Care Survey included only three diagnosis and three reasons to visit; theref ore, other comorbidities cannot be accounted f or, neither in severity adjustment nor in contraindications f or the suggested treatment. For example, according to one of the QIs, all the patients with CHF, excluding the ones with hyperkalemia and angioedema, were supposed to receive ACE inhibitors, otherwise the quality of care f or this indicator was not considered to be f ully achieved. It is not clear how researchers learned of these comorbid conditions if only three diagnosis and three reasons f or visits were captured by the survey. Moreover, the survey included inf ormation f or only eight drugs and ACE inhibitors might be number nine on the list, thus it would not have been included. Also, several other medical conditions traditionally considered as contraindications to ACE inhibitors were not listed as the exclusion criteria f or this QI (f or example renal artery stenosis or hypersensitivity to the drug) (Bicket, 2002). Further, it is not clear how EHRs without CDS tools are supposed to encourage clinicians to increase the quality of their clinical processes. By def inition, EHRs serve as a repository database f or storage and representation of clinical data, theref ore the lack of the correlation between these QIs and EHRs is not surprising but rather expected. On the other hand, outcomes other than the assessed QIs (f or example readmission rates) may have improved in EHR visits but they were not evaluated in these studies. Finally, the National Ambulatory Medical Care Survey is a cross-sectional survey not designed to assess EHRs and theref ore the ability to claim EHRs, or CDSs, are not producing better quality care (causal relationships) is very limited (McDonald & Abhyankar, 2011; Mohan & Hersh, 2011).

In contrast, a smaller study that allowed f or more f lexible and comprehensive QIs when examining the impact of CDS tools on the quality of care f ound a signif icant improvement in similar process measures (Persell et al., 2011). In this study, the EHR allowed providers to enter patient and medical reasons f or not f ollowing CDS guidelines and recommendations presented by the QIs (i.e., exceptions) as part of routine workf low. T hese exceptions were f urther excluded f rom the f inal quality estimation. T heref ore, a signif icant number of patients, sometimes up to 7.4% of the total number, who were not treated according to the guideline suggested f or them were excluded f rom the f inal quality calculation. Adding this f lexibility to exclude patients is actually a desirable option because clinicians and patients have a legitimate right to agree or ref use certain treatment f or a good reason and it should not af f ect the QIs. In this study, clinicians who identif ied a signif icant reason (other than stated in direct contradictions to the drug or procedure included in the guideline) not to prescribe were able to do so without being penalized f or the diminished adherence to the QIs. For example, prescribing cancer screening to a f rail elderly person might cause more suf f ering and harm than benef its. It is important to mention all exceptions in this study were assessed by a group of peer reviewers and most of them were f ound legitimate (Persell et al., 2010). Additionally, clinicians involved in this project were explicitly inf ormed about the quality goals and they were receiving monthly notices with their score on the suggested QIs. To conclude, the ef f ect of EHRs (and CDSs) on the quality of care is yet to be comprehensively explored. To date, most of the research examining national samples of ambulatory practices is limited by how QIs are def ined, measured and the quality of the data sources. Moreover, most of the nationwide studies examine outcomes related to processes only. We suggest f urther research in this f ield should be based on sound conceptual or theoretical f rameworks that explicate the logical relationships among the database elements, the EHR or CDS intervention components, and the outcomes of interest. Hopef ully, this will be possible in the near f uture as the implementation of EHRs across most healthcare settings in the US is supposed to happen by 2015 (US Department of Health and Human services, 2011). Additionally, QIs analyzed in f uture studies should be more comprehensive and f lexible, ref lecting the complex picture of healthcare services. Given this complexity, mixed method studies are also suggested to f ully capture how the EHR or CDS is used to inf luence quality of care. Finally, a mix of structure, process, and outcome components should be included in examination of the ef f ects of EHRs on quality of care to f ully understand the interplay of these variables.

Ref erences Bicket, D. (2002). Using ACE inhibitors appropriately. American Family Physician, 66(3), 461-468. Blumenthal, D., & Tavenner, M. (2010). “T heMeaningf ul Use” regulation f or electronic health records. New England Journal of Medicine, 363(6), 501-504. doi:10.1056/NEJMp1006114 CDC. (2011). The National Ambulatory Medical Care Survey (NAMCS). Retrieved f rom http://www.cdc.gov/nchs/ahcd.htm Classen, D. C., & Bates, D. W. (2011). Finding the meaning in “Meaningf ul Use.” New England Journal of Medicine, 365(9), 855-858. Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., & Taylor, R. (2005). Can electronic medical record systems transf orm health care? Potential health benef its, savings, and costs. Health Affairs, 24(5), 1103-1117. doi:10.1377/hlthaf f .24.5.1103 Linder, J. A., Ma, J., Bates, D. W., Middleton, B., & Staf f ord, R. S. (2007). Electronic health record use and the quality of ambulatory care in the United States. Archives of Internal Medicine, 167(13), 1400-1405. doi:10.1001/archinte.167.13.1400

McDonald, C., & Abhyankar, S. (2011). Clinical decision support and rich clinical repositories: A symbiotic relationship. Comment on “electronic health records and clinical decision support systems.”Archives of Internal Medicine, 171(10), 903-905. doi:10.1001/archinternmed.2010.518 Mohan, V., & Hersh, W. R. (2011). EHRs and health care quality: Correlation with out-of -date, dif f erently purposed data does not equate with causality. Archives of Internal Medicine, 171(10), 952-953. doi:10.1001/archinternmed.2011.188 Oetgen, W. J., Mullen, J. B., & Mirro, M. J. (2011). Electronic health records, the PINNACLE registry, and quality care. Archives of Internal Medicine, 171(10), 953-4; author reply 954. doi:10.1001/archinternmed.2011.189 Persell, S. D., Dolan, N. C., Friesema, E. M., T hompson, J. A., Kaiser, D., & Baker, D. W. (2010). Frequency of inappropriate medical exceptions to quality measures. Annals of Internal Medicine, 152(4), 225-U49. Persell, S. D., Kaiser, D., Dolan, N. C., Andrews, B., Levi, S., Khandekar, J., . . . Baker, D. W. (2011). Changes in perf ormance af ter implementation of a multif aceted electronic-health-record-based quality improvement system. Medical Care, 49(2), 117-125. doi:10.1097/MLR.0b013e318202913d Radecki, R. P., & Sittig, D. F. (2011). Application of electronic health records to T he Joint Commission’s 2011 National Patient Saf ety Goals. Journal of the American Medical Association, 306 (1), 92-93. Romano, M. J., & Staf f ord, R. S. (2011). Electronic health records and clinical decision support systems impact on national ambulatory care quality. Archives of Internal Medicine, 171(10), 897-903. doi:10.1001/archinternmed.2010.527 Shih, S. C., McCullough, C. M., Wang, J. J., Singer, J., & Parsons, A. S. (2011). Health inf ormation systems in small practices improving the delivery of clinical preventive services. American Journal of Preventive Medicine, 41(6), 603-609. doi:10.1016/j.amepre.2011.07.024 US Department of Health and Human services. (2011). CMS EHR meaningful use overview. Retrieved f rom https://www.cms.gov/ehrincentiveprograms/30_Meaningf ul_Use.asp Proofed by Monica Key

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