As artificial intelligence (AI) becomes more embedded in risk adjustment and quality programs, clinical leaders are increasingly responsible for ensuring technologies are safe, effective, and trusted by care teams.
Trust is already fragile. Research shows clinicians override most clinical alerts, as high as 96 percent, because alerts are low value, poorly timed, or impossible to act on in real clinical contexts. When AI simply adds more noise, adoption stalls and confidence erodes. Then, AI loses the ability to deliver on value-based care goals.
The stakes are rising. Clinical leaders need reliable ways to meet documentation and quality requirements without accelerating burnout. As leadership evaluates how to use AI to further value-based care goals, these five principles can help ensure the technology is trustworthy and useful.
Principle 1: Start with workflow, not the model
Too often, new decision‑support tools look great in demos but quickly disappear into the background. The problem is a mismatch between the technology and how clinicians actually work.
Effective AI‑driven suspecting programs are built around the natural flow of a visit. When AI delivers insights into the EHR in real time, adoption moves from “optional extra clicks” to “part of how we practice.”
For senior clinical leaders, the question to ask vendors and internal teams is simple: “Show me, step by step, how this fits into a single patient visit from pre‑charting to after‑visit work.”
Principle 2: Control the noise up front
Hybrid AI, which combines generative models with deterministic clinical rules, gives leaders a real control knob: they can tune thresholds so the volume of “suspect” diagnoses matches their review capacity. It works best when “evidence extraction” is separated from “clinical decision logic,” with AI reading charts to build a reusable evidence layer and clinician‑authored rules deciding which suspects are worth showing. In practice, leaders adjust three levers—suspect volume per patient, confidence and impact thresholds, and specialty and site context—to cut irrelevant suspects by up to threefold compared with legacy approaches.
Principle 3: Separate recapture, suspects, and care gaps
Another way to build trust is to make it clear what type of work the AI is asking clinicians to do: recapturing existing diagnoses, evaluating possible diagnoses, or closing care gaps. When these categories are clearly labeled instead of lumped into one feed, physicians can quickly decide what to address in the visit and what to route to the team. This clarity also supports governance, giving risk leaders visibility into diagnosis gaps and quality leaders visibility into care gaps without pushing all that work onto already over-stretched physicians.
Principle 4: “Show your work” to earn trust
AI systems that cannot explain themselves are dead on arrival in clinical practice. Clinicians do not have time to reverse‑engineer why a suspect appeared, and auditors will not accept “the model said so” as justification.
A more trustworthy pattern is a “show your work” design. For every AI suggestion, the system should clearly display:
- The clinical evidence used, linked back to the source documents
- The reasoning pattern or rules, translated into plain language
- The confidence level and recommended action
Hybrid architectures that create an audit trail from the original record through the evidence to the final recommendation make this feasible at scale. For clinical leaders, this is critical to supporting peer review, responding to queries, and standing behind AI‑supported documentation when auditors or regulators ask hard questions.
Single-click, direct links to source evidence available directly within Reveleer's provider workflow.
Principle 5: Treat AI as a learning program, not a one‑time install
Finally, the most important shift is organizational. AI suspecting should be treated as a living program with feedback loops, not a static tool that goes live and stays frozen for three years.
Practical steps start with monitoring alerts and suspect performance over time. That means tracking positive predictive value, response rates, time to action, and downstream outcomes such as risk score accuracy and closure of key quality gaps; use clinician feedback to retrain models or adjust rules.
Finally, governance needs to sit with a multidisciplinary group to review the metrics and decide when to tighten or relax thresholds as the program matures. The goal is not to eliminate all noise. It is to dial the system toward a signal‑to‑noise ratio that clinicians recognize as helpful. When clinicians see that their feedback is implemented, trust grows organically. Clinical leaders today are being judged on both outcomes and experience. With these five key principles in mind, thoughtful AI design supports both and lightens the burden for clinicians. Ultimately, the organizations that will find the most success with value-based care technologies are the ones whose clinicians actually want the AI in the room with them.
For more about how to build AI workflows that clinicians actually trust, download The definitive guide to AI in value-based care.
About the author
As Product Marketing Manager at Reveleer, Marena Hildebrandt, DNP, RN, PHN, NEA-BC, draws on more than a decade of nursing leadership and product marketing experience across health care and technology settings. With a doctorate in Health Innovation and Leadership and board certification as a Nurse Executive-Advanced, Hildebrandt brings a clinician’s perspective to every project, translating complex clinical and regulatory requirements into clear, actionable solutions for providers and health organizations. Connect with Hildebrandt on LinkedIn.
