Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure.

M. E. Patterson; P. Marken; Y. Zhong; S. D. Simon; W. Ketcherside

doi:10.4338/ACI-2014-01-RA-0008

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2014; 05(03): 670-684
DOI: 10.4338/ACI-2014-01-RA-0008

Research Article

Schattauer GmbH

Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure.

M. E. Patterson

¹Division of Pharmacy Practice and Administration, University of Missouri-Kansas City School of Pharmacy, Kansas City, Missouri

,

P. Marken

¹Division of Pharmacy Practice and Administration, University of Missouri-Kansas City School of Pharmacy, Kansas City, Missouri

,

Y. Zhong

²Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas

,

S. D. Simon

³Department of Informatics Medicine and Personalized Health, University of Missouri-Kansas City, Kansas City, Missouri

,

W. Ketcherside

⁴Ketcherside Group, L.L.C., Kansas City, Missouri

› Author Affiliations

Abstract

Summary

Background: Regulatory standards for 30-day readmissions incentivize hospitals to improve quality of care. Implementing comprehensive electronic health record systems potentially decreases readmission rates by improving medication reconciliation at discharge, demonstrating the additional benefits of inpatient EHRs beyond improved safety and decreased errors.

Objective: To compare 30-day all-cause readmission incidence rates within Medicare fee-for-service with heart failure discharged from hospitals with full implementation levels of comprehensive EHR systems versus those without.

Methods: This retrospective cohort study uses data from the American Hospital Association Health IT survey and Medicare Part A claims to measure associations between hospital EHR implementation levels and beneficiary readmissions. Multivariable Cox regressions estimate the hazard ratio of 30-day all-cause readmissions within beneficiaries discharged from hospitals implementing comprehensive EHRs versus those without, controlling for beneficiary health status and hospital organizational factors. Propensity scores are used to account for selection bias.

Results: The proportion of heart failure patients with 30-day all-cause readmissions was 30%, 29%, and 32% for those discharged from hospitals with full, some, and no comprehensive EHR systems. Heart failure patients discharged from hospitals with fully implemented comprehensive EHRs compared to those with no comprehensive EHR systems had equivalent 30-day readmission incidence rates (HR = 0.97, 95% CI 0.73 – 1.3)

Conclusions: Implementation of comprehensive electronic health record systems does not necessarily improve a hospital’s ability to decrease 30-day readmission rates. Improving the efficiency of post-acute care will require more coordination of information systems between inpatient and ambulatory providers.

Citation: Patterson ME, Marken P, Zhong Y, Simon SD, Ketcherside W. Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure. Appl Clin Inf 2014; 5: 670–684

http://dx.doi.org/10.4338/ACI-2014-01-RA-0008

Keywords

Electronic health records - quantitative evaluation - inpatient care

Full Text

References

References
1 Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009; 360 (14) 1418-1428.
2 Krumholz HM, Lin Z, Keenan PS, Chen J, Ross JS, Drye EE, Bernheim SM, Wang Y, Bradley EH, Han LF, Normand SL. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA 2013; 309 (06) 587-593.
3 Medicare Payment Advisory Commission (MedCAP).. Report to the Congress: creating greater efficiency in Medicare. http://www.medpac.gov/documents/Jun07_EntireReport.pdf
4 Health Policy Brief: Medicare Hospital Readmissions Reduction Program, Health Affairs . November 12, 2013.
5 Kleeberg P. Leveraging meaningful use to assist in reducing hospital readmissions. Key Health Alliance: Regional Extension Assistance Center for HIT. RARE Campaign Webinar. August 24, 2012. http://www. rarereadmissions.org/documents/Slides_Meaningful_Use_20120824.pdf. Accessed March 22, 2014
6 HealthIT.gov.. Frequently asked questions for providers & professionals: Do electronic health records align with patient-centered medical home initiatives?. http://www.healthit.gov/providers-professionals/faqs/do-electronic-health-records-align-patient-centered-medical-home-initia. Accessed March 22, 2014
7 Jones SS, Heaton P, Friedberg MW, Schneider EC. Today’s ’meaningful use’ standard for medication orders by hospitals may save few lives; later stages may do more. Health Aff (Millwood) 2011; 30 (10) 2005-2012.
8 Longhurst CA, Parast L, Sandborg CI, Widen E, Sullivan J, Hahn JS, Dawes CG, Sharek PJ. Decrease in hospital-wide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics 2010; 126 (01) 14-21.
9 Patterson ME, Hernandez AF, Hammill BG, Fonarow GC, Peterson ED, Schulman KA, Curtis LH. Process of care performance measures and long-term outcomes in patients hospitalized with heart failure. Med Care 2010; 48 (03) 210-216.
10 Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, Servi D, Laffel G, Sweitzer BJ, Shea BF, Hallisey R. et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA 1995; 274 (01) 29-34.
11 Bates DW, Leape LL, Cullen DJ, Laird N, Petersen LA, Teich JM, Burdick E, Hickey M, Kleefield S, Shea B, Vander Vliet M, Seger DL. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 1998; 280 (15) 1311-1316.
12 Reckmann MH, Westbrook JI, Koh Y Lo C, Day RO. Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc 2009; 16 (05) 613-623.
13 Shamliyan TA, Duval S, Du J, Kane RL. Just what the doctor ordered. Review of the evidence of the impact of computerized physician order entry system on medication errors. Health Serv Res 2008; 43 1 Pt 1 32-53.
14 Wolfstadt JI, Gurwitz JH, Field TS, Lee M, Kalkar S, Wu W, Rochon PA. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med 2008; 23 (04) 451-458.
15 DesRoches CM, Campbell EG, Vogeli C, Zheng J, Rao SR, Shields AE, Donelan K, Rosenbaum S, Bristol SJ, Jha AK. Electronic health records’ limited successes suggest more targeted uses. Health Aff (Millwood). 2010; 29 (04) 639-646.
16 Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, Taylor R. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood) 2005; 24 (05) 1103-1117.
17 R. Amarasingham, P.C, Patel, K. Toto, Nelson LL, Swanson TS, Moore BJ, Xie B, Zhang S, Alvarez KS, Ma Y, Drazner MH, Kollipar U, Halm EA. Allocating scarce resources in real time to reduce heart failure readmissions: A prospective, controlled study,“ BMJ Quality & Safety, published online July 31, 2013
18 American Hospital Association.. 2007 Hospital EHR Adoption Database [supplement to FY2008 AHA Annual Survey Database]. Chicago: Health Forum,; 2009
19 American Hospital Association.. FY2008 AHA Annual Survey Database Chicago: Health Forum; 2009
20 Area Health Resources Files (AHRF). 2012–2013. US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Professions; Rockville, MD.:
21 Jha AK, DesRoches CM, Campbell EG Donelan K, Rao SR, Ferris TG, Shields A, Rosenbaum S, Blumenthal D. Use of electronic health records in U. S. hospitals. N Engl J Med 2009; 360 (16) 1628-1638.
22 National Center for Health Statistics, Office of Analysis and Epidemiology.. Analytic issues in using the medicare enrollment and claims data linked to NCHS surveys. December 2012. Hyattsville, Maryland.:
23 Stroupe KT, Teal EY, Weiner M, Gradus-Pizlo I, Brater DC, Murray MD. Healthcare and medication costs and use among older adults with heart failure. Am J Med 2004; 116 (07) 443-450.
24 Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 2004; 57 (12) 1288-1294.
25 Jonikas MA, Mandl KD. Surveillance of medication use: early identification of poor adherence. J Am Med Inform Assoc 2012; 19 (04) 649-654.
26 Fonarow GC, Abraham WT, Albert NM, Stough WG, Gheorghiade M, Greenberg BH, O’Connor CM, Sun JL, Yancy CW, Young JB. OPTIMIZE-HF Investigators and Coordinators. Influence of beta-blocker continuation or withdrawal on outcomes in patients hospitalized with heart failure: findings from the OPTIMIZE-HF program. J Am Coll Cardiol 2008; 52 (03) 190-199.