TY - JOUR
T1 - Making pharmacogenomic-based prescribing alerts more effective
T2 - A scenario-based pilot study with physicians
AU - Overby, Casey Lynnette
AU - Devine, Emily Beth
AU - Abernethy, Neil
AU - McCune, Jeannine S.
AU - Tarczy-Hornoch, Peter
N1 - Funding Information:
The authors would like to thank: Dr. Guilherme Del Fiol for assisting with the development of OpenInfobutton websites; Dr. Joe Smith for assisting with the development of Cerner Discern Expert® triggered alert messages; Dr. Lingtak-Neander Chan for assisting with developing cardiology scenarios; Dr. Cathy Yeung for assisting with developing pharmacogenetics and metabolism resources specific to this study; Drs. Isabelle Ragueneau-Majlessi, Cathy Yeung, and Sophie Argon, for providing input on the interpretation of pharmacogenomic information and for allowing us to use the e-PKgene resource in this study; Drs. Daniel Capurro, Bernardo Goulart, and Veena Shankaran for pilot testing our simulated cases; and Dr. David Fenstermacher who provided feedback on manuscript content. This paper is largely based on Chapter 7 titled “Evaluating the utility of the pharmacogenomics clinical decision support model implementation” from the PhD dissertation of CLO titled “A clinical decision support model for incorporating pharmacogenomics knowledge into electronic health records for drug therapy individualization: a microcosm of personalized medicine.” This research was supported by the following grants: NIH NHGRI #T32 HG000035, NIH NLM #T15 LM07442, and NIH NCRR UL 1RR 025014 (Overby); and AHRQ 5K08 HS014739 (PI: Devine). The funders played no role in the study.
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - To facilitate personalized drug dosing (PDD), this pilot study explored the communication effectiveness and clinical impact of using a prototype clinical decision support (CDS) system embedded in an electronic health record (EHR) to deliver pharmacogenomic (PGx) information to physicians. We employed a conceptual framework and measurement model to access the impact of physician characteristics (previous experience, awareness, relative advantage, perceived usefulness), technology characteristics (methods of implementation-semi-active/active, actionability-low/high) and a task characteristic (drug prescribed) on communication effectiveness (usefulness, confidence in prescribing decision), and clinical impact (uptake, prescribing intent, change in drug dosing). Physicians performed prescribing tasks using five simulated clinical case scenarios, presented in random order within the prototype PGx-CDS system. Twenty-two physicians completed the study. The proportion of physicians that saw a relative advantage to using PGx-CDS was 83% at the start and 94% at the conclusion of our study. Physicians used semi-active alerts 74-88% of the time. There was no association between previous experience with, awareness of, and belief in a relative advantage of using PGx-CDS and improved uptake. The proportion of physicians reporting confidence in their prescribing decisions decreased significantly after using the prototype PGx-CDS system (p=. 0.02). Despite decreases in confidence, physicians perceived a relative advantage to using PGx-CDS, viewed semi-active alerts on most occasions, and more frequently changed doses toward doses supported by published evidence. Specifically, sixty-five percent of physicians reduced their dosing, significantly for capecitabine (p=. 0.002) and mercaptopurine/thioguanine (p=. 0.03). These findings suggest a need to improve our prototype such that PGx CDS content is more useful and delivered in a way that improves physician's confidence in their prescribing decisions. The greatest increases in communication effectiveness and clinical impact of PGx-CDS are likely to be realized through continued focus on content, content delivery, and tailoring to physician characteristics.
AB - To facilitate personalized drug dosing (PDD), this pilot study explored the communication effectiveness and clinical impact of using a prototype clinical decision support (CDS) system embedded in an electronic health record (EHR) to deliver pharmacogenomic (PGx) information to physicians. We employed a conceptual framework and measurement model to access the impact of physician characteristics (previous experience, awareness, relative advantage, perceived usefulness), technology characteristics (methods of implementation-semi-active/active, actionability-low/high) and a task characteristic (drug prescribed) on communication effectiveness (usefulness, confidence in prescribing decision), and clinical impact (uptake, prescribing intent, change in drug dosing). Physicians performed prescribing tasks using five simulated clinical case scenarios, presented in random order within the prototype PGx-CDS system. Twenty-two physicians completed the study. The proportion of physicians that saw a relative advantage to using PGx-CDS was 83% at the start and 94% at the conclusion of our study. Physicians used semi-active alerts 74-88% of the time. There was no association between previous experience with, awareness of, and belief in a relative advantage of using PGx-CDS and improved uptake. The proportion of physicians reporting confidence in their prescribing decisions decreased significantly after using the prototype PGx-CDS system (p=. 0.02). Despite decreases in confidence, physicians perceived a relative advantage to using PGx-CDS, viewed semi-active alerts on most occasions, and more frequently changed doses toward doses supported by published evidence. Specifically, sixty-five percent of physicians reduced their dosing, significantly for capecitabine (p=. 0.002) and mercaptopurine/thioguanine (p=. 0.03). These findings suggest a need to improve our prototype such that PGx CDS content is more useful and delivered in a way that improves physician's confidence in their prescribing decisions. The greatest increases in communication effectiveness and clinical impact of PGx-CDS are likely to be realized through continued focus on content, content delivery, and tailoring to physician characteristics.
KW - Clinical decision support systems
KW - Computer-assisted drug therapy
KW - Computerized physician order entry
KW - Drug safety
KW - Electronic health records
KW - Personalized medicine
KW - Pharmacogenomics
KW - Physician's practice patterns
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U2 - 10.1016/j.jbi.2015.04.011
DO - 10.1016/j.jbi.2015.04.011
M3 - Article
C2 - 25957826
AN - SCOPUS:84930694481
SN - 1532-0464
VL - 55
SP - 249
EP - 259
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
ER -