Thank you to all who participated in SymphonyRM’s first Health AI University sessions with SymphonyRM’s Joe Schmid, CTO and Chris Hemphill, AI Director. We hope you found the content valuable as you develop or build upon your strategy to adopt AI to drive outcomes.
But in case you missed Health AI University …
We are sharing the recorded series for leaders and strategists who weren’t able to attend. You can still register and listen to the lessons over the holidays. This four-part series provides an introduction to Artificial Intelligence with an emphasis on simplifying concepts to help healthcare leaders evaluate AI solutions. Each session covers fundamental concepts with application to healthcare and includes a corresponding tool from the Health AI Toolkit to help leaders plan their AI strategy.
Session 1: Understanding AI & the Impact on Healthcare
Joe Schmid introduces AI in simplified terms of “Cheap Prediction” to drive healthcare improvements. He shares a strategy to start with your goal and back into an AI strategy to reach your goals. AI isn’t the goal – it is a low-cost tool to help achieve outcomes.
Toolkit Resource: Health AI University: Strategy Brainstorming Guide
Session 2: Understanding AI & Machine Learning
Joe dives into Machine Learning vs. Rules-Based logic in AI models – explaining how machine learning actually trains the model – feeding results back into the program to make the model perform smarter.
Toolkit Resource: Health AI University: AI Dictionary
Session 3: Understanding Model Performance
Joe and Chris pull the concepts from earlier sessions together – applying machine learning to a predictive model for patients at risk for cardiovascular surgery. While this sounds complex, we don’t have to be data scientists to understand the concepts driving model performance. There really isn’t a “secret sauce”, rather it is important to understand how populations are stratified through model performance.
Toolkit Resources:
Health AI University: 5 Questions for Evaluating AI Models
Health AI University: Comparing Model Performance Interactive Tool
Session 4: Evaluating Vendor Data Pipelines, What’s in the Near Future for AI
Chris brings Health AI University to real-world evaluation strategies for leaders looking at AI vendors. He covers
- Aligning to KPIs
- Change management
- Data management capabilities
- Model transparency
- Data ethics & equity
For your Toolkit, we are taking the final exam for you – finalizing an AI Vendor Evaluation (RFP) template for University participants. When we’ve asked healthcare leaders what barriers they have to getting started, we often hear, “Finding the right people with the right experience to put together an AI RFP”. For this reason, we’ve worked with healthcare leaders, data scientists, contracting experts and end users to do the work for you.
Please keep an eye out for this tool over the holidays. This template should provide comprehensive questions for your organization to incorporate into RFPs, RFIs or other evaluation methods.
Register now for complete access to the 4-part series and toolkits.
Thank you, and Happy Holidays!
