Investments in artificial intelligence (AI) in healthcare have exploded over the past decade, particularly in recent years. Investors have poured more than $30 billion into healthcare AI startups over the past three years and about $60 billion over the past 10 years, a new report notes. report from venture capital firm Flare Capital Partners.
Much of the financial support for the sector over the past decade, which includes providers, health plans and life sciences companies, has been generated by interest by hospitals and health systems in how to deploy technology to improve clinical and operational performance.
But more capital does not universally equate to more value creation. And nowhere is the dichotomy between the potential value and adoption of AI more apparent than in healthcare, where adoption is relatively early and there remains enormous potential, the authors of the paper explain. report.
So where have AI companies created the greatest value in healthcare and where still lie the greatest opportunities to harness the technology’s vast potential?
4 takeaways from the analysis
1 | Stay focused on the ROI potential of future AI applications.
Healthcare organizations have generally been more cautious buyers of AI than other sectors, the report said. This is partly because hospitals and health systems generally do not have the same level of resources to devote to AI as other sectors like health plans and life sciences companies . The authors believe healthcare buyers will initially allocate their growing AI budgets to areas where they have seen sustainable ROI in the past before relying on unproven ground.
Take away
Healthcare organizations have generally not been quick to buy into the hype around AI and have carefully weighed the financial benefits and impact of the technology on patient and clinician experiences. AI budgets will prioritize financial, patient engagement, and operational throughput value propositions that drive more tangible ROI. And while this is a good thing, similar constant focus and attention will need to be devoted to identifying and capitalizing on opportunities to improve performance through AI.
2 | Clinical decision support tools may take longer to mature.
About half of the health system’s AI funding has gone to clinical care startups that facilitate accurate clinical decision-making while alleviating persistent workforce challenges such as shortages of personnel and costs. “Despite investor interest, clinical decision support solutions recorded some of the lowest maturity rates among all healthcare AI startups (6.8%), while Imaging AI performed slightly better with a maturity rate of 9.9%. Additionally, startups addressing these functional areas have higher capital intensity rates relative to their average valuations and large-scale value creation,” the report said.
Take away
This likely speaks to the fact that clinical workflows that directly affect care decisions pose the highest risk and liability for care providers. Therefore, these solutions require a higher threshold of accuracy, and their reliability and auditability must continue to be closely scrutinized and heavily regulated, the authors believe. This can lead to longer sales and implementation cycles and make it more difficult for providers to analyze the value these solutions create from clinician decision-making. These dynamics can make it more difficult to assess the potential return on investment.
3 | Examine the potential of AI to anticipate clinical deterioration in patients.
This is where the most valuable and mature startups seem to be gaining ground. Insights from these uses of AI are used to develop advanced preventive care plans that are also more personalized, allowing care teams to coordinate care more effectively and avoid unnecessary use.
Take away
This area of AI application appears to be an ideal place to preserve the clinical autonomy that clinicians deeply value. Together, leading startups are showing that AI can drive valuable resource efficiencies and superior clinical outcomes when thoughtfully integrated into a new model of care, the authors say.
4 | Business and patient engagement operations with AI are attracting 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 support. Patient engagement and RCM tend to be more repetitive, manual, and less fraught with direct clinical risk.
Take away
AI applications in these areas are among the most mature in healthcare and can have two significant impacts on healthcare systems: optimizing payment capture and managing work efficiency .