Artificial Intelligence

Scaling Adoption of Medical AI


Medical AI is on the brink of improving outcomes for countless patients, prompting a trio of all-star researchers to pen an NEJM AI article tackling what might be its biggest obstacle: real-world adoption.

What drives real-world adoption? Those who have been around the block as many times as Dr. Michael Abramoff, Dr. Tinglong Dai, and Dr. James Zou are all-too familiar with the answer… Reimbursement makes the world go ‘round.

To help medical AI developers get their tools in front of the patients who need them, the authors explore the pros and cons of current paths to reimbursement, while offering novel frameworks that could lead to better financial sustainability.

Traditional Fee-for-Service treats medical AI similarly to how new drugs or medical devices are reimbursed, and is a viable path for AI that can clear the hurdle of demonstrating improvements to clinical outcomes, health equity, clinician productivity, and cost-effectiveness (e.g. AI for diabetic eye exams).

  • Meeting these criteria is a prerequisite for adopting AI in healthcare, yet even among the 692 FDA-authorized AI systems, few have been able to pass the test. The approach carries substantial risk in terms of time and resources for AI developers.
  • Despite those limitations, FFS might be appropriate for AI because health systems are adept at assessing the financial impact of new technologies under it, and reimbursement through a CPT code provides hard-to-match financial sustainability.

Value-based care frameworks provide reimbursement on the basis of patient- or population-related metrics (MIPS, HEDIS, full capitation), and obtaining authorization for medical AI to “count” toward closing care gaps for MIPS and HEDIS has been shown to be considerably more straightforward than attaining a CPT code.

  • That said, if a given measure is not met (e.g. 80% of the population must receive an annual diabetic eye examination), the financial benefit of closing even three quarters of that care gap is typically zero, potentially disincentivizing AI adoption.

Given the limitations of existing pathways, the authors offer a potential new approach that’s derived from the Medicare Part B model, which reimburses drugs administered in an outpatient setting based on a “cost plus” markup.

  • Here, providers could acquire the rights to use AI, then get reimbursed based on the average cost of the service plus a specified margin, contingent upon CMS coverage of a particular CPT code.
  • This model essentially splits revenue between AI creators and users, and would alleviate some of the tensions of both FFS and VBC models.

The Takeaway

Without sustainable reimbursement, widespread medical AI adoption won’t be possible. Although the quest continues for a silver bullet (even the authors’ revenue-sharing model still carries the risk of overutilization and requires the creation of new CPT codes), exploring novel approaches is essential given the challenges of achieving reimbursement through existing FFS and VBC pathways.

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