Care gaps are often framed as a checklist: mammograms, colon cancer screening, eye exams, medication adherence. But in episode five of Care Decoded, Michael Lesnick and Dr. Sunil Nihalani make a sharper point: gap closure is a clinical moment, not a metric. The best opportunity to act is when the patient is in front of you. After that, everything depends on fragile handoffs, approvals, scheduling, follow-through, and trust.
Dr. Nihalani traces care gaps back to “preventive care” long before EHRs—paper charts with a front-sheet grid providers updated visit by visit. The method changed. The challenge stayed the same: surfacing the right gaps at the right time, with accurate data, in a workflow that already runs hot.
And accuracy is the hinge. A “gap” that’s already closed wastes time, erodes trust, and trains clinicians to ignore everything that follows. The conversation also highlights where payer signals and clinical reality can misalign—especially medication adherence, where pharmacy claims activity doesn’t always reflect what the patient is actually taking.
The takeaway is practical: closing gaps requires a multi-pronged playbook, dedicated support, and technology that cleans up noise before it reaches the provider. Done right, the result isn’t just better Stars performance. It’s better prevention, earlier detection, and a patient experience that builds trust.
Care gaps persist for three consistent reasons:
1) The data presented to providers isn’t always accurate.
Health plans may flag a mammogram or eye exam as overdue, but the patient may have completed it recently in a different system. The provider then has to “dig” in the EHR to verify what’s true.
2) Patients need context, not reminders.
A call that says, “You need a mammogram,” often triggers resistance: “Why? I feel fine.” Screening measures aren’t experienced as urgent, so the clinical team has to explain why it matters and why now is the right time.
3) Closing a gap is a workflow, not a click.
Even when a patient agrees, the sequence can break at multiple points: referral submission, prior approval, network restrictions, appointment scheduling, transportation, follow-through, and attendance.
In other words: the gap isn’t closed when a box is checked. It’s closed when the care actually happens.
One of the most useful concepts from episode five is the idea of “meeting the moment.” The visit itself is the highest-leverage window. It’s when patients are present, engaged, and most likely to accept preventive action—if the provider can surface the need, explain it clearly, and connect it to the patient’s goals.
But that moment is under pressure. Patients arrive with immediate complaints. Providers have limited time. And if gap data is noisy, the provider’s attention gets spent on verification instead of care.
Dr. Nihalani calls out a frequent point of friction: medication adherence signals.
Many plans infer adherence based on pharmacy claims. If a claim doesn’t arrive, the patient may be labeled non-adherent. But in practice, patients may have surplus medication at home, receive early refills, or get mailed fills that change refill timing. The result is a false signal that triggers unnecessary outreach and frustrates both patients and practices.
The larger theme is important: when the data source doesn’t match lived clinical reality, “gap closure” becomes wheel-spinning.
Episode five lays out a realistic playbook: there is no silver bullet. Strong gap closure requires multiple layers working together.
1) Get the patient in the office.
The best chance of action is during a visit.
2) Give the provider clean, prioritized, accurate gaps at that moment.
Providers need confidence that what they’re seeing is real.
3) Build follow-through capacity.
Many practices use dedicated HEDIS/quality staff to work lists, contact patients, schedule appointments, and track completion. This support can exist even in small practices—Dr. Nihalani shares that his two-provider practice had a dedicated staff member focused on this work, and they consistently achieved 4.5+ Stars performance by “constantly working it.”
4) Track closure actively.
Gaps don’t close themselves. They require consistent monitoring until completion.
The strongest AI use case in episode five is straightforward: scrub and validate payer gap lists against EHR data before those gaps reach the provider. If the AI can confirm there’s no evidence the gap was closed (or that the patient is ineligible), the provider sees fewer false alerts and trusts what remains.
That’s a key distinction: AI isn’t replacing the HEDIS team’s outreach and scheduling work. It’s reducing the time wasted verifying whether a flagged gap is even real.
And the rule is simple: accuracy determines adoption. Clinicians will tolerate helpful friction, but they will not tolerate inaccurate noise.
Toward the end of episode five, Dr. Nihalani connects gap closure to broader outcomes: early detection, prevention, and patient trust. When screening measures catch disease early—or prevent it entirely—patients experience the system as reliable rather than reactive. That builds confidence in future recommendations and strengthens the patient-provider relationship.
The message is balanced: gap closure can drive Stars and financial incentives, but the deeper benefit is clinical. Done well, it’s one of the most direct ways to convert data into better outcomes.
Care gaps persist because data is noisy, workflows are multi-step, and patients need context to act.
The point of care is the highest-leverage moment to close a gap—but only if the information is accurate and usable.
Dedicated support staff and tracking discipline are often what separate “good intentions” from 4+ Star results.
AI is most valuable when it reduces noise by validating payer gap lists against the EHR before clinicians see them.
Better gap closure improves outcomes and strengthens patient trust, not just quality scores.
