As the nation continues its efforts to contain healthcare costs and improve quality, healthcare information technology provides some of our most potent yet underutilized tools. Clinical prediction rules are frontline decision aids that combine state-of-the-art evidence with real-time patient history, physical examination, and laboratory data. While often well-validated, clinical prediction rules have been underutilized in practice. Recently, our team developed the integrated clinical prediction rule (iCPR) system, embedding CPRs within the nation's largest commercial electronic health record (EHR) system. Using this novel system, we demonstrated high rates of provider utilization and a significant reduction in antibiotic prescribing and diagnostic test ordering among suspected cases of strep throat and pneumonia at a single healthcare facility. The objective of the proposed project is to generalize this platform across diverse settings and create a toolkit for further dissemination. Building on the success of the original iCPR project, the specific aims of this proposal are to (1) integrate our previously tested and refined iCPR tool into the same commercial EHR in three different clinical settings, adapting the innovation to provider preference, culture, and local workflow rather than imposing a rigidly standardized tool, (2) identify and measure rate and variability of iCPR uptake across different settings, (3) determine iCPR impact on antibiotic prescribing and diagnostic test-ordering patterns across diverse clinical settings with a randomized controlled trial, and (4) use a well-established theory-driven implementation framework to identify facilitators and barriers to integration in each setting, and develop a toolkt for adapting and implementing the tool in diverse settings. To achieve these aims, we propose a five- year study in which we first adapt, integrate and usability-test the original iCPR at three new diverse sites. We will then conduct a two-year randomized controlled trial with a one-year post-trial open-access observation period to determine the persistence of: 1) the tool's utilizatio and 2) its impact on antibiotic- and test-ordering in patients with suspected strep throat or pneumonia. In the final year, study findings will be compiled into a toolkit so that any healthcare facility using the Epic EHR can integrate iCPR into its ambulatory workflow. The study uses several innovative and significant approaches, including: 1) adapting the nation's most widespread commercial EHR system; 2) building the new tool with off-the-shelf technology included in every Epic EHR package, so the innovation can be easily ported to all Epic EHR users; 3) using highly specific, well-validated clinical prediction rules as its core content; 4) guiding the integration process with highly generalizable usability testing techniques; and 5) using a hybrid RE-AIM and normalization process theory implementation evaluation framework. Together, these innovative approaches make iCPR uniquely suited to overcome longstanding barriers and integrate and disseminate evidence-based tools into the primary care workflow at the point of care in real time.
The proposed project seeks to generalize the innovative platform we have developed to embed evidence- based rules, termed clinical prediction rules (iCPR), directly into practice across diverse ambulatory settings. We will determine if and how primary care practitioners will use these commercial electronic health record embedded customized iCPR tools in the treatment of acute respiratory infections, and we will identify the variations in uptake across several different primary care settings.
| Richardson, Safiya; Mishuris, Rebecca; O'Connell, Alexander et al. (2017) ""Think aloud"" and ""Near live"" usability testing of two complex clinical decision support tools. Int J Med Inform 106:1-8 |
| Feldstein, David A; Hess, Rachel; McGinn, Thomas et al. (2017) Design and implementation of electronic health record integrated clinical prediction rules (iCPR): a randomized trial in diverse primary care settings. Implement Sci 12:37 |
