Using artificial intelligence to identify patients with migraine and associated symptoms and conditions within electronic health records.

BACKGROUND: Real-world evidence (RWE)-based on information obtained from sources such as electronic health records (EHRs), claims and billing databases, product and disease registries, and personal devices and health applications-is increasingly used to support healthcare decision making. There is variability in the collection of EHR data, which includes "structured data" in predefined fields (e.g., problem list, open claims, medication list, etc.) and "unstructured data" as free text or narrative. Healthcare providers are likely to provide more complete information as free text, but extracting meaning from these fields requires newer technologies and a rigorous methodology to generate higher-quality evidence. Herein, an approach to identify concepts associated with the presence and progression of migraine was developed and validated using the complete patient record in EHR data, including both the structured and unstructured portions. METHODS: "Traditional RWE" approaches (i.e., capture from structured EHR fields and extraction using structured queries) and "Advanced RWE" approaches (i.e., capture from unstructured EHR data and processing by artificial intelligence [AI] technology, including natural language processing and AI-based inference) were evaluated against a manual chart abstraction reference standard for data collected from a tertiary care setting. The primary endpoint was recall; differences were compared using chi square. RESULTS: Compared with manual chart abstraction, recall for migraine and headache were 66.6% and 29.6%, respectively, for Traditional RWE, and 96.8% and 92.9% for Advanced RWE; differences were statistically significant (absolute differences, 30.2% and 63.3%; P < 0.001). Recall of 6 migraine-associated symptoms favored Advanced RWE over Traditional RWE to a greater extent (absolute differences, 71.5-88.8%; P < 0.001). The difference between traditional and advanced techniques for recall of migraine medications was less pronounced, approximately 80% for Traditional RWE and ≥ 98% for Advanced RWE (P < 0.001). CONCLUSION: Unstructured EHR data, processed using AI technologies, provides a more credible approach to enable RWE in migraine than using structured EHR and claims data alone. An algorithm was developed that could be used to further study and validate the use of RWE to support diagnosis and management of patients with migraine.

Detailing experienced nurse decision making during acute patient care simulations.

INTRODUCTION: Nurse decision making (DM) is critical for patient safety. Eye-tracking methods can effectively assess nurse DM. The purpose of this pilot study was to use eye-tracking methods to assess nurse DM during a clinical simulation. MATERIALS AND METHODS: Experienced nurses managed a simulated patient manikin who suffered from a stroke mid-simulation. We assessed nurses' gaze patterns prior to and after the stroke. DM in general was assessed by nursing faculty using a clinical judgement rubric, and dichotomously based on recognition of the stroke or not. RESULTS: Data from eight experienced nurses was examined. For the nurses who recognized the stroke, visual attention was focused on the vital sign monitor and patient's head, which suggest those locations were consistently examined for correct decision-makers. CONCLUSIONS: Dwell time on general AOIs was associated with poorer DM, which may reflect poorer pattern recognition. Eye-tracking metrics may be effective to objectively assess nurse DM.