Let’s Kill the Risk Matrix: Better Risk Assessment for HealthTech Decisions

Risk matrices introduce systematic errors in decision-making. Motivated by cases of risk assessment in health technology (e.g., third-party cybersecurity risk in hospitals, medical device vulnerability assessments), we demonstrate the systematic failure of risk matrices and how to implement a better alternative.

Agenda

• The case for improving health tech risk assessment

• What's wrong with risk matrices, and why we should replace them

• A better alternative you can implement today (demo)

• Discussion, Q&A

About the Speakers

Shannon Lantzy is focused on getting healthcare market innovation - getting solutions faster, better, cheaper, and with more delight along the way. Specifically, we're aiming to improve third party risk assessment for healthcare stakeholders (e.g., hospitals, regulators). Dr. Lantzy has spent 20+ years consulting in R&D for NASA, FDA, and medical device manufacturers. Her Ph.D. research and academic publications center around the economics of information asymmetry, behavioral economics, and consumer decision-making under uncertainty. 

Sara Eggers is a decision scientist with over 20 years of methodological expertise and 13+ years FDA regulatory experience. Dr. Eggers spent 5 years as Director of FDA Center for Drugs’ Decision Support and Analysis Staff. Her experience spans regulatory decision-making, pharmaceutical benefit-risk assessment, patient-focused drug development, food and drug safety, and the landscape of problematic substance use.

Peter Mallik is the COO of Hubbard Decision Research. Hubbard Decision Research has developed quantitative analysis solutions to Information Technology investments, military logistics, entertainment media, major policy decisions, and business operations. Customers range from small businesses to Fortune 500 companies. A range of organizations that need to measure the immeasurable and make big decisions without perfect information have been using the methods of Doug Hubbard since 1997.

The solutions developed by Hubbard Decision Research are both practical and scientifically validated. They have been applied to problems like:

• Business cases for investments with many uncertain and intangible benefits

• Quantitative analysis solutions to risk analysis and risk management

• Performance metrics that are based on objective measures and selected for measurable impacts on decisions