Prospective Risk Adjustment: Point-of-Care Decision Support for Defensible Coding

Key Takeaways

• Prospective risk adjustment captures and confirms conditions for patients during the visit, so every diagnosis is encounter-linked and backed by clinical evidence.
• CMS finalized the exclusion of unlinked chart review codes for CY2027, making encounter-driven documentation the compliance standard, not an option [1].
• OIG’s February 2026 MA compliance guidance flags add-only chart reviews and in-home assessments as risky coding practices, raising the value of getting the record right during the encounter [2].
• A provider-friendly workflow uses decision support, not pressure: it surfaces suspected conditions and care gaps to the clinician, who keeps final authority over what is documented.
• These programs ease query fatigue, cut manual chart reviews, and give providers and health plans a more defensible view of member risk than retrospective risk adjustment alone.

What Is Prospective Risk Adjustment?

Prospective risk adjustment is the process of identifying, confirming, and recording a patient’s health conditions at or before the point of care, so diagnoses are captured with clinical evidence during the encounter rather than recovered afterward. It pairs pre-visit planning with in-visit decision support, then validates the record before the claim goes out.

Two terms anchor the work. An HCC (Hierarchical Condition Category) is a group of related diagnoses that the Centers for Medicare & Medicaid Services uses to calculate a member’s risk score. The RAF (Risk Adjustment Factor) is that score, which sets how much CMS pays a health plan to care for the member. Prospective coding aims to make both reflect the patient’s health accurately, supported by evidence in the note.

Why it matters: a diagnosis is only as good as the proof behind it. When a clinician confirms a chronic condition face to face and records it with MEAT-level detail (Monitor, Evaluate, Assess, or Treat, the industry convention for showing a condition was actively managed), the code is defensible. When the same code is added later from a chart with no supporting encounter, it is exposed. Detailed clinical documentation produces the first kind of record, not the second.

For a foundation on coding fundamentals and MEAT, see our guide to risk adjustment coding.

Prospective vs Retrospective vs Concurrent: The Key Differences

Risk adjustment runs on three timing models, and they are not interchangeable. The clearest way to see the key differences is to line them up against when each one acts on the patient record.

Retrospective risk adjustment still has a role. The compliance-first version is two-way: it adds supported codes and removes unsupported ones, which is the practice CMS and OIG now expect. For a full treatment of that model, see our guide to retrospective risk adjustment. The shift across the healthcare industry is clear: retrospective work is moving from an offensive growth engine to a defensive safety layer, while prospective risk adjustment becomes the path to growing the right way.

Concurrent risk adjustment sits between the two, capturing codes as the encounter is documented. Some healthcare organizations pair prospective and concurrent risk adjustment so concurrent coding keeps pace with care.

How Point-of-Care Decision Support Works

Point-of-care decision support is the engine of this model. It runs across three risk adjustment workflows: pre-visit, in-visit (concurrent), and post-visit. Here is the process step by step.

• Pre-visit planning. Before the appointment, the system analyzes longitudinal patient data, current records plus the prior two years, and produces a summary for the care team on the patients scheduled that day. It surfaces suspected diagnoses, open care gaps, and recapture opportunities for chronic conditions due to be reassessed this year.
• In-visit decision support. During the encounter, the tool works inside the EHR (electronic health record) and shows the clinician evidence-backed prompts: this patient has lab results and a prior note consistent with stage 3 CKD, for example. The clinician confirms, edits, or rejects each suggestion. Final authority stays with the physician.
• Recording during the visit. The clinician documents the confirmed condition with supporting detail at the point of care, so the diagnosis and its clinical evidence live in one encounter note.
• Post-visit pre-claim audit. Before the claim goes out, the system validates diagnosis and procedure codes against the chart, catching gaps or unsupported entries while there is still time to fix them.

This sequence cuts manual chart reviews, lowers the chance of coding issues reaching the claim, and gives coders a cleaner record to work from. AI-assisted chart review with RAAPID runs 8 to 12 minutes per chart, compared with the longer manual passes coders face without it.*

Why Encounter-Driven Documentation Is the New Compliance Standard

Encounter-driven documentation is no longer best practice. It is the rule. CMS finalized the exclusion of unlinked chart review codes in its CY2027 Rate Announcement, alongside a 2.48% effective growth rate for the year [1]. A diagnosis that cannot be tied to a documented encounter does not count, full stop.

OIG reinforced the point. Its February 2026 Medicare Advantage compliance program guidance, the first major update in decades, flags add-only chart reviews and in-home health risk assessments as practices that invite scrutiny [2]. The throughline is intent: programs that only add codes, and never remove unsupported ones, read to regulators as engines built to inflate payment.

The enforcement record backs this up. In March 2026, a DOJ settlement resolved allegations that a large insurer ran an add-only chart review program that submitted diagnosis codes but failed to delete unsupported ones its own reviews had identified [4]. Separately, an OIG audit of an MA organization found unsupported high-risk codes in 247 of 271 sampled enrollee-years, a 91% error rate, with conditions such as acute stroke coded long after they had resolved into history-of status [3]. This upfront approach is the direct counter to that failure pattern. Clinicians confirming a condition in person will record acute as acute and history-of as history-of, because the patients are right there.

A Provider-Friendly Approach to In-Visit Coding

The fastest way to break this kind of program is to turn it into pressure on physicians. Provider-friendly design does the opposite. It treats the clinician as the decision-maker and the AI as support, which is what keeps providers engaged instead of fighting the tool.

Decision support means the system shows its work. Every suspected diagnosis arrives with the evidence behind it: the lab value, the prior note, the medication. The clinician sees why a condition is flagged and acts on clinical judgment, not a quota. This keeps coding accurate and keeps the physician in control, which separates real in-visit coding from a checkbox exercise.

It also reduces the administrative burden that drives query fatigue. When suspected conditions and care gaps surface before the visit, the clinician is not chasing missing records after the fact, and coders are not sending a stack of queries back. Care teams spend less time reconciling charts and more time with patients. That is how a program earns physician buy-in instead of provider abrasion, and it helps reduce costs tied to rework.

Where Prospective Coding Fits in Medicare Advantage and Value-Based Care

In value-based care, payment follows documented patient complexity, so the accuracy of the record drives both clinical and financial outcomes. Prospective coding fits Medicare Advantage and ACA programs because it produces accurate documentation at the moment of care, which supports compliant risk scores and cleaner claims for the plan.

The stakes are large. MedPAC reported in March 2026 that MA spending runs about 14% higher than equivalent fee-for-service care, part of roughly $615 billion in total MA payments for the year [5]. With dollars that size under review, plans are judged on whether their risk scores reflect real conditions. A strong risk adjustment program helps providers and health plans meet that bar by grounding every diagnosis in an encounter.

There is a care dimension too. Identifying chronic conditions early, during a planned visit, means patients get attention to problems that might otherwise go unmanaged for another year. Proactive documentation during the visit turns earlier identification into earlier intervention, which improves patient outcomes and is the point of value-based care in the first place.

What Strong Programs Deliver

Healthcare organizations that run a strong risk adjustment program see gains across three groups: patients, clinicians, and plans. The common thread is that accurate, timely records improve care and compliance at once.

For patients:

• Earlier identification of unmanaged or undiagnosed chronic conditions
• More personalized care built on a complete view of the patient’s health
• Better coordination across care settings along the healthcare continuum

For clinicians and care teams:

• Less query fatigue and a lighter administrative burden
• Decision support that respects clinical judgment instead of overriding it
• Cleaner handoffs between coding, quality, and clinical teams

For health plans:

• Defensible, encounter-linked records that hold up in review, including RADV audits.
• More accurate risk scores and steadier financial benefits
• A single, trustworthy view of member risk across the patient population

RAAPID coding teams report a 60 to 80% productivity improvement with AI-powered support, and accuracy reaches 92% out of the box, rising to 98% or higher after human-in-the-loop quality review.*

How RAAPID Supports Prospective Risk Adjustment

RAAPID’s Prospective Risk Adjustment Solution is an end-to-end, EHR-agnostic platform that supports clinicians and the patients they treat where care happens. It uses Neuro-Symbolic AI, RAAPID’s core technology, which combines large language models with a clinical knowledge graph to deliver suggestions that are explainable and tied to evidence in the note. Every flagged condition carries an audit-ready trail, so coders or auditors can see exactly why a diagnosis was suggested.

The platform runs the three risk adjustment workflows described above: pre-visit summaries that arm care teams with suspected conditions and care gaps, an in-visit module inside the EHR that delivers actionable insights during the encounter, and a post-visit audit that validates codes before claim submission. The design principle is decision support, not automation. The AI recommends; the clinician decides. To go deeper on the technology behind it, see our overview of Neuro-Symbolic AI for risk adjustment.

The result is the RAAPID standard for defensible accuracy: every diagnosis is encounter-linked, clinically evidenced, explainable, and auditable.

**See prospective risk adjustment in action. Book a demo.** 

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