Background: In the inpatient setting, opioids are the most commonly prescribed medication and the 2nd most frequent cause of adverse drug events (ADE).  Identifying patients at high risk for ADEs related to narcotics is essential.  These ADEs include ileus, altered mental status, and respiratory depression. Obstructive sleep apnea, organ impairment, and other medication use (i.e. benzodiazepines) are well-known risk factors for ADEs due to opioid administration.  These risks factors can enable providers to identify patients most likely to have an intervenable ADE. However, many institutions do not systematically screen patients for these risk factors prior to prescribing opioids. For example, at our institution, the baseline screening rate for patients at high risk for opiate-related ADEs is 7.5% (SD 5.3%) on one of our medical units.

Purpose: To use real-time patient-level data to identify individuals at highest risk of opioid-related ADEs, provide recommendations for providers to prevent these ADEs, and  integrate this logic into a novel electronic health record-based (EHR) patient safety dashboard. (Figure).

Description: Through an iterative and interdisciplinary design approach, we integrated risk factors for opioid-related ADEs into a larger patient safety dashboard. The opioid logic focuses on identifying patients with the following risks: (1) respiratory depression (RASS score or respiratory rate), (2) moderate-to-large doses of narcotics (> 50 mg of morphine equivalent per day), (3) simultaneous sedatives and/or narcotics,  (4) IV narcotic use in patients on oral diets, (5) 24 hour pain score > 8, (6) narcotics with no adjunctive medications (i.e. Tylenol, NSAIDS), (7) no bowel regimen while getting opioids, and (8) high-risk comorbid conditions (OSA, etc.). The dashboard is presented as a stoplight report with each patient’s name followed by green (low risk), yellow (risky state), or red (action needed) flag (figure). Each red flag comes with a recommended provider action item (i.e. patient on opioids, no adjunctive pain medications ordered), while each yellow flag describes the risky state. The dashboard can be accessed in real time by all providers and nurses through the EHR.

Conclusions: Real time data collection in the EHR can be used to identify patients with comorbidities or risky clinical states that place them at higher risk for adverse drug events related to opioids.  The real-time nature gives clinicians the opportunity to intervene before adverse events occur.  The impact on ADE rate and prescribing patterns is currently under investigation. We hope this can increase the rate of screening for patients at high risk for ADEs to nearly 100% by minimizing the barriers to identify this information.