Bayesian Early-Warning Risk Surveillance for Healthcare Performance Monitoring

Provides Bayesian early-warning surveillance methods for monitoring healthcare performance and patient safety outcomes. The package draws on risk-adjusted monitoring frameworks developed by Steiner et al. (2000) , Spiegelhalter et al. (2003) , Cook et al. (2011) , and Neuburger et al. (2017) . The package implements Bayesian predictive modelling, risk-adjusted monitoring, early-warning signal detection, and graphical tools for continuous quality improvement and healthcare performance assessment.

Overview

bewrs provides tools for Bayesian early-warning risk surveillance, dynamic risk scoring, validation, calibration assessment, decision-theoretic intervention optimisation, and Expected Value of Intervention analysis for healthcare performance monitoring.

Example outputs

Calibration assessment

Risk stratification

Dynamic BEWRS scoring

## Methodological references

The methods implemented in bewrs are motivated by work on Bayesian hierarchical modelling, healthcare provider profiling, prediction model validation, calibration assessment, decision curve analysis, and Bayesian decision theory, including:

• Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis. 3rd ed. CRC Press; 2013.
• Spiegelhalter DJ. Funnel plots for comparing institutional performance. BMJ. 2005;331:302–305. doi:10.1136/bmj.331.7512.302
• Vickers AJ, Elkin EB. Decision curve analysis. Medical Decision Making. 2006;26(6):565–574. doi:10.1177/0272989X06295361
• Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models. Epidemiology. 2010;21(1):128–138. doi:10.1097/EDE.0b013e3181c30fb2
• Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Medicine. 2019;17:230. doi:10.1186/s12916-019-1466-7