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The American Journal of Managed Care August 2018
Impact of a Medical Home Model on Costs and Utilization Among Comorbid HIV-Positive Medicaid Patients
Paul Crits-Christoph, PhD; Robert Gallop, PhD; Elizabeth Noll, PhD; Aileen Rothbard, ScD; Caroline K. Diehl, BS; Mary Beth Connolly Gibbons, PhD; Robert Gross, MD, MSCE; and Karin V. Rhodes, MD, MS
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Choosing Wisely Clinical Decision Support Adherence and Associated Inpatient Outcomes
Andrew M. Heekin, PhD; John Kontor, MD; Harry C. Sax, MD; Michelle S. Keller, MPH; Anne Wellington, BA; and Scott Weingarten, MD
Levers to Reduce Use of Unnecessary Services: Creating Needed Headroom to Enhance Spending on Evidence-Based Care
Michael Budros, MPH, MPP, and A. Mark Fendrick, MD
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Michael E. Chernew, PhD
Optimizing Number and Timing of Appointment Reminders: A Randomized Trial
John F. Steiner, MD, MPH; Michael R. Shainline, MS, MBA; Jennifer Z. Dahlgren, MS; Alan Kroll, MSPT, MBA; and Stan Xu, PhD
Impact of After-Hours Telemedicine on Hospitalizations in a Skilled Nursing Facility
David Chess, MD; John J. Whitman, MBA; Diane Croll, DNP; and Richard Stefanacci, DO
Baseline and Postfusion Opioid Burden for Patients With Low Back Pain
Kevin L. Ong, PhD; Kirsten E. Stoner, PhD; B. Min Yun, PhD; Edmund Lau, MS; and Avram A. Edidin, PhD
Patient and Physician Predictors of Hyperlipidemia Screening and Statin Prescription
Sneha Kannan, MD; David A. Asch, MD, MBA; Gregory W. Kurtzman, BA; Steve Honeywell Jr, BS; Susan C. Day, MD, MPH; and Mitesh S. Patel, MD, MBA, MS
Evaluating HCV Screening, Linkage to Care, and Treatment Across Insurers
Karen Mulligan, PhD; Jeffrey Sullivan, MS; Lara Yoon, MPH; Jacki Chou, MPP, MPL; and Karen Van Nuys, PhD
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Ramona Shayegani, PharmD; Mary Jo Pugh, PhD; William Kazanis, MS; and G. Lucy Wilkening, PharmD
Medicare Advantage Enrollees’ Use of Nursing Homes: Trends and Nursing Home Characteristics
Hye-Young Jung, PhD; Qijuan Li, PhD; Momotazur Rahman, PhD; and Vincent Mor, PhD

Choosing Wisely Clinical Decision Support Adherence and Associated Inpatient Outcomes

Andrew M. Heekin, PhD; John Kontor, MD; Harry C. Sax, MD; Michelle S. Keller, MPH; Anne Wellington, BA; and Scott Weingarten, MD
This analysis examines the associations between adherence to Choosing Wisely recommendations embedded into clinical decision support alerts and 4 measures of resource use and quality.
ABSTRACT

Objectives: To determine whether utilization of clinical decision support (CDS) is correlated with improved patient clinical and financial outcomes.

Study Design: Observational study of 26,424 patient encounters. In the treatment group, the provider adhered to all CDS recommendations. In the control group, the provider did not adhere to CDS recommendations.

Methods: An observational study of provider adherence to a CDS system was conducted using inpatient encounters spanning 3 years. Data comprised alert status (adherence), provider type (resident, attending), patient demographics, clinical outcomes, Medicare status, and diagnosis information. We assessed the associations between alert adherence and 4 outcome measures: encounter length of stay, odds of 30-day readmission, odds of complications of care, and total direct costs. The associations between alert adherence and the outcome measures were estimated using 4 generalized linear models that adjusted for potential confounders, such as illness severity and case complexity.

Results: The total encounter cost increased 7.3% (95% CI, 3.5%-11%) for nonadherent encounters versus adherent encounters. We found a 6.2% (95% CI, 3.0%-9.4%) increase in length of stay for nonadherent versus adherent encounters. The odds ratio for readmission within 30 days increased by 1.14 (95% CI, 0.998-1.31) for nonadherent versus adherent encounters. The odds ratio for complications increased by 1.29 (95% CI, 1.04-1.61) for nonadherent versus adherent encounters.

Conclusions: Consistent improvements in measured outcomes were seen in the treatment group versus the control group. We recommend that provider organizations consider the introduction of real-time CDS to support adherence to evidence-based guidelines, but because we cannot determine the cause of the associations between CDS interventions and improved clinical and financial outcomes, further study is required.

Am J Manag Care. 2018;24(8):361-366
Takeaway Points

This analysis examined the associations between adherence to Choosing Wisely recommendations embedded into clinical decision support (CDS) alerts and 4 measures of resource use and quality.
  • Encounters in which providers adhered to all alerts had significantly lower total costs, shorter lengths of stay, a lower probability of 30-day readmissions, and a lower probability of complications compared with nonadherent encounters.
  • Full adherence to Choosing Wisely alerts was associated with savings of $944 from a median encounter cost of $12,940.
  • Health systems should consider real-time CDS interventions as a method to encourage improved adoption of evidence-based guidelines.
The Health Information Technology for Economic and Clinical Health Act, an important component of the American Recovery and Reinvestment Act, enabled the federal government to subsidize hospitals, health systems, and physicians $40 billion1 to implement electronic health records (EHRs) and imposed significant penalties on nonadopters. This investment was expected to result in up to $470 billion in inpatient cost savings alone2 through reduced patient length of stay,3 reduced utilization of services, and other outcomes.4 Today, certified EHRs are operational in 96% of nonfederal acute care hospitals and health systems in the United States,5 but the expected cost savings have not yet been realized.6 The evidence that EHRs improve quality and patient outcomes has been mixed. Some studies have found improved quality of care in the ambulatory care setting,7 higher guideline adherence, fewer medication errors, and decreased adverse drug effects.8 However, other studies have found that EHR use is not associated with decreased readmissions9,10 or lower rates of mortality.8

In 2012, the ABIM Foundation introduced the Choosing Wisely (CW) initiative, a voluntary effort by more than 70 physician subspecialty societies to identify commonly used low-value services.11 The intent of this publicly promoted initiative was to stimulate provider–patient discussions about appropriate care and thereby reduce low-value tests and treatments.7 Although the primary aim of CW is not to lower costs, reducing inappropriate care could lead to lower costs for both patients and payers. To date, CW may not have achieved clinically significant changes in reducing low-value care.12-14 Public promotion alone does not appear to be sufficient to achieve widespread adoption.10 A 2015 claims-based analysis of 7 CW recommendations found that use of 2 low-value services declined, but the decreases were not clinically significant.10 In their recommendations, the authors called for innovative methods to disseminate CW recommendations.10 Provider difficulty interpreting guidelines and evaluating patient risk,15,16 patient need for reassurance,13 and provider fear of malpractice litigation17 pose additional obstacles.

Ideally, an EHR infrastructure could overcome these obstacles and provide real-time computerized clinical decision support (CDS) to inform healthcare providers when their care deviates from evidence-based guidelines. CDS comprises a variety of tools, including computerized alerts and reminders with information such as diagnostic support, clinical guidelines, relevant patient information, diagnosis-specific order sets, documentation templates, and drug–drug interactions.18 CDS provides the ability to modify tests and treatments based on context- and patient-specific information presented at the point of care. Utilizing CDS can help providers avoid ordering a low-value test or intervention that could lead to additional nontherapeutic interventions or harm. CDS has been shown to improve a variety of processes, including prescribing practices,19 appropriate use of diagnostic radiology,20 adherence to quality measures,21 and conformance to evidence-based care.19 Systems that automate CDS, provide tailored recommendations based on patient characteristics, and prompt clinicians to provide a reason for overriding recommendations have been shown to be significantly more likely to succeed than systems that provide only patient assessments.19

We implemented select CW recommendations in the EHR at a large academic health system in the form of 92 alert-based CDS interventions, both inpatient and ambulatory. Inpatient alerts selected for study were those deemed the most technically feasible to deploy accurately and with a sufficient number of relevant orders that would trigger an alert, thus providing a sufficient volume of alerted encounters to evaluate. When initiating a potentially inappropriate order, a provider received real-time notification of deviation from a CW recommendation. That provider then had the option to cancel, change, or justify the order, if he or she agreed with the alert’s recommendation in the context of the individual patient. The objective of this study was to evaluate the relationships between providers who adhered to CW alerts and measurable outcomes.

METHODS

Study Setting

We conducted an observational study of provider adherence to the 18 highest-volume CW alerts utilizing a commercially available EHR-embedded CDS system at Cedars-Sinai Health System, a nonprofit tertiary 886-bed hospital and multispecialty academic health science center located in Los Angeles, California. The medical staff is pluralistic and includes employed and independent physicians in private practice, physician extenders, and residents.

This study included inpatient encounters from October 22, 2013, to July 31, 2016. The study protocol was approved by the Cedars-Sinai Medical Center (CSMC) Institutional Review Board.

Study Population and Data Sources

Data for the study were collected from 3 sources: data sent from the EHR to the CDS analytics platform, which included the category of the provider triggering the alert (eg, resident, attending) and clinical data allowing for the assessment of adherence or nonadherence to the alert during the encounter; claims data, which included patient demographics (eg, age, gender), diagnoses, services provided, admit and discharge dates, Medicare Severity-Diagnosis Related Group codes, and costs; and direct cost data associated with the patient care department, which we describe below. The unit of analysis is the patient encounter; this covers the entire inpatient visit, and there is only 1 encounter per visit. Data were matched using a common encounter identifier. Encounters in which the providers were considered adherent included all encounters that received CW alerts and for which providers adhered to all alerts. Alerts were considered adhered to when the order flagged by the CDS as potentially conflicting with CW was not signed within 1 hour after an alert was shown to a provider. The nonadherent group included encounters where providers received CW alerts and for which they did not adhere to any (complete nonadherence). Approximately 1400 encounters that did not meet either of these criteria were excluded because they included partial adherence to some but not all of the alerts. The Elixhauser index was computed as an unweighted sum of comorbidities present during all encounters for a given patient to estimate the morbidity burden.22


 
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