Randomized clinical trials play an important role in the advancement of science, pursuit of improved health, and quest for more effective delivery of health care. Patients, policy makers, and the medical community recognize these trials as the gold standard in the development of evidence on which to base the delivery of medical care [1]. In spite of their importance, the conduct of these trials is challenging for a variety of reasons, including their cost and complexity to perform [2]. The field of applied clinical informatics is increasingly wellpositioned to facilitate many of the traditional steps required for the successful conduct of a prospective randomized clinical trial, potentially catalyzing the efficient generation of highquality medical evidence and incorporation of the medical record into a learning health system [3, 4]. Most randomized clinical trials follow a relatively set sequence of six steps. A population of patients is screened for eligibility by study inclusion and exclusion criteria. Those patients who meet criteria are considered for enrollment in the trial. Enrolled patients are randomized into study groups (i.e., control vs. intervention(s)). The assigned intervention is delivered and the rate of receipt of study intervention is recorded. Data are collected about patients’ response to the intervention over the course of the trial and outcomes at the end of the trial are measured. Data are synthesized and analyzed to allow interpretation of the results of the trial. Now more than ever, there is burgeoning potential for clinical informatics to facilitate each of these steps. BSniffing^ applications can automatically screen populations of patients for eligibility, by comparing defined inclusion and exclusion criteria with data housed in an electronic medical record [5]. These applications can then be used to flag potential study participants [6]. Once patients are enrolled in a study, randomization assignment can be automatically generated using a real-time randomization scheme built into a study application. Delivery of the assigned intervention can also be facilitated by informatics (e.g., an advisor in the computerized provider electronic order (CPOE) entry system that directs providers to an assigned medication, or an automatic generation of an order to pharmacy, laboratory, etc.) [7] or the informatics applications themselves may be the study intervention (e.g., a clinical decision support system to aid providers in the management of a clinical condition) [8, 9]. Through the automated collection of clinical data from existing sources which make up the larger electronic medical record (e.g., CPOE, pharmacy logs, clinical monitoring systems, laboratory systems, billing systems, registration systems), informatics can provide information regarding compliance with the assigned study intervention (e.g., dispensing of assignedmedication by pharmacy, administration by bedside nurse), changes over time in response to the intervention (e.g., changes in physiologic measures and laboratory values over the course of the study), and trial outcomes (e.g., death, hospital length of stay, development of organ dysfunction). Although these data are typically collected in raw form and then analyzed at defined time points in a study, informatics can also help synthesize, analyze, and present the data in real-time to facilitate monitoring and conduct of the study. (e.g., dashboards for study personnel detailing enrollment, compliance, and safety measures or interim analyses in Bayesian design studies). * Jesse M. Ehrenfeld jesse.ehrenfeld@Vanderbilt.Edu
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Jul 11, 2016
Dominik Freunberger + 1
Open Access
