A Potential Answer to the Alert Override Riddle: Using Patient Attributes to Predict False Positive Alerts.

Electronic health records (EHRs) use alerts to help prevent medical errors, yet clinicians override many of these alerts due to desensitization from constant exposure (alert fatigue). We hypothesize that a clinician might override an alert warning about the dangers of a treatment if the patient's health is so poor that the treatment is worth the risk or if a patient's health suggests the treatment is not needed. We used logistic regression with general estimating equations to determine if the Early Warning Score (EWS), a measurement used to predict critical care need, could be used to predict alert overrides. EWS was a significant predictor of overrides for three alerts. Although EWS could not predict overrides for all alert rules, these results suggest that EWS may be helpful for some alerts, but that additional EHR data will be needed for predicting override behavior to a useful degree.

Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.

OBJECTIVE: Clinical informatics researchers depend on the availability of high-quality data from the electronic health record (EHR) to design and implement new methods and systems for clinical practice and research. However, these data are frequently unavailable or present in a format that requires substantial revision. This article reports the results of a review of informatics literature published from 2010 to 2016 that addresses these issues by identifying categories of data content that might be included or revised in the EHR. MATERIALS AND METHODS: We used an iterative review process on 1,215 biomedical informatics research articles. We placed them into generic categories, reviewed and refined the categories, and then assigned additional articles, for a total of three iterations. RESULTS: Our process identified eight categories of data content issues: Adverse Events, Clinician Cognitive Processes, Data Standards Creation and Data Communication, Genomics, Medication List Data Capture, Patient Preferences, Patient-reported Data, and Phenotyping. DISCUSSION: These categories summarize discussions in biomedical informatics literature that concern data content issues restricting clinical informatics research. These barriers to research result from data that are either absent from the EHR or are inadequate (e.g., in narrative text form) for the downstream applications of the data. In light of these categories, we discuss changes to EHR data storage that should be considered in the redesign of EHRs, to promote continued innovation in clinical informatics. CONCLUSION: Based on published literature of clinical informaticians' reuse of EHR data, we characterize eight types of data content that, if included in the next generation of EHRs, would find immediate application in advanced informatics tools and techniques.