Top 4 Health Care AI Investment Trends to Watch

Investments in artificial intelligence (AI) in health care have been soaring over the past decade, particularly in the last several years. Investors have poured more than $30 billion into health care AI startups in the last three years and roughly $60 billion in the last 10 years, notes a new report from the venture capital firm Flare Capital Partners.

Much of the sector’s financial backing over the past decade, which includes providers, health plans and life sciences companies, has been generated by the interest hospitals and health systems have shown in how to deploy the technology to improve clinical and operational performance.

But more capital does not universally equate to more value creation. And nowhere is the dichotomy greater between potential value and the embrace of AI more apparent than in health care, where adoption is relatively early and enormous potential remains, the report’s authors explain.

So, where have AI companies created the greatest value in health care and where do the greatest opportunities still lie for achieving the technology’s vast potential?

4 Takeaways from the Analysis

1 | Stay focused on ROI potential for future AI applications.

Health care organizations typically have been more prudent buyers of AI than other sectors, the report states. That’s partly because hospitals and health systems usually don’t have the same level of resources to commit to AI as other sectors like health plans and life sciences companies. The authors believe health care buyers initially will allocate their growing AI budgets to areas where they have seen a durable return on the investment in the past before standing on unproven ground.

Takeaway

Health care organizations generally have not been quick to buy into the hype surrounding AI and have been weighing carefully financial benefits with the technology’s impact on patient and clinician experiences. AI budgets will prioritize financial, patient engagement and operational throughput value propositions that yield more tangible ROI. And while that’s a good thing, similar ongoing attention and focus will need to be devoted to identifying and capitalizing on opportunities to improve performance with AI.

2 | Clinical decision-support tools may take more time to mature.

Roughly half of health system AI funding has gone toward clinical care startups that facilitate clinical decision-making accuracy while mitigating persistent workforce challenges such as staff shortages and costs. “Despite investor interest, clinical decision support solutions have yielded amongst the lowest maturity rates amongst all health system AI startups (6.8%) while imaging AI solutions have fared slightly better with a 9.9% maturity rate. Furthermore, startups addressing these functional areas have higher capital intensity rates relative to their average valuations and scaled value creation,” the report states.

Takeaway

This likely speaks to the fact that clinical workflows that directly affect care decisions pose the highest risk and liability to care providers. Therefore, these solutions require a higher threshold of accuracy, and their reliability and auditability must continue to be scrutinized closely and regulated heavily, the authors believe. This can lead to longer sales and implementation cycles and make it more difficult for providers to parse the value created by these solutions from decision-making by clinicians. These dynamics can make it more difficult to assess ROI potential.

3 | Examine AI potential to anticipate clinical deterioration in patients.

This is where the most valuable and mature startups seem to be gaining traction. The insights gleaned from these AI uses are being used to develop advanced preventive care plans that are also more personalized, allowing care teams to coordinate care more efficiently and avoid unnecessary utilization.

Takeaway

This area of AI application appears to be a sweet spot for preserving the clinical autonomy that clinicians deeply value. Combined, leading startups are showing that AI can yield valuable resource efficiency and superior clinical outcomes when thoughtfully wrapped into a new care model, the authors state.

4 | Business and patient engagement operations with AI draw growing investor interest.

Financial or back-office AI startups and companies developing AI tools to support patient engagement and revenue-cycle management (RCM) are generating significant investor backing. Patient engagement and RCM tend to be more repetitive, manual and less fraught with direct clinical risk.

Takeaway

AI applications in these areas are among the most mature in health care and can support two significant impacts for health systems — optimizing payment capture and managing labor efficiency.