# Connecting data science and translational simulation

## TL;DR

This paper explores how data science can improve healthcare simulations to make them more effective and actionable for quality improvement.

## Contribution

The paper extends Nickson’s IPO model by integrating data science principles to guide translational simulation initiatives.

## Key findings

- Data science principles can enhance quality improvement in translational simulation.
- Case studies demonstrate the practical application of data-driven simulation.
- Integrating data strategies into the IPO model improves simulation outcomes.

## Abstract

Translational simulation is designed to explore and improve healthcare systems and performance. But this potential can only be realised if simulation activities generate actionable insights, using methods that are efficient and cost effective. Robust data strategies are required, embracing established quality‑improvement (QI) frameworks and the recent applications of artificial intelligence. Data collection, analysis and presentation are the primary functions of simulation for quality improvement. This requires translational simulation practitioners to adopt disciplined application of data science, if their diagnostic and interventional simulations are to be translated into tangible gains in healthcare quality and safety.

This article is presented in three parts. First, I review contemporary data science principles in QI and emerging capabilities for data capture, synthesis, and tailored dissemination of findings. Second, I illustrate these principles through case vignettes drawn from the literature. Third, I synthesise these lessons to extend Nickson’s Input–Process–Output model, offering guidance for data strategy development for translational simulation initiatives. By integrating a rigorous data orientation into the foundational IPO schema, I argue that translational simulation can better realise its potential.