Where to start with AI in your health system
summary
Summarizes a practical path: focuses on operational AI, especially revenue-cycle automation rather than experimental disease models, and recommends scoping, governance and change management. Cites market estimates ($6.6B by 2021, $150B savings by 2026) and hard numbers to target like 95% registration accuracy and <5% initial denial rate. Warns that ~50% of RPA projects underdeliver, Forrester shows each $1 of RPA software needs ~$3.50 in services and ~77% of cost is hidden change management. Gives a concrete example: prior authorization bots that log into payer portals and check status.
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chapters (16)
- 0:00Intro
- 2:06Meet Your Presenter
- 4:13Al Investments in Health Systems
- 7:26How can Aland Automation Alleviate Revenue Cycle Challenges?
- 10:26Selecting the Right Partner is Crucial
- 11:22Unlocking Massive Value Requires a Data-driven Approach
- 13:09Considerations to ensure your Program Unlocks Massive Value
- 14:13Evaluating Total Cost of Ownership: Hard Costs
- 15:21Integrating Revenue Cycle KPIs as a way to measure value
- 16:55What makes a good candidate process for automation?
- 18:07Automation Value Levers
- 19:42Workflow Example: Prior Authorization Management
- 20:52Effective Change Management and Algement of Resources are Critical for Success
- 21:52Pillars of Focus for Successful Change Management
- 23:14Questions to consider When Building your Change Management Program
- 25:31Example Governance Structure
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