Design and Testing of an EHR-Integrated, Busulfan Pharmacokinetic Decision Support Tool for the Point-of-Care Clinician - Abdel-Rahman, Susan M.; Breitkreutz, Matthew L.; Bi, Charlie; Matzuka, Brett J.; Dalal, Jignesh; Casey, K. Leigh; Garg, Uttam; Winkle, Sara; Leeder, J. Steven; Breedlove, JeanAnn; Rivera, Brian (Frontiers in Pharmacology, 2016)
BACKGROUND: Busulfan demonstrates a narrow therapeutic index for which clinicians routinely employ therapeutic drug monitoring (TDM). However, operationalizing TDM can be fraught with inefficiency. We developed and tested software encoding a clinical decision support tool (DST) that is embedded into our electronic health record (EHR) and designed to streamline the TDM process for our oncology partners. METHODS: Our development strategy was modeled based on the features associated with successful DSTs. An initial Requirements Analysis was performed to characterize tasks, information flow, user needs, and system requirements to enable push/pull from the EHR. Back-end development was coded based on the algorithm used when manually performing busulfan TDM. The code was independently validated in MATLAB using 10,000 simulated patient profiles. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users (n=28) were recruited to participate in traditional usability testing under an IRB approved protocol. RESULTS: Decision support software was developed to systematically walk the point-of-care clinician through the TDM process. The system is accessed through the EHR which transparently imports all of the requisite patient data. Data are visually inspected and then curve fit using a model-dependent approach. Quantitative goodness-of-fit are converted to single tachometer where “green” alerts the user that the model is strong, “yellow” signals caution and “red” indicates that there may be a problem with the fitting. Override features are embedded to permit application of a model-independent approach where appropriate. Simulations are performed to target a desired exposure or dose as entered by the clinician and the DST pushes the user approved recommendation back into the EHR. Usability testers were highly satisfied with our DST and quickly became proficient with the software. CONCLUSIONS: With early and broad stake-holder engagement we developed a clinical DST for the non-pharmacologist. This tools affords our clinicians the ability to seamlessly transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent prescription order entry.