Clinical evaluation of a machine learning-based early warning system for patient deterioration.

BACKGROUND: The implementation and clinical impact of machine learning-based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-time, machine learning-based early warning system for patient deterioration used in the general internal medicine (GIM) unit of an academic medical centre. METHODS: In this nonrandomized, controlled study, we evaluated the association between the implementation of a machine learning-based early warning system and clinical outcomes. We used propensity score-based overlap weighting to compare patients in the GIM unit during the intervention period (Nov. 1, 2020, to June 1, 2022) to those admitted during the pre-intervention period (Nov. 1, 2016, to June 1, 2020). In a difference-indifferences analysis, we compared patients in the GIM unit with those in the cardiology, respirology, and nephrology units who did not receive the intervention. We retrospectively calculated system predictions for each patient in the control cohorts, although alerts were sent to clinicians only during the intervention period for patients in GIM. The primary outcome was non-palliative in-hospital death. RESULTS: The study included 13 649 patient admissions in GIM and 8470 patient admissions in subspecialty units. Non-palliative deaths were significantly lower in the intervention period than the pre-intervention period among patients in GIM (1.6% v. 2.1%; adjusted relative risk [RR] 0.74, 95% confidence interval [CI] 0.55-1.00) but not in the subspecialty cohorts (1.9% v. 2.1%; adjusted RR 0.89, 95% CI 0.63-1.28). Among high-risk patients in GIM for whom the system triggered at least 1 alert, the proportion of non-palliative deaths was 7.1% in the intervention period, compared with 10.3% in the pre-intervention period (adjusted RR 0.69, 95% CI 0.46-1.02), with no meaningful difference in subspecialty cohorts (10.4% v. 10.6%; adjusted RR 0.98, 95% CI 0.60-1.59). In the difference-indifferences analysis, the adjusted relative risk reduction for non-palliative death in GIM was 0.79 (95% CI 0.50-1.24). INTERPRETATION: Implementing a machine learning-based early warning system in the GIM unit was associated with lower risk of non-palliative death than in the pre-intervention period. Machine learning-based early warning systems are promising technologies for improving clinical outcomes.

Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration.

Hospital early warning systems that use machine learning (ML) to predict clinical deterioration are increasingly being used to aid clinical decision-making. However, it is not known how ML predictions complement physician and nurse judgment. Our objective was to train and validate a ML model to predict patient deterioration and compare model predictions with real-world physician and nurse predictions. DESIGN: Retrospective and prospective cohort study. SETTING: Academic tertiary care hospital. PATIENTS: Adult general internal medicine hospitalizations. MEASUREMENTS AND MAIN RESULTS: We developed and validated a neural network model to predict in-hospital death and ICU admission in 23,528 hospitalizations between April 2011 and April 2019. We then compared model predictions with 3,374 prospectively collected predictions from nurses, residents, and attending physicians about their own patients in 960 hospitalizations between April 30, and August 28, 2019. ML model predictions achieved clinician-level accuracy for predicting ICU admission or death (ML median F1 score 0.32 [interquartile range (IQR) 0.30-0.34], AUC 0.77 [IQ 0.76-0.78]; clinicians median F1-score 0.33 [IQR 0.30-0.35], AUC 0.64 [IQR 0.63-0.66]). ML predictions were more accurate than clinicians for ICU admission. Of all ICU admissions and deaths, 36% occurred in hospitalizations where the model and clinicians disagreed. Combining human and model predictions detected 49% of clinical deterioration events, improving sensitivity by 16% compared with clinicians alone and 24% compared with the model alone while maintaining a positive predictive value of 33%, thus keeping false alarms at a clinically acceptable level. CONCLUSIONS: ML models can complement clinician judgment to predict clinical deterioration in hospital. These findings demonstrate important opportunities for human-computer collaboration to improve prognostication and personalized medicine in hospital.

Nurses' death anxiety, comfort level during communication with patients and families regarding death, and exposure to communication education: a quantitative study.

The first purpose of this study was to determine if a nurse's death anxiety is related to the comfort level of the nurse during communication with patients and families regarding death. The second purpose was to explore whether nurses reported having been exposed to communication education regarding dealing with difficult subjects such as death and whether this exposure was related to comfort level of the nurse during communication with patients and families regarding death. Findings of this study benefit nurse educators and nurses involved in staff development because the results indicate that comfort level of the nurse during communication with patients and families is adversely affected by an increase in the nurse's own death anxiety, and positively affected by exposure to communication education. Thus, these results indicate a need for education in this area. The next step is to identify the most effective type, objectives, and content of this type of education.

What do nurses know about teaching patients?

A quasiexperimental study was conducted to ascertain what nurses know about teaching patients and whether a planned education offering could increase the knowledge nurses have about the teaching process in patient education. A convenience sample of 44 nurses participated in a pretest/posttest and 1- to 2-month follow-up test on the teaching process. Knowledge deficit was present and learning evident. Nurse educators should instruct staff on the teaching process, giving nurses the necessary skills to deliver effective patient education.