Predictive Modeling of Nurse Staffing Patterns and Their Association with Inpatient Mortality in Tertiary Care Facilities
Keywords:
Nurse staffing, predictive modeling, inpatient mortality, tertiary care, machine learning, workforce analyticsAbstract
Purpose:
This study evaluates the predictive relationship between nurse staffing patterns and inpatient mortality in tertiary care facilities using advanced predictive modeling techniques.
Design/methodology/approach:
A retrospective cohort design was used, extracting nurse staffing data, patient acuity scores, demographic variables, and mortality outcomes over three years (2020–2023) from electronic health records (EHRs) of three tertiary care hospitals. Machine learning models (random forest, gradient boosting, logistic regression) were developed to determine the influence of staffing variables on mortality.
Findings:
The results demonstrate significant associations between nurse-to-patient ratios, shift distribution patterns, and inpatient mortality, with predictive models achieving accuracy > 85 % in classifying mortality risk.
Practical implications:
Findings support strategic nurse staffing adjustments and real-time predictive tools to minimize mortality risk and improve patient outcomes.
Originality/value:
This study integrates machine learning into staffing policy evaluation with large-scale EHR analytics across multiple tertiary hospitals, advancing evidence-based workforce planning.
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