By Jay Ackerman

The healthcare industry is at a critical inflection point and the promise of value-based care is at risk due to operational complexity and clinician burnout. AI offers a potential lifeline to streamline processes and restore focus on patient care. However, widespread adoption is stalling. Concerns around trust, transparency, and usability are slowing progress. This isn't just a technology gap, but a leadership and trust gap.

AI has become one of the healthcare industry’s defining disruptors transforming diagnostics, documentation, and decision-making at every level of care. Yet, despite near-universal recognition of its promise, implementation gaps threaten VBC’s future. In fact, only 40% of payers and 38% of providers are fully committed to adoption.
Why? Trust hasn’t caught up with technology.

In a highly regulated industry built on sensitive patient data, skepticism about AI’s reliability, bias, and transparency runs deep. Without clear leadership to prioritize explainability, accountability, and hands-on training to build confidence among clinicians, adoption stalls. This shows us that progress in adopting AI in healthcare depends less on algorithms and more on human-centered trust and alignment.

The crisis of confidence
When patient care is on the line, blind trust in AI isn’t an option, and it shouldn’t be. As a result, the industry holds valid concerns around reliability, bias, and ethical use.

Specifically, nearly three quarters of payers and providers worry about hallucinations that AI may deliver, such as a system generating a diagnosis that is not supported by patient data or coming up with false reasoning for denying a claim. Additionally, ethical concerns are cited as a major barrier by nearly all leaders according to the same study, and this hesitation is heightened by a lack of visibility. Without explainable, transparent systems, organizations hesitate to rely on AI in care making decisions.

To overcome this roadblock, the focus needs to shift to governance, making transparency and accountability main pillars. Currently, AI too often operates in a black box system, pulling data and blindly giving it to clinicians and healthcare leaders. In order to build trust, there needs to be a switch to a glass box system, one that shows clinicians and healthcare leaders exactly where the data is coming from and why the technology is delivering certain results. This combination of trust-centered leadership and operational discipline is non-negotiable on the path forward, addressing reliability fears and ethical concerns.

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