This document discusses AI entrepreneurship in healthcare. It defines AI entrepreneurship as pursuing opportunities under uncertain conditions to create value through innovation using a valid, automatic, scalable and time-sensitive business model. AI entrepreneurship can be internal within an organization or external. The document provides a checklist for sick care AI entrepreneurship that includes intellectual property, pricing models, regulations, ethics and other considerations. It also discusses examples of business models and the importance of problem scoping and translating solutions for audiences. The overall document provides an overview and guidance for AI entrepreneurship in healthcare.

1. Foundations of
Sick Care AI
Entrepreneurship
Arlen Meyers, MD, MBA
Society of Physician
Entrepreneurs

2. What is AI Entrepreneurship?
• 1.The pursuit of opportunity
• 2.Under VUCA (volatile, uncertain, complex and ambiguous) conditions
• 3.Achieving the goal of creating multiples of stakeholder defined value to meet the
sextuple aims when compared to the competitive offerings or the status quo
• 4. Designing, developing, deploying and disseminating innovation
• 5. Using a VAST (valid, automatic, scaleable, time-sensitive) business model
• 6. To achieve the sextuple aim

3. AI entrepreneurship can be internal or external
• Internal: getting your ideas to internal stakeholders
in your organization i.e. intrapreneurship
• External: getting your ideas to external
stakeholders

4. Sick care AI entrepreneurship check list
• INTELLECTUAL PROPERTY
• AI LAW
• PRICING, REVENUE/BUSINESS MODELS and REIMBURSEMENT
• REGULATORY AFFAIRS
• BUSINESS MODELS
• DIGITAL THERAPEUTICS
• DISSEMINATION and IMPLEMENTATION
• NON-CLINICAL CAREERS
• USE CASE TESTING and ASSESSMENT

5. Sick care AI entrepreneurship check list
• ETHICS AND RESPONSIBLE USE
• AI EHR INTEGRATION AND INTEROPERABILITY
• LEADERPRENEURSHIP and CHANGE MANAGEMENT
• RAISING CAPITAL
• USE CASES
• COMPARATIVE EFFECTIVENESS/ROI
• CYBERSECURITY

6. Examples of business models (how a company
creates and harvests value)

7. At first, be a problem seeker, not a problem
solver
• 1. Problem Spotting: Seeing the real issue
• 2. Problem Scoping: Gaining clarity and specificity
• What, precisely, is the problem we’re trying to solve?
• What outcomes, if improved, would indicate that the problem has actually been solved?
• What data would ideally be available to solve the problem, and what data are actually
available?
• How will the analysis lead to a solution?
3. Problem Shepherding: Getting updates, gathering feedback
4. 4. Solution Translating: Speaking in the language of the audience: deliver a solution
that’s understandable and, therefore, actionable.
4 Skills the Next Generation of Data Scientists Needs to Develop (hbr.org)

8. Thank you
Please join us at www.sopenet.org
See you on the second Tuesday of each month at 7am MT
Respond to the Canvas invitation
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