Optimizing Clinical Decision Support in the Electronic Health Record

Abstract

SummaryObjective: Adoption of clinical decision support (CDS) tools by clinicians is often limited by workflow barriers. We sought to assess characteristics associated with clinician use of an electronic health record-embedded clinical decision support system (CDSS). Methods: In a prospective study on emergency department (ED) activation of a CDSS tool across 14 hospitals between 9/1/14 to 4/30/15, the CDSS was deployed at 10 active sites with an on-site champion, education sessions, iterative feedback, and up to 3 gift cards/clinician as an incentive. The tool was also deployed at 4 passive sites that received only an introductory educational session. Activation of the CDSS -which calculated the Pulmonary Embolism Severity Index (PESI) score and provided guidance -and associated clinical data were collected prospectively. We used multivariable logistic regression with random effects at provider/facility levels to assess the association between activation of the CDSS tool and characteristics at: 1) patient level (PESI score), 2) provider level (demographics and clinical load at time of activation opportunity), and 3) facility level (active vs. passive site, facility ED volume, and ED acuity at time of activation opportunity). Results: Out of 662 eligible patient encounters, the CDSS was activated in 55%: active sites: 68% (346/512); passive sites 13% (20/150). In bivariate analysis, active sites had an increase in activation rates based on the number of prior gift cards the physician had received (96% if 3 prior cards versus 60% if 0, p<0.0001). At passive sites, physicians < age 40 had higher rates of activation (p=0.03). In multivariable analysis, active site status, low ED volume at the time of diagnosis and PESI scores I or II (compared to III or higher) were associated with higher likelihood of CDSS activation. Conclusions: Performing on-site tool promotion significantly increased odds of CDSS activation. Optimizing CDSS adoption requires active education. Research Article Background and SignificanceThe goal of knowledge translation (KT) is to close the gap between proven science and real-time care delivery. Historically, the adoption of new evidence into clinical practice has lagged a decade or more -with variable uptake across physicians and settings [1,2]. While KT, and implementation science more generally, are relatively new fields of emphasis and study, previous work has identified barriers to effective KT as well as best practices for implementation [3].For example, Davidoff has stressed the importance of concentrating not only on the diffusion of new validated techniques and treatments into practice but also on the "undiffusion" of previous practice -especially amongst those physicians who may be prone to suffering from inertia when it comes to changing their practice [4]. Michie has described a behavior change wheel framework for identifying well-suited interventions (e.g., education, environmental context, etc.) to combat barriers to implementation of knowledge translation tools [5]. Diner has de...

Effect of Clinical Decision Support on Diagnostic Imaging for Pediatric Appendicitis

“…At KPNC, physicians were trained to access AppyCDS through an existing EHR-linked CDS platform, RISTRA (Risk Stratification). 23 , 24 In addition, KPNC physicians were notified of potentially eligible patients through automated text message alerts. 25 At KPNC, physicians accessing AppyCDS completed initial screening questions, identified exclusions, and, for eligible patients, proceeded with data entry to calculate a pARC score.…”

Developing a Prognostic Information System for Personalized Care in Real Time

“…Adoption of such tools in actual practice has remained limited, in part because they are sometimes developed without a clear picture of how they will influence real-life decision processes [26]. Experience in our setting suggests that prediction tools may succeed best when their development is initiated and promoted by clinicians [2728]. Although the oncologists who have tested PRISM have described how it could be useful in conversations with patients, empirical evaluation in clinician-patient interactions is needed to elucidate its actual benefits and limitations.…”