Referral Rates for Cardiac Rehabilitation Among Eligible Inpatients After Implementation of a Default Opt-Out Decision Pathway in the Electronic Medical Record | Cardiology | JAMA Network Open | JAMA Network
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Figure.  Cardiac Rehabilitation Referral Rates by Site and Time
Cardiac Rehabilitation Referral Rates by Site and Time
Table.  Sample Characteristics
Sample Characteristics
1.
Benjamin  EJ, Blaha  MJ, Chiuve  SE,  et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics—2017 update: a report from the American Association.   Circulation. 2017;135(10):e146-e603. doi:10.1161/CIR.0000000000000485 PubMedGoogle ScholarCrossref
2.
Writing Committee Members; Thomas  RJ, King  M,  et al.  AACVPR/ACCF/AHA 2010 update: performance measures on cardiac rehabilitation for referral to cardiac rehabilitation/secondary prevention services: a report of the American Association of Cardiovascular and Pulmonary Rehabilitation and the American College of Cardiology Foundation/American Heart Association Task Force on Performance Measures (Writing Committee to Develop Clinical Performance Measures for Cardiac Rehabilitation).   Circulation. 2010;122(13):1342-1350. doi:10.1161/CIR.0b013e3181f5185b PubMedGoogle ScholarCrossref
3.
Aragam  KG, Dai  D, Neely  ML,  et al.  Gaps in referral to cardiac rehabilitation of patients undergoing percutaneous coronary intervention in the United States.   J Am Coll Cardiol. 2015;65(19):2079-2088. doi:10.1016/j.jacc.2015.02.063 PubMedGoogle ScholarCrossref
4.
Ades  PA, Keteyian  SJ, Wright  JS,  et al.  Increasing cardiac rehabilitation participation from 20% to 70%: a road map from the Million Hearts Cardiac Rehabilitation Collaborative.   Mayo Clin Proc. 2017;92(2):234-242. doi:10.1016/j.mayocp.2016.10.014 PubMedGoogle ScholarCrossref
5.
Patel  MS, Volpp  KG, Asch  DA.  Nudge units to improve the delivery of health care.   N Engl J Med. 2018;378(3):214-216. doi:10.1056/NEJMp1712984 PubMedGoogle ScholarCrossref
6.
Patel  MS, Day  SC, Halpern  SD,  et al.  Generic medication prescription rates after health system-wide redesign of default options within the electronic health record.   JAMA Intern Med. 2016;176(6):847-848. doi:10.1001/jamainternmed.2016.1691 PubMedGoogle ScholarCrossref
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    Research Letter
    Cardiology
    January 14, 2021

    Referral Rates for Cardiac Rehabilitation Among Eligible Inpatients After Implementation of a Default Opt-Out Decision Pathway in the Electronic Medical Record

    Author Affiliations
    • 1Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 2Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 3Office of the Chief Medical Information Officer, University of Pennsylvania, Philadelphia
    • 4Penn Medicine Nudge Unit, Penn Medicine Center for Healthcare Innovation, Philadelphia, Pennsylvania
    • 5The Wharton School, University of Pennsylvania, Philadelphia
    • 6Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
    JAMA Netw Open. 2021;4(1):e2033472. doi:10.1001/jamanetworkopen.2020.33472
    Introduction

    Ischemic heart disease is the leading cause of mortality in the United States.1 Cardiac rehabilitation (CR) is an evidence-based therapy that reduces mortality, morbidity, and hospital readmissions in patients with ischemic heart disease.2 However, CR is widely underused: 25% of US hospitals refer less than 20% of eligible patients.3 Novel scalable approaches are needed to improve CR referral rates.4

    Default options are the path of least resistance and set conditions that occur if no alternative is chosen.5 Previous work has shown how changing default settings can significantly influence clinicians’ prescribing behaviors.6 Default options may also influence more complex decision pathways, but this tactic has not been well examined.

    In this quality improvement study, we evaluated changes in CR referral after automated electronic health record–based technology was used to identify eligible patients and decision pathways were redesigned from opt-in to opt-out referral. We compared changes that occurred across 3 years at an intervention hospital with changes in 2 control hospitals in the same academic health system.

    Methods

    The study period extended from January 1, 2016, to December 31, 2018. From September to December 2016, we experimented with redesigning defaults in the decision pathway at 1 of 3 Penn Medicine hospitals in Philadelphia, Pennsylvania. In January 2017, we implemented an opt-out CR referral decision pathway that used the electronic health record to automatically identify eligible patients from the electronic health record and notify appropriate staff on the wards by using secure text messaging (eMethods in the Supplement). Rounds were restructured so that cardiologists signed templated CR orders and staff met with patients to facilitate CR placement before discharge. Appropriate CR referrals were manually verified. We also created educational material for the patient on the importance and relevance of CR therapy, which was provided to the patient by the clinical resource coordinator. This study was reviewed and determined to qualify as a quality improvement study by the University of Pennsylvania institutional review board. Patient informed consent was waived because the study was primarily a quality improvement and operation project advancing guideline-directed standard of care. This study followed the Standards for Quality Improvement Reporting Excellence (SQUIRE) guideline.

    Data for 1 year before and 2 years after the intervention were obtained from medical record–abstracted CR referral rates submitted to the CathPCI Registry, one of the registries in the National Cardiovascular Data Registry administered by the American College of Cardiology.4 A linear probability model was used to perform a difference-in-differences analysis to evaluate changes in CR referral rates at the intervention site vs the 2 control sites during a 1-year preintervention period and a 2-year postintervention period, excluding the washout period of rapid experimentation. The unit of analysis was the patient, and the model included variables for time (before vs after the intervention), site (intervention vs controls), and an interaction term for time and site. A test of control sites was conducted in the first 9 months of year 1 to evaluate pre-intervention trends. All models were adjusted for age, sex, race/ethnicity (obtained from the electronic heart record based on patient report), insurance, annual household income, body mass index, and history of myocardial infarction, congestive heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, hypertension, diabetes, and smoking. Two-sided hypothesis tests used a significance level of .05; SAS statistical software, version 9.4 (SAS Institute Inc) was used for all analyses.

    Results

    The sample consisted of 2832 patients with ischemic heart disease at 3 hospital sites. The patients had a mean (SD) age of 66.7 (11.4) years; 812 (28.7%) were female, 1870 (66.0%) 66.0% were White, and 564 (19.9%) were Black (Table). The Figure shows CR referral rates by site over time. Preintervention trends did not differ between the intervention and control sites. Preintervention trends did not differ between the intervention and control sites. At the end of the study, the percentage of CR referrals at the intervention site was 85.7% and for the control sites was 31.6%. Compared with the control sites over time, the intervention site had a significant 47–percentage point increase in CR referrals (95% CI, 39.2-55.1 percentage points; P < .001).

    Discussion

    The findings of this quality improvement study suggest that default options can be used for more complex decision pathways because the intervention was associated with a sustained significant increase in CR referrals. This increase may be due to a shift in effort on the part of cardiologists who previously had to manually opt-in to refer patients and now were prompted to sign orders during rounds unless they opted-out. This method facilitated CR referral by optimizing cardiologists’ workflow and sharing tasks with other staff.

    At the control sites, CR referrals increased in the second quarter of 2017, which may have been due to cardiologists becoming aware of initial results at the intervention site. However, this increase was not as substantial as that at the intervention site and highlights the importance of combining education with behavioral change strategies. This study was limited by its observational design at only 1 health system and by the lack of follow-up data on patient CR participation rates. In conclusion, restructuring decision pathways from an opt-in to an opt-out choice was associated with a significant increase in CR referrals. This pathway represents a low-cost, scalable approach that could be expanded to other health systems and for other therapies.

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    Article Information

    Accepted for Publication: November 22, 2020.

    Published: January 14, 2021. doi:10.1001/jamanetworkopen.2020.33472

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Adusumalli S et al. JAMA Network Open.

    Corresponding Author: Srinath Adusumalli, MD, MSHP, Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania Health System, 3400 Civic Center Blvd, 11-139 South Pavilion, Philadelphia, PA 19104 (sri@pennmedicine.upenn.edu).

    Author Contributions: Drs Adusumalli and Patel had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Adusumalli, Chokshi, Patel.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Adusumalli, Jolly.

    Critical revision of the manuscript for important intellectual content: Adusumalli, Chokshi, Gitelman, Rareshide, Kolansky, Patel.

    Statistical analysis: Adusumalli, Rareshide.

    Obtained funding: Patel.

    Administrative, technical, or material support: Adusumalli, Jolly, Chokshi, Gitelman.

    Supervision: Adusumalli, Kolansky, Patel.

    Conflict of Interest Disclosures: Dr Patel reported being supported by career development awards from the Department of VA Health Services Research and Development and the Doris Duke Charitable Foundation. Dr Patel reported being the owner of Catalyst Health LLC, a technology and behavioral change consulting firm, and being an advisory board member for and receiving stock options from Life.io, Healthmine, and Holistic Industries outside the submitted work. Dr Patel also reported receiving research funding from Deloitte that was not related to the work described in this article. No other disclosures were reported.

    Funding/Support: This study was supported by award 2T32HL007843-21 from the National Institutes of Health and by the University of Pennsylvania Health System through the Penn Medicine Nudge Unit.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: We thank Katherine Choi, MD (Penn Medicine Center for Health Care Innovation), for providing technical assistance with this project. She received no compensation for this work.

    References
    1.
    Benjamin  EJ, Blaha  MJ, Chiuve  SE,  et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics—2017 update: a report from the American Association.   Circulation. 2017;135(10):e146-e603. doi:10.1161/CIR.0000000000000485 PubMedGoogle ScholarCrossref
    2.
    Writing Committee Members; Thomas  RJ, King  M,  et al.  AACVPR/ACCF/AHA 2010 update: performance measures on cardiac rehabilitation for referral to cardiac rehabilitation/secondary prevention services: a report of the American Association of Cardiovascular and Pulmonary Rehabilitation and the American College of Cardiology Foundation/American Heart Association Task Force on Performance Measures (Writing Committee to Develop Clinical Performance Measures for Cardiac Rehabilitation).   Circulation. 2010;122(13):1342-1350. doi:10.1161/CIR.0b013e3181f5185b PubMedGoogle ScholarCrossref
    3.
    Aragam  KG, Dai  D, Neely  ML,  et al.  Gaps in referral to cardiac rehabilitation of patients undergoing percutaneous coronary intervention in the United States.   J Am Coll Cardiol. 2015;65(19):2079-2088. doi:10.1016/j.jacc.2015.02.063 PubMedGoogle ScholarCrossref
    4.
    Ades  PA, Keteyian  SJ, Wright  JS,  et al.  Increasing cardiac rehabilitation participation from 20% to 70%: a road map from the Million Hearts Cardiac Rehabilitation Collaborative.   Mayo Clin Proc. 2017;92(2):234-242. doi:10.1016/j.mayocp.2016.10.014 PubMedGoogle ScholarCrossref
    5.
    Patel  MS, Volpp  KG, Asch  DA.  Nudge units to improve the delivery of health care.   N Engl J Med. 2018;378(3):214-216. doi:10.1056/NEJMp1712984 PubMedGoogle ScholarCrossref
    6.
    Patel  MS, Day  SC, Halpern  SD,  et al.  Generic medication prescription rates after health system-wide redesign of default options within the electronic health record.   JAMA Intern Med. 2016;176(6):847-848. doi:10.1001/jamainternmed.2016.1691 PubMedGoogle ScholarCrossref
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