Artificial intelligence models are improving faster than ever. Yet for many patients and clinicians, the experience of care looks much the same as it did before the latest wave of AI.
In a new whitepaper, Ambience Healthcare President and Co-Founder Mike Ng calls this “the million miles between better models and better care.”
Health systems are starting to demand more from AI than just smart models. They want tangible outcomes and ROI that span across settings and clinician workflows—and urgency for these tools to deliver is rising. Health system leaders are looking to AI to make it possible for clinicians to spend more time with patients and deliver more care, but they cannot risk bolting on another AI point solution that can’t keep up with evolving models and increased demand.
Because as expectations and AI capabilities grow, so does pressure on the systems beneath the models. According to the whitepaper, at one academic medical center, a single pre-charting tool used by roughly 10% of clinicians generated more EHR transaction volume than all other human and application activity combined. Ng warns that AI agents could soon generate 1,000 times the transaction volume of human users across EHRs and other systems of record, and that current systems of record lack the core capabilities needed to scale AI across care teams.
Adopting better models isn’t enough. To harness AI’s potential, health systems need a new class of shared infrastructure beneath their AI tools.
That’s what it will take to close the gap between better models and better care, Ng argues. And closing that gap will increasingly decide which organizations thrive.
The right foundation lets health systems choose the models and applications that serve them today without being locked into those choices tomorrow. Download the whitepaper to learn why a shared, open infrastructure for clinical AI is essential to your organization’s sustainability now and growth in the future. The whitepaper details:
- What a shared “system of context” is and how it can reconcile fragmented patient records
- How that foundation can capture clinician, patient and organizational knowledge—and help your organization achieve targeted outcomes
- How AI agents can move from closing out encounters to coordinating care beyond the clinic
- The five governance responsibilities for testing, monitoring and overseeing AI applications