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CRBHSF

Cyber-Resilient Bayesian Healthcare Surveillance Framework

CRBHSF provides a comprehensive framework for prospective healthcare performance surveillance through the integration of Bayesian risk estimation, latent organisational trust modelling, cyber-resilience assessment, decision-theoretic optimisation, and digital-twin deployment simulation.

The package was developed to support uncertainty-aware healthcare surveillance and proactive operational risk management in complex, digitally dependent healthcare systems.

Highlights

Bayesian healthcare surveillance
Cyber-resilience risk assessment
Latent organisational trust modelling
Prospective deterioration prediction
External validation framework
Ablation and incremental-value analysis
Decision-theoretic intervention prioritisation
Digital-twin deployment simulation
Publication-quality visualisations
Reproducible healthcare analytics workflows

Framework Overview

The CRBHSF workflow integrates healthcare performance data, Bayesian surveillance modelling, organisational trust estimation, cyber-resilience assessment, risk stratification, intervention prioritisation, and digital-twin deployment evaluation within a unified analytical framework.

Why CRBHSF?

Traditional healthcare monitoring systems primarily identify performance issues after they occur. CRBHSF extends conventional surveillance by integrating:

Bayesian uncertainty quantification
Prospective deterioration monitoring
Latent organisational trust estimation
Cyber-resilience risk assessment
External validation workflows
Ablation and incremental-value analysis
Decision-theoretic intervention prioritisation
Capacity-constrained deployment simulation
Digital-twin operational evaluation

Installation

install.packages("remotes")

remotes::install_github("zerish12/CRBHSF")

Core Workflow

library(CRBHSF)

df <- clean_health_data(
  data,
  provider_col = "provider",
  time_col = "month"
)

df <- fit_bayesian_surveillance(
  df,
  y_col = "y",
  n_col = "n"
)

df <- estimate_latent_trust(
  df,
  anomaly_col = "anomaly",
  corruption_col = "corruption",
  cyber_col = "cyber",
  missing_col = "missing"
)

df <- compute_crbhsf_risk(df)

df <- compute_crpr(df)

df <- create_deterioration_outcome(
  df,
  provider_col = "provider",
  time_col = "month",
  value_col = "risk_crbhsf",
  threshold = 0.04
)

validation_results <- validate_surveillance(
  df,
  outcome_col = "future_deterioration",
  score_col = "risk_crbhsf"
)

validation_results

Example Outputs

Cyber-Resilient Risk Distribution

Distribution of cyber-resilient Bayesian surveillance risk scores across healthcare providers.

Incremental Predictive Value of Framework Components

Ablation analysis illustrating the incremental contribution of Bayesian surveillance, latent trust modelling, and cyber-resilience assessment.

Digital-Twin Deployment Impact

Estimated reduction in operational losses under alternative intervention-capacity scenarios.

Main Functions

Function	Purpose
`clean_health_data()`	Healthcare data cleaning and preparation
`create_deterioration_outcome()`	Future deterioration outcome generation
`fit_bayesian_surveillance()`	Bayesian surveillance modelling
`estimate_latent_trust()`	Organisational trust estimation
`compute_crbhsf_risk()`	Cyber-resilient Bayesian risk computation
`compute_crpr()`	Cyber-Resilience Pressure Ratio
`validate_surveillance()`	Model validation and performance assessment
`run_ablation_study()`	Incremental-value evaluation
`compare_ml_benchmarks()`	Machine-learning benchmark comparison
`estimate_evib()`	Expected intervention benefit estimation
`simulate_digital_twin()`	Digital-twin deployment simulation
`plot_risk_distribution()`	Risk visualisation
`plot_ablation_auc()`	Ablation-analysis visualisation
`plot_deployment_impact()`	Deployment-impact visualisation
`generate_surveillance_report()`	Automated surveillance reporting

Research Applications

CRBHSF is designed for:

Healthcare performance surveillance
NHS operational analytics
Hospital performance monitoring
Cyber-resilience assessment
Health system resilience research
Bayesian healthcare analytics
Operational risk prediction
Digital-twin deployment studies
Decision-theoretic intervention planning
Healthcare operations research

Methodological Foundations

The framework draws upon contemporary developments in:

Bayesian healthcare surveillance
Clinical prediction model development
External validation methodology
Decision-analytic model evaluation
Cyber-resilience assessment
Digital-twin healthcare simulation

Key references include:

Efthimiou O, Seo M, Chalkou K, et al. (2024). Developing clinical prediction models: A step-by-step guide. BMJ.
Collins GS, Moons KGM, Dhiman P, et al. (2024). TRIPOD+AI Statement. BMJ.
Vickers AJ, Elkin EB (2006). Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making.

Package Status

Current version: 0.1.0

Status: Active development

Platform: R

License: MIT

Citation

If you use CRBHSF in research, please cite:

Khan MZ, Khan AW (2026).

CRBHSF: Cyber-Resilient Bayesian Healthcare Surveillance Framework.

R package version 0.1.0.

Authors

Muhammad Zahir Khan

Independent Researcher in Health Data Science and Statistical Methodology, United Kingdom

Email: zahirstat007@gmail.com

Abdul Wahid Khan

BS Cyber Security Student

Email: B24F0570CYS128@paf-iast.edu.pk

Future Development

Planned extensions include:

Fully Bayesian hierarchical trust estimation
Real-time healthcare surveillance dashboards
Advanced cyber-threat integration
Interactive digital-twin environments
NHS performance monitoring applications
Shiny-based deployment tools

Project Vision

CRBHSF aims to advance healthcare surveillance beyond conventional retrospective monitoring by integrating uncertainty quantification, cyber resilience, organisational trust, operational risk assessment, and deployment-oriented decision support within a unified analytical framework. The package is intended to support researchers, healthcare organisations, policy analysts, and operational decision-makers seeking proactive and resilience-aware performance management strategies.

License

MIT License

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.