Implementing Electronic Health Record Default Settings to Reduce Opioid Overprescribing: A Pilot Study.

Objective: To pilot test the effectiveness, feasibility, and acceptability of instituting a 15-pill quantity default in the electronic health record for new Schedule II opioid prescriptions. Design: A mixed-methods pilot study in two health systems, including pre-post analysis of prescribed opioid quantity and focus groups or interviews with prescribers and health system administrators. Methods: We implemented a 15-pill electronic health record default for new Schedule II opioids and assessed opioid quantity before and after implementation using electronic health record data on 6,390 opioid prescriptions from 448 prescribers. We then analyzed themes from focus groups and interviews with four staff members and six prescribers. Results: The proportion of opioid prescriptions for 15 pills increased at both sites after adding an electronic health record default, with one reaching statistical significance (from 4.1% to 7.2% at CHC, P = 0.280, and 15.9% to 37.2% at WVU, P < 0.001). The proportion of 15-pill prescriptions increased among high-prescribing departments and among most high- and low-frequency prescribers, except for low-frequency prescribers at CHC. Sites reported limited challenges in instituting the default, although ease of implementation varied by electronic health record vendor. Most prescribers were not aware of the default change and stated that they made prescribing decisions based on patient clinical characteristics rather than defaults. Conclusions: This pilot provides initial evidence that changing default settings can increase the number of prescriptions at the default level. This low-cost and relatively simple intervention could have an impact on opioid overprescribing. However, default settings should be selected carefully to avoid unintended consequences.

Quality Improvement Intervention for Reduction of Redundant Testing.

Laboratory data are critical to analyzing and improving clinical quality. In the setting of residual use of creatine kinase M and B isoenzyme testing for myocardial infarction, we assessed disease outcomes of discordant creatine kinase M and B isoenzyme +/troponin I (-) test pairs in order to address anticipated clinician concerns about potential loss of case-finding sensitivity following proposed discontinuation of routine creatine kinase and creatine kinase M and B isoenzyme testing. Time-sequenced interventions were introduced. The main outcome was the percentage of cardiac marker studies performed within guidelines. Nonguideline orders dominated at baseline. Creatine kinase M and B isoenzyme testing in 7496 order sets failed to detect additional myocardial infarctions but was associated with 42 potentially preventable admissions/quarter. Interruptive computerized soft stops improved guideline compliance from 32.3% to 58% (P < .001) in services not receiving peer leader intervention and to >80% (P < .001) with peer leadership that featured dashboard feedback about test order performance. This successful experience was recapitulated in interrupted time series within 2 additional services within facility 1 and then in 2 external hospitals (including a critical access facility). Improvements have been sustained postintervention. Laboratory cost savings at the academic facility were estimated to be ≥US$635 000 per year. National collaborative data indicated that facility 1 improved its order patterns from fourth to first quartile compared to peer norms and imply that nonguideline orders persist elsewhere. This example illustrates how pathologists can provide leadership in assisting clinicians in changing laboratory ordering practices. We found that clinicians respond to local laboratory data about their own test performance and that evidence suggesting harm is more compelling to clinicians than evidence of cost savings. Our experience indicates that interventions done at an academic facility can be readily instituted by private practitioners at external facilities. The intervention data also supplement existing literature that electronic order interruptions are more successful when combined with modalities that rely on peer education combined with dashboard feedback about laboratory order performance. The findings may have implications for the role of the pathology laboratory in the ongoing pivot from quantity-based to value-based health care.

Reduction in time to first action as a result of electronic alerts for early sepsis recognition.

The use of an electronic alerting system to notify practitioners when a patient meets modified systemic inflammatory response syndrome criteria was hypothesized to decrease the time to goal-directed therapy initiation. This retrospective, before-and-after study analyzed adult patients identified with sepsis or septic shock and compared 30 patients prior to electronic alert initiation with 30 patients after initiation. The primary endpoint was time to any sepsis-related intervention. Patients in the post-alert group demonstrated a shorter time to any sepsis-related intervention by a median difference of 3.5 hours (P = .02). Using computerized medical records to create an electronic alerting system has the potential to identify high-risk patients and initiate interventions sooner. At our institution, the creation of an alerting system with real-time data has decreased the time it takes to begin sepsis workup and treatment.

Assessment of commercial NLP engines for medication information extraction from dictated clinical notes.

PURPOSE: We assessed the current state of commercial natural language processing (NLP) engines for their ability to extract medication information from textual clinical documents. METHODS: Two thousand de-identified discharge summaries and family practice notes were submitted to four commercial NLP engines with the request to extract all medication information. The four sets of returned results were combined to create a comparison standard which was validated against a manual, physician-derived gold standard created from a subset of 100 reports. Once validated, the individual vendor results for medication names, strengths, route, and frequency were compared against this automated standard with precision, recall, and F measures calculated. RESULTS: Compared with the manual, physician-derived gold standard, the automated standard was successful at accurately capturing medication names (F measure=93.2%), but performed less well with strength (85.3%) and route (80.3%), and relatively poorly with dosing frequency (48.3%). Moderate variability was seen in the strengths of the four vendors. The vendors performed better with the structured discharge summaries than with the clinic notes in an analysis comparing the two document types. CONCLUSION: Although automated extraction may serve as the foundation for a manual review process, it is not ready to automate medication lists without human intervention.