Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing

Abstract

SummaryObjective: Pharmacogenomic-guided dosing has the potential to improve patient outcomes but its implementation has been met with clinical challenges. Our objective was to develop and evaluate a clinical decision support system (CDSS) for pharmacogenomic-guided warfarin dosing designed for physicians and pharmacists. Methods: Twelve physicians and pharmacists completed 6 prescribing tasks using simulated patient scenarios in two iterations (development and validation phases) of a newly developed pharmacogenomic-driven CDSS prototype. For each scenario, usability was measured via efficiency, recorded as time to task completion, and participants' perceived satisfaction which were compared using Kruskal-Wallis and Mann Whitney U tests, respectively. Debrief interviews were conducted and qualitatively analyzed. Usability findings from the first (i.e. development) iteration were incorporated into the CDSS design for the second (i.e. validation) iteration. Results: During the CDSS validation iteration, participants took more time to complete tasks with a median (IQR) of 183 (124-247) seconds versus 101 (73.5-197) seconds in the development iteration (p=0.01). This increase in time on task was due to the increase in time spent in the CDSS corresponding to several design changes. Efficiency differences that were observed between pharmacists and physicians in the development iteration were eliminated in the validation iteration. The increased use of the CDSS corresponded to a greater acceptance of CDSS recommended doses in the validation iteration (4% in the first iteration vs. 37.5% in the second iteration, p<0.001). Overall satisfaction did not change statistically between the iterations but the qualitative analysis revealed greater trust in the second prototype. Conclusions: A pharmacogenomic-guided CDSS has been developed using warfarin as the test drug. The final CDSS prototype was trusted by prescribers and significantly increased the time using the tool and acceptance of the recommended doses. This study is an important step toward incorporating pharmacogenomics into CDSS design for clinical testing. Research ArticleBL Background And SignificancePersonalized medicine is an emerging field with the fundamental objective to predict a patient's response in order to individualize drug therapy for improved medication effectiveness and safety. Pharmacogenomic-guided drug therapy is at the forefront of personalized medicine with over 150 United States Food and Drug Administration (FDA) approved drug labels incorporating pharmacogenomic information.[1] Nonetheless, the translation of pharmacogenomic testing into routine clinical practice has been slow due to several obstacles towards its implementation [2][3][4]. Currently, the routine use of clinical pharmacogenomics is limited to centers with resources to overcome these obstacles [5][6][7][8][9].A major obstacle in the implementation of clinical pharmacogenomics is the rapid pace that science has identified genetic polymorphisms that can predict drug response. Th...

Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development

“…2)Cleveland Clinc (University of Florida) physiciansprototypeOwusu-Obeng et al 2014 [36]PG4KDSSt. Jude Children Research Hospital; Physician; Nurse practitioner; Pharmacists; PatientsprototypeBell et al 2014 [12]; Hoffman et al 2014 [39]; Gammal et al 2016 [31]GeneSightNo study location; Physiciansfully developed (CLIA-certified laboratory)Altar et al 2015 [21]Genomic prescribing system (GPS) portalUniversity of Chicago; Physicians; PatientsprototypeO’Donnell et al 2014 [22]; Hussain et al 2016 [23]CLIPMERGE PGxMount Sinai Medical Center; PhysciansprototypeGottesman et al 2013 [8]Pharmacogenetics Testing Implementation Committee (PGTIC) (s/n)National Institutes of Health Clinical Center, Bethesda, MarylandprototypeGoldspiel et al 2014 [9]genAPOslo University; PhysiciansprototypeLaerum et al 2013 [40]YouScriptUniversity of Utah; Physicians; Pharmacistsfully developedBrixner et al 2015 [41]TreatGxUniversity of British Columbia; primary care physicians; pharmacistfully developedDawes et al 2016 [43]Coriell Personalized Medicine Collaborative (CPMC) (s/n)Ohio State University Wexner Medical Center; patientsprototypeSweet et al 2014 [42]Warfarin Dosing prototype (s/n)Wishard Hospital and Veterans Affairs Medical Center Indianapolis; Physicians; PharmacistsprototypeMelton et al 2016 [24]Notes: “s/n” = surrogate name …”

“…Each post-test alert of the 12 pharmacogenomic CDSS referred physicians to additional information about related genes or drugs. Six of them also linked the physicians to full guideline texts and original references [11, 19, 20, 24, 25, 32, 33, 35, 37, 38]. …”

Utilizing a user-centered approach to develop and assess pharmacogenomic clinical decision support for thiopurine methyltransferase

“…Specifically, any usability errors identified during testing were used to enhance the design of the PGx-CDS prototype. A similar, iterative prototyping method has been successfully applied in other research studies of health information technology [41, 42].…”

Noninterruptive Clinical Decision Support Decreases Ordering of Respiratory Viral Panels during Influenza Season

“…To actually improve adherence to guidelines and other best practices, health systems often use Clinical Decision Support Systems (CDSS), which have improved accuracy in cholesterol management, 15 medication-laboratory monitoring, 16 and warfarin dosing. 17 For reducing unnecessary laboratory testing specifically, Clinical Decision Support (CDS) techniques that intrinsically modify computerized physician order entry (CPOE)-such as interruptive alerts and cost display-have been shown to be superior to extrinsic techniques such as education, auditing, and feedback. 18,19 Specifically, CPOE modifications have been shown to reduce duplicate inpatient laboratory orders, 20 improve adherence to guidelines for vitamin-D screening, 21 and reduce nonindicated testing such as erythrocyte sedimentation rate, urinalysis, glucose, international normalized ratio (INR), and others.…”