Meagan R. Bean

Background: Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools produce mixed results and are often disliked by clinicians. Principles from behavioral economics such as “nudges” may improve the effectiveness and clinician satisfaction of CDS tools. This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools. Objective: The purpose of this paper is to describe a systematic and pragmatic approach grounded in implementation science to identify and prioritize how best to incorporate different types of nudges into CDS tools. We provide a case example of how this systematic approach was applied to design a CDS tool to improve guideline-concordant prescribing of mineralocorticoid receptor antagonists for patients with heart failure and reduced ejection fraction. Methods: We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, and Ego nudge framework and the Practical, Robust Implementation and Sustainability Model implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. To illustrate how these frameworks can be applied in a real-life scenario, we use a case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools. Results: We defined four iterative steps guided by the Practical, Robust Implementation and Sustainability Model: (1) engage partners for user-centered design, (2) develop a shared understanding of the nudge types, (3) determine the overarching CDS format, and (4) brainstorm and prioritize nudge types to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned. Conclusions: We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues when designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. This approach can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.

Background: Virtual care interventions have the potential to improve access to care and serial medication intensification for patients with chronic heart failure with reduced ejection fraction (HFrEF). However, concerns remain that these interventions might unintentionally create or widen existing disparities in care delivery and patient outcomes. Objective: This study aimed to characterize the health care use patterns of patients who have HFrEF, including specialty type and frequency of in-person and virtual visits. Methods: We conducted a retrospective cohort study of patients with HFrEF within a large health system. Inclusion criteria were patients alive with an ejection fraction ≤40% as of September 1, 2021, and at least one virtual or in-person outpatient visit to a primary care or cardiology clinician in the prior year. Descriptive statistics were used to evaluate baseline patient demographics and clinical use data and outcomes. Univariate analyses were performed both with virtual visits as a variable (received or did not receive) using the chi-square test for association and as a discrete outcome using the Wilcoxon rank-sum test to capture potentially important predictor variables that could influence use or frequency of using virtual visits. The primary outcome of interest was the odds of at least one virtual visit during the 1-year evaluation period from 2021 to 2022. Descriptive statistics were used to evaluate baseline patient demographics and care use. A logistic regression model was used to model at least one primary care or cardiology virtual visit. Results: A total of 8481 patients were included in the analysis. The mean age was 65.9 years (SD 15.1), 5672 (66.9%) patients were male and 6608 (77.9%) patients were non-Hispanic White. The majority of patients had no cardiology (7938/8481, 93.6%) or primary care (7955/8481, 93.8%) virtual visits during the evaluation period. Multivariable logistic regression showed significantly higher odds of having at least one virtual visit for patients with certain digital access—for example, email on file (odds ratio [OR] 9.3, P≤.001), cell phone on file (OR 2.9, P≤.001), and active electronic health record patient portal (OR 2.8, P≤.001)—than those without. Age, race, ethnicity, rurality, and Social Vulnerability Index were not associated with virtual visits. Conclusions: Only a minority of patients with HFrEF were seen via virtual visits. Patients who regularly used digital technology were more likely to have virtual visits. Patients were more likely to be seen in a cardiology clinic than by a primary care provider. Although there was no evidence of an association between social determinants of health factors like race, ethnicity, or rurality with digital divide indicators, these findings should be interpreted with caution given the limitations of these data. Future studies should aim to replicate the findings of this study and explore ways to enhance the effective and equitable use of virtual visits.

People with functional seizures (FSs) experience high health care utilization and costs revolving around the emergency department (ED). Overall, appropriate treatment of FS is underused, and better care pathways are associated with lower ED reattendance. Our objective was to assess changes in total ED and inpatient visits and costs before and after referral to a specialized, comprehensive FS treatment clinic. We collected data from 100 consecutive patients referred to the University of Colorado (CU) FS Clinic between July 2019 and December 2021. Hospital account data were obtained directly from the electronic health record. Total ED and inpatient visits, charges, and payments 1 year before and 1 year after referral were collected and analyzed using the Wilcoxon signed-rank test. Ninety-four patients were included for analysis. 79% were female, 52% were on Medicaid, and the mean age was 41 (SD 13) years. Total visits after referral (ED and inpatient) were significantly reduced compared with total visits before referral (mean = 1.44 (SD 3.52) vs 1.83 (SD 3.52), p = 0.045). The same test was performed for total charges after and before referral ($15,551 (SD $38,712) vs $30,257 (SD $81,589), p = 0.03) and for total payments after and before referral ($2,469 (SD $6,682) vs $5,199 (SD $15,084), p = 0.02). Referral to a specialized FS clinic is associated with reduced health care utilization and costs. This proof-of-concept study reveals that hospitals should implement policies to support efficient care pathways to comprehensive FS treatment programs with potential for cost savings.