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Purpose of Project
To translate an evidence-based Clostridioides difficile* (CDI) treatment clinical pathway using a systematic, transparent process into machine readable clinical decision support (CDS) prototyped for electronic health record (EHR) integration. (*The bacterium Clostridium difficile was renamed Clostridioides difficile in August 2016.)
- Getting evidence into practice to improve clinical decision making remains an ongoing challenge.
- We successfully translated a clinical pathway into machine readable CDS prototyped for EHR integration.
- We used the CDS Authoring Tool on the CDS Connect website to translate this clinical pathway into Clinical Quality Language (CQL) shareable decision support.
- Creating CDS artifacts from a clinical pathway informed by an EPC report may promote dissemination of work from AHRQ reports to a wide audience and support AHRQ’s Learning Health System initiatives.
Background. Translating evidence into tools that improve clinical decision making remains an ongoing challenge. In 2018, the ECRI Institute–Penn Medicine Evidence Based Practice Center (EPC) utilized the 2016 AHRQ EPC report update on the Early Diagnosis, Prevention, and Treatment of Clostridium difficile* to develop a clinical pathway for the treatment of Clostridium difficile infection (CDI) in the acute care setting. In this AHRQ EPC methods project, we sought to develop a rigorous process to further translate the previously created CDI treatment clinical pathway into clinical decision support tools prototyped for integration into the Penn Medicine electronic health record (EHR). (*Note that the bacterium Clostridium difficile was renamed Clostridioides difficile in August 2016.)
Methods. A core team including the pathway program manager at the University of Pennsylvania Health System (UPHS), and physicians with subject matter, evidence synthesis, and informatics expertise was assembled. We developed a step-wise, task based, iterative process to ensure feasibility of evidence translation from the original CDI treatment clinical pathway to our final products. Publicly available tools such as electronic GuideLine Implementation Assessment (eGLIA), Guideline Elements Model (GEM), and the Value Set Authority Center (VSAC) were utilized to perform a systematic, transparent, and reproducible translation process.
Results. We successfully translated a clinical pathway into machine-readable clinical decision support (CDS) prototyped for EHR integration. Using the CDS Authoring Tool on CDS Connect, we translated this clinical guidance into encoded Clinical Quality Language (CQL) and to support the creation and dissemination of shareable decision support. Findings from this translation effort led to improvements in the source CDI treatment pathway. Following an agile systems development life-cycle process reduced ambiguity and improved clarity for both the source CDI pathway and CDS products.
Discussion. Several lessons learned emerged from this project. Early and ongoing collaboration between clinical subject experts and the CDS development team accelerated development and adaptation of evidence into machine readable CDS. Future enhancements to the CDS Authoring Tool will enhance its utility and productivity. Our process utilized publicly accessible tools to develop transparent and reproducible CDS products. Moving forward, instruments to assess the quality of clinical pathways and CDS will be necessary to encourage organizations to utilize shareable CDS, a long term goal for repositories such as CDS Connect.
Suggested citation: Michel J, Flores E, Mull N, Tsou AY. Translation of a Clinical Pathway for C. Difficile Treatment Into a Machine-Readable Clinical Decision Support Artifact Prototyped for Electronic Health Record Integration. Methods Research Report. (Prepared by the ECRI Institute –Penn Medicine Evidence-based Practice Center under Contract No. 290-2015-00005-I.) AHRQ Publication No. 20-EHC001-EF. Rockville, MD: Agency for Healthcare Research and Quality; November 2019. Posted final reports are located on the Effective Health Care Program search page. DOI: https://doi.org/10.23970/AHRQEPCMETHQUALIMPRCDIFF.