Purpose: To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity. Methods:In three combined medical-surgical ICUs, we obtained data on demographics, treatment with antipsychotic medications, and outcomes. We applied NLP to caregiver progress notes to diagnose behavioral disturbance and analyzed simultaneous CAM-ICU. Results:We assessed 2313 patients with a median lowest Richmond Agitation-Sedation Scale (RASS) score of − 2 (− 4.0 to − 1.0) and median highest RASS score of 1 (0 to 1). Overall, 1246 (53.9%) patients were NLP-Dx-BD positive (NLP-Dx-BD pos ) and 578 (25%) were CAM-ICU positive (CAM-ICU pos ). Among NLP-Dx-BD pos patients, 539 (43.3%) were also CAM-ICU pos . In contrast, among CAM-ICU pos patients, 539 (93.3%) were also NLP-Dx-BD pos . The use of antipsychotic medications was highest in patients in the CAM-ICU pos and NLP-Dx-BD pos group (24.3%) followed by the CAM-ICU neg and NLP-Dx-BD pos group (10.5%). In NLP-Dx-BD neg patients, antipsychotic medication use was lower at 5.1% for CAM-ICU pos and NLP-Dx-BD neg patients and 2.3% for CAM-ICU neg and NLP-Dx-BD neg patients (overall P < 0.001). Regardless of CAM-ICU status, after adjustment and on time-dependent Cox modelling, NLP-Dx-BD was associated with greater antipsychotic medication use. Finally, regardless of CAM-ICU status, NLP-Dx-BD pos patients had longer duration of ICU and hospital stay and greater hospital mortality (all P < 0.001). Conclusion:More patients were NLP-Dx-BD positive than CAM-ICU positive. NLP-Dx-BD and CAM-ICU assessment describe partly overlapping populations. However, NLP-Dx-BD identifies more patients likely to receive antipsychotic medications. In the absence of NLP-Dx-BD, treatment with antipsychotic medications is rare.

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Objective: The early prediction of hospital admission is important to ED patient management. Using available electronic data, we aimed to develop a predictive model for hospital admission. Methods: We analysed all presentations to the ED of a tertiary referral centre over 7 years. To our knowledge, our data set of nearly 600 000 presentations is the largest reported. Using demographic, clinical, socioeconomic, triage, vital signs, pathology data and keywords in electronic notes, we trained a machine learning (ML) model with presentations from 2015 to 2020 and evaluated it on a held-out data set from 2021 to mid-2022. We assessed electronic medical records (EMRs) data at patient arrival (baseline), 30, 60, 120 and 240 min after ED presentation. Results:The training data set included 424 354 data points and the validation data set 53 403. We developed and trained a binary classifier to predict inpatient admission. On a held-out test data set of 121 258 data points, we predicted admission with 86% accuracy within 30 min of ED presentation with 94% discrimination. All models for different time points from ED presentation produced an area under the receiver operating characteristic curve (AUC) ≥0.93 for admission overall, with sensitivity/specificity/ F1-scores of 0.83/0.90/0.84 for any inpatient admission at 30 min after presentation and 0.81/0.92/0.84 at baseline. The models retained lower but still high AUC levels when separated for short stay units or inpatient admissions. Conclusion:We combined available electronic data and ML technology to achieve excellent predictive performance for subsequent hospital admission. Such prediction may assist with patient flow.