Videos and Podcasts
How and Why You Should Assess Bias & Fairness in Healthcare AI Before Deploying to Clinical Workflows
Watch the on-demand session to learn why it's important to evaluate algorithms for bias before deployment and what metrics you can use to assess bias. Plus, get a demo of new product features built precisely for this purpose.
With CMS’ launch of the ACO REACH program bringing attention to its focus on improving health equity, reducing health disparities has become an industry-wide priority. Many providers and payers use machine learning algorithms or rules-based systems for population health, clinical decision support, and other decisions that affect healthcare resource allocation. But if these tools aren’t evaluated for algorithmic bias, they can unintentionally make health disparities worse.
In this webinar, WellSect and Medical Home Network will discuss the importance of evaluating algorithms for bias prior to deployment as well as metrics for assessing bias. We will also demo new features for algorithmic bias evaluation that enable data science teams using the WellSect platform to assess algorithmic bias as part of the process for training and validating machine learning models in healthcare.
Register to watch the webinar on-demand.
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Related Resources
A Framework for Measuring the ROI and Health Equity Impact of AI-Enabled Health Programs
Discover a useful framework that your organization can use to evaluate programs’ ROI and impact on health equity, especially when introducing an artificial intelligence / machine learning component.
Why Most Fairness Metrics Don’t Work in Healthcare AI/ML
Selecting an appropriate definition of fairness is difficult for healthcare algorithms, as they are applied to myriad diverse problems. Read the paper to learn why we need different definitions of fairness and to understand the most ideal fairness metric for population health AI/ML.
AI = ROI How AI Drives Health Outcomes and Tangible ROI in Healthcare
In this webinar with Massachusetts Health Data Consortium, WellSect discusses measuring tangible ROI for predictive systems, creating explainable AI, addressing algorithmic bias, and overcoming the deployment challenges of machine learning models.
