Design and Usability of an Electronic Health Record—Integrated, Point-of-Care, Clinical Decision Support Tool for Modeling and Simulation of Antihemophilic Factors

Abstract

Background With the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers. Objectives The aim of this study is to develop and examine the usability of a CDS tool for antihemophilic factor dose individualization. Methods Our development strategy was based on the features associated with successful CDS tools and driven by a formal requirements analysis. The back-end code was based on algorithms developed for manual individualization and unit tested with 23,000 simulated patient profiles created from the range of patient-derived pharmacokinetic parameter estimates defined in children and adults. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users were recruited to participate in traditional usability testing under an institutional review board approved protocol. Results CDS software was developed to systematically walk the point-of-care clinician through dose individualization after seamlessly importing the requisite patient data from the EHR. Classical and population pharmacokinetic approaches were incorporated with clearly displayed estimates of reliability and uncertainty. Users can perform simulations for prophylaxis and acute bleeds by providing two of four therapeutic targets. Testers were highly satisfied with our CDS and quickly became proficient with the tool. Conclusion With early and broad stakeholder engagement, we developed a CDS tool for hematology provider that affords seamless transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent dose selection.

Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome

“…Clinician informational needs and preferences for CDS design is important to optimize acceptance and adherence to CDS recommendations. [26][27][28][29][30] Finally, the relative risk calculations for diLQTS in this investigation were conducted broadly at the drug level across all patients, ignoring the specific patient-level information that may have placed a given patient at greater or lesser risk of diLQTS. Accurate risk prediction of diLQTS is an active area of research in our group, and we anticipate that with improved prediction modeling, a more accurate risk assessment could both improve accuracy of CDS alerts, as well as provide more meaningful guidance for providers at the time of prescription.…”

Human Factors and Organizational Issues in Health Informatics: Innovations and Opportunities

“…Many technologies focused on the creation and evaluation of customized clinical decision support tools. The application of these customized clinical decision support tools varied widely in context, including diagnostic support [ 17 , 132 ], antibiotic stewardship [ 36 , 70 ], screening for and management of chronic conditions [ 53 , 91 , 119 ], identifying individuals at risk for varied clinical outcomes [ 50 , 69 , 87 , 118 ].…”

“…In this synthesis, we focused on categorizing studies based on the expansion of health informatics design opportunities supported by the (relatively) recent ability to customize EHR interfaces, open APIs that allow developers to directly create new software tools leveraging EHR data, and increasingly accessible platforms for mobile app development. [53,91,119], identifying individuals at risk for varied clinical outcomes [50,69,87,118].…”

“…[54,69,93,105,113,118,123,125,135] [14,30,31,35,48,52,55,57,64,66,73,76,83,87,88,91,93,99, 101,103,107-109,111,114,115,120] [14,18,48,89,91,116-118,120,126]…”

The Effect of Electronic Health Record Usability Redesign on Annual Screening Rates in an Ambulatory Setting

“…17 Many studies of EHR usability are limited to descriptions of UCD processes or results of usability evaluations. [18][19][20][21] Other studies of EHR usability focus on poor usability and its association with patient harm and clinician dissatisfaction. [2][3][4]6,22,23 While it is important to continue to describe the impact of poor usability, our understanding of usability is enhanced by careful descriptions of the improvements seen with corrections of usability problems.…”