Commercializing AI in Healthcare: The Jobs to be Done
This is an a16z Blog Post from September 18th, 2023
Authors: Jay Rughani, Daisy Wolf, Vijay Pande, and Julie Yoo
In enterprise healthcare, there are many “Jobs to be Done” (JTBD) for AI. To this end, we present the first part of the 6th episode of the Digital Health Go-to-Market Playbook series–Commercializing AI in Healthcare.
This piece, Part A, uses Clay Christensen’s Jobs to be Done lens, along with an assessment of viable product wedges and business models, to share what we see as the most promising applications of AI in enterprise healthcare. We also describe which use cases are more conducive to the application of generative AI in the form of large language models (LLMs) versus traditional machine learning (ML).
In Part B (coming soon), we’ll be summarizing interviews with key decision makers at leading payor and provider enterprises on what entrepreneurs should consider as they bring their AI products to market.
AI Jobs to be Done in enterprise healthcare
Enterprise healthcare tasks are well-suited to AI for two reasons:
- The tasks are based on large amounts of complex and esoteric data that must be synthesized, in real time, to inform a consequential decision or action.
- The tasks are labor-intensive, with low historical adoption of traditional software products to facilitate them. We’ve previously described the leapfrog opportunity that exists because of this point.
Broadly speaking, we classify the opportunities for AI across two dimensions: the complexity of the task and the cost of a mistake. Enterprise healthcare tasks are highly complex and unforgiving to errors. Building specialist AIs to perform healthcare tasks offers the most challenging technical problems in the field today, as well as the greatest opportunity for impact.
Building specialist AIs to perform healthcare tasks offers the most challenging technical problems in the field today, as well as the greatest opportunity for impact.A16zBioHealth
Within the healthcare tasks, we further subdivide along two axes: clinical vs. non-clinical, and consumer- vs. professional-facing. Each box in the map below represents what we believe is a viable wedge for a company building opportunity, where taking an AI-native approach will confer a competitive edge, and where a credible business model exists.
Criteria
In addition to the general factors mentioned above, what specifically makes a JTBD worthy to be on this opportunity map? We applied the following criteria to make that determination:
Area of high spend on highly trained labor
A top pain point for healthcare enterprises these days is workforce retention, especially among workers who require significant upfront and ongoing training (the average hospital turned over 100% of their workforce in the last 5 years, with labor costs growing approximately 21% over the past three years). These are areas where hiring “AI staff members” (versus buying software and mandating employees to change their workflow to use it; see third criterion below) has high potential for uptake. In addition, there tends to be a wide gradient of labor challenges across different domains; e.g. the revenue cycle department likely has more labor pressure than the marketing department.
Potential for 10x performance with AI
Relatedly, areas in which humans are prone to error or are generally slow and inefficient (even when supported by software products) are most likely to benefit from AI approaches. For instance, in prior authorizations, a recent AMA survey found physicians and their staff spend approximately 14 hours per week completing PAs, while the GAO estimates that federal agencies made upwards of $128 billion worth of incorrect payments from Medicare and Medicaid to providers in 2022.
Areas with low adoption of software
Healthcare enterprises are more likely to adopt AI if its cost benefit is at least an order of magnitude (and ideally more!) better than the status quo. Therefore, we’re likely to see a stronger opportunity in areas that have a low penetration of existing software tools, where AI cost benefit is being compared to human labor, versus software. For example, medical scribing is one of the areas where there appears to be high uptake of AI solutions because humans currently perform the majority of scribing tasks.
Well understood regulatory risk
Healthcare is one of the only industries that has at least one established AI regulatory framework (e.g. FDA’s Software as a Medical Device, or SaMD, pathway, and the FDA 510K pathway). In the areas not covered by the existing regulatory regime, non-clinical products may feel safer to C-suites than clinical products. In the realm of clinical use cases, founders will have to account for the need and preference of the enterprise to have “humans in the loop” versus a fully autonomous system.
Established revenue rails and financial incentives
Some of the less-well-funded areas for health systems, such as social needs coordination, might find it harder to justify “hiring” AI than areas of higher, more specialized labor spend, or areas in which an AI product could simply ride existing revenue or reimbursement rails–either for substitute human services or software tools.
Note that some JTBDs did not make it onto the map based on these criteria, per the note in the graphic above (e.g. marketing, conflict of interest management, scientific journal/grant writing). That said, those task areas may rise in importance over time as technology, financial rails, and regulatory frameworks develop.
Conclusion
Per our healthcare AI thesis, we believe that the best builders of enterprise healthcare AI solutions will both understand how to exploit the latest advancements in AI, and, more importantly, how to commercialize a product with a durable go-to-market strategy. While each of the JTBDs described above are viable product wedges, we also believe that the winning companies will have multiple integrated products that perform a wide breadth of tasks, since healthcare enterprises are continuing to consolidate their vendor relationships and relying on each partner to cover a large surface area of use cases.
The healthcare industry desperately needs entrepreneurs building solutions to the scalability and cost structure problems that can uniquely be addressed by AI. Stay tuned for Part B, in which we hear from the buyers of enterprise AI solutions and discuss defensibility, pricing, and packaging of these AI solutions.
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CONTRIBUTORS
Jay Rughani is an investing partner on the Bio+Health team, focused on AI & data products across healthcare and life sciences.
Daisy Wolf is an investing partner on the Bio + Health team, focused on consumer health, the intersection of healthcare and fintech, and healthcare software.
Vijay Pande is the founding General Partner of a16z Bio + Health, focused on the cross-section of biology and computer science.
Julie Yoo is a General Partner on the Bio + Health team, focused on transforming how we access, pay for, and experience healthcare.
MORE FROM THESE CONTRIBUTORS
- Where Will AI Have the Biggest Impact? Healthcare.Daisy Wolf and Vijay Pande
- Washington: Will You Let AI Make Us Healthier?Colin Rom, Jeff Clark, Jay Rughani, and Vijay Pande
- AI at the Intersection: The a16z Investment Thesis on AI in Bio + HealthVijay Pande
- The New Industrial Revolution: Bio x AIVijay Pande
- Investing in Hippocratic AIJulie Yoo and Justin Larkin
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