OBJECTIVE: 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.
OBJECTIVE: 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.
Entities:
Keywords:
CPOE; Clinical decision support systems; computer-assisted drug therapy; pharmacogenetics; warfarin
Authors: P Lenzini; M Wadelius; S Kimmel; J L Anderson; A L Jorgensen; M Pirmohamed; M D Caldwell; N Limdi; J K Burmester; M B Dowd; P Angchaisuksiri; A R Bass; J Chen; N Eriksson; A Rane; J D Lindh; J F Carlquist; B D Horne; G Grice; P E Milligan; C Eby; J Shin; H Kim; D Kurnik; C M Stein; G McMillin; R C Pendleton; R L Berg; P Deloukas; B F Gage Journal: Clin Pharmacol Ther Date: 2010-04-07 Impact factor: 6.875
Authors: Leila Ahmadian; Mariette van Engen-Verheul; Ferishta Bakhshi-Raiez; Niels Peek; Ronald Cornet; Nicolette F de Keizer Journal: Int J Med Inform Date: 2010-12-17 Impact factor: 4.046
Authors: Jonathan M Teich; Jerome A Osheroff; Eric A Pifer; Dean F Sittig; Robert A Jenders Journal: J Am Med Inform Assoc Date: 2005-03-31 Impact factor: 4.497
Authors: Daniel S Budnitz; Daniel A Pollock; Kelly N Weidenbach; Aaron B Mendelsohn; Thomas J Schroeder; Joseph L Annest Journal: JAMA Date: 2006-10-18 Impact factor: 56.272
Authors: Kristin W Weitzel; Amanda R Elsey; Taimour Y Langaee; Benjamin Burkley; David R Nessl; Aniwaa Owusu Obeng; Benjamin J Staley; Hui-Jia Dong; Robert W Allan; J Felix Liu; Rhonda M Cooper-Dehoff; R David Anderson; Michael Conlon; Michael J Clare-Salzler; David R Nelson; Julie A Johnson Journal: Am J Med Genet C Semin Med Genet Date: 2014-03-10 Impact factor: 3.908
Authors: Stephen E Kimmel; Benjamin French; Scott E Kasner; Julie A Johnson; Jeffrey L Anderson; Brian F Gage; Yves D Rosenberg; Charles S Eby; Rosemary A Madigan; Robert B McBane; Sherif Z Abdel-Rahman; Scott M Stevens; Steven Yale; Emile R Mohler; Margaret C Fang; Vinay Shah; Richard B Horenstein; Nita A Limdi; James A S Muldowney; Jaspal Gujral; Patrice Delafontaine; Robert J Desnick; Thomas L Ortel; Henny H Billett; Robert C Pendleton; Nancy L Geller; Jonathan L Halperin; Samuel Z Goldhaber; Michael D Caldwell; Robert M Califf; Jonas H Ellenberg Journal: N Engl J Med Date: 2013-11-19 Impact factor: 91.245
Authors: Alissa L Russ; Michael Weiner; Scott A Russell; Darrell A Baker; W Jeffrey Fahner; Jason J Saleem Journal: Jt Comm J Qual Patient Saf Date: 2012-12
Authors: P H O'Donnell; A Bush; J Spitz; K Danahey; D Saner; S Das; N J Cox; M J Ratain Journal: Clin Pharmacol Ther Date: 2012-08-29 Impact factor: 6.875
Authors: Marc Hinderer; Martin Boeker; Sebastian A Wagner; Martin Lablans; Stephanie Newe; Jan L Hülsemann; Michael Neumaier; Harald Binder; Harald Renz; Till Acker; Hans-Ulrich Prokosch; Martin Sedlmayr Journal: BMC Med Inform Decis Mak Date: 2017-06-06 Impact factor: 2.796
Authors: Khoa A Nguyen; Himalaya Patel; David A Haggstrom; Alan J Zillich; Thomas F Imperiale; Alissa L Russ Journal: BMC Med Inform Decis Mak Date: 2019-10-17 Impact factor: 2.796