# Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions

## TL;DR

This paper summarizes lessons learned from academic healthcare institutions on how to successfully identify data science projects in healthcare.

## Contribution

The paper provides actionable recommendations for identifying data science scenarios in healthcare based on cross-institutional experiences.

## Key findings

- Successful approaches include promoting idea portfolios and articulating value propositions.
- Alignment with organizational priorities and securing project champions are critical for success.
- Content analysis revealed common themes across institutions for effective scenario identification.

## Abstract

To describe successful and unsuccessful approaches to identify scenarios for data science implementations within healthcare settings and to provide recommendations for future scenario identification procedures.

Representatives from seven Toronto academic healthcare institutions participated in a one-day workshop. Each institution was asked to provide an introduction to their clinical data science program and to provide an example of a successful and unsuccessful approach to scenario identification at their institution. Using content analysis, common observations were summarized.

Observations were coalesced to idea generation and value proposition, prioritization, approval and champions. Successful experiences included promoting a portfolio of ideas, articulating value proposition, ensuring alignment with organization priorities, ensuring approvers can adjudicate feasibility and identifying champions willing to take ownership over the projects.

Based on academic healthcare data science program experiences, we provided recommendations for approaches to identify scenarios for data science implementations within healthcare settings.