How the presentation of patient information and decision-support advisories influences opioid prescribing behavior: A simulation study

Abstract

Objective The United States faces an opioid crisis. Integrating prescription drug monitoring programs into electronic health records offers promise to improve opioid prescribing practices. This study aimed to evaluate 2 different user interface designs for prescription drug monitoring program and electronic health record integration. Materials and Methods Twenty-four resident physicians participated in a randomized controlled experiment using 4 simulated patient cases. In the conventional condition, prescription opioid histories were presented in tabular format, and computerized clinical decision support (CDS) was provided via interruptive modal dialogs (ie, pop-ups). The alternative condition featured a graphical opioid history, a cue to visit that history, and noninterruptive CDS. Two attending pain specialists judged prescription appropriateness. Results Participants in the alternative condition wrote more appropriate prescriptions. When asked after the experiment, most participants stated that they preferred the alternative design to the conventional design. Conclusions How patient information and CDS are presented appears to have a significant influence on opioid prescribing behavior.

Examining Racial/Ethnic Differences in Patterns of Opioid Prescribing: Results from an Urban Safety-Net Healthcare System

“…Opioid prescribing is commonly informed by patient characteristics, prescriber attitudes about opioids, as well as decision processing tools [63][64][65][66]. Opioid risk assessment screeners, urine toxicology screenings, standardized pain scales, and prescription drug monitoring systems are Table 2 Adjusted hazard rate (HR) of receiving an opioid use disorder diagnosis subsequent to an opioid prescription by race/ethnicity a *p<0.05; **p<0.01; ***p<0.001 a Sample consisted of adults that had at least three primary care visits, at least one opioid prescription, and did not have an opioid use disorder diagnoses prior to their first opioid prescription during the 32-month study period (n=22,201); regression model adjust for age, sex, chronic pain, anxiety disorder, bipolar disorder, major depression disorder, schizophrenia disorder, alcohol use disorder, cocaine use disorder, cannabis use disorder, and tobacco use disorder.…”

Daring discourse: artificial intelligence in pain medicine, opportunities and challenges

“…Likewise, EHRs are already presenting important data in real time while clinicians are actively ordering medications, such as a window displaying creatinine values when a renally cleared medication is ordered. If this could be enhanced in ways that reduce the cognitive burden on pain providers that are already extended to maximum efficiency, patient outcomes could be improved 4. Lastly, using AI to predict duration of care to best use healthcare resources can help reign in healthcare costs 30.…”

“…CDS related to opioid prescribing has recently been classified as a medical device and will require US Food and Drug Administration (FDA) approval if the system lacks transparency in algorithms or has a closed loop process, which is used by some prescription drug monitoring programs (PDMPs) to calculate opioid risk scores 2 3. CDS has a large body of literature supporting its benefits but also highlighting associated alarm fatigue 4…”

Optimization of Prescription Drug Monitoring Program to Overcome Opioid Epidemic in West Virginia

“…The current PDMPs are mainly focused on Schedule II drugs and lack the functionality to address the current opioid crisis, which consists of a wide range of prescription drugs [ 29 ]. Additionally, the user interface for the current PDMP is not well developed, consisting of irrelevant and unorganized data with a lack of practical workflow [ 30 ]. Apart from that, one of the major limitations in addressing the opioid crisis appropriately is the lack of training provided to prescribers regarding PDMP use [ 31 , 32 ].…”