Most clinicians are not afraid to visit. They fear unfinished notes, after-hours diagrams, and coding queries that arrive days later. The Agency for Healthcare Research and Quality (AHRQ) found in a 2024 technical brief that documentation burden is often cited as a contributing factor to clinician burnout.
Prospective risk adjustment (PRA) offers a different rhythm. Instead of chasing documentation after the fact, it moves work to the meeting where the information is most up-to-date. This article explains the 2026 and 2027 US policy changes, why encounter documentation matters, and how small practices can use it without going out of business.
Why does documentation accuracy shape clinicians’ well-being?
When documenting mental health diagnoses such as depression, accuracy is important for both coding and quality of care. Understanding the diagnosis and treatment of depression helps clinicians recognize that appropriate assessment includes screening for bipolar disorder, substance abuse, trauma, and thyroid disease, all of which significantly affect diagnosis and prognosis. This thorough approach to diagnosis ensures that risk adjustment scores reflect the patient’s true clinical complexity.
In terms of behavioral health, nuance has clinical significance. A comment that simply says “depression” ignores severity, response to treatment, and current condition. If you accurately record this detail once during the visit, it will reduce the post-production work. The goal is accuracy, not volume.
Risk management in plain language
Based on the documented conditions, risk adjustment estimates how much care the patient population is likely to need. Diagnoses are mapped to Hierarchical Condition Categories (HCC), which are linked into a Risk Adjustment Factor (RAF) score. The American Academy of Family Physicians (AAFP) notes that scores are reset annually and diagnoses must be supported by MEAT: Monitored, Evaluated, Assessed, or Treated.
Timing is the practical difference. The National Coordinating Office (ONC) defines prospective risk adjustment as using only the characteristics of previous patients, so new diagnoses count later. The documentation completed this year will shape next year’s accuracy.
What is potential risk management and why it matters now
The regulatory background makes the documentation of supply points more important.
- For calendar year 2026, Medicare Advantage risk scores use 100% of the 2024 V28 CMS-HCC model.
- Beginning in 2027, CMS will exclude diagnoses from Medicare Advantage risk score calculations from non-linked chart review records unless beneficiaries are linked to another parent organization.
- CMS describes Risk Adjustment Data Validation (RADV) as the primary way to address Medicare Advantage overpayments by verifying that payment diagnoses are supported by medical records. On May 29, 2026, CMS announced that selected plans had been notified for 2021 RADV audits.
- Under the ACO REACH model, standard and new entry ACOs will transition to 100% weighting for V28’s future HCC model in the 2026 performance year.
The common thread is clear: diagnoses must be linked to a real encounter and supported by records. This discussion pertains to Medicare Advantage under CMS-HCC V28, which differs from the ACA marketplace, HHS-HCC. This shift is the reason future risk adjustment changed from optional to structural. When the CY2027 rule goes into effect, encounter-recorded documentation is the only type that reliably counts.
How PRA reduces administrative friction at source
AHIMA notes that follow-up reviews can add fatigue to provider inquiries and disrupt workflow by imposing documentation requirements after the encounter. Future workflows detect problems earlier.
Preliminary visit. Before the patient arrives, a brief summary of suspected conditions and open care gaps provides context. This is preparation, not direction.
While visiting. During the encounter, the clinician confirms or rules out the conditions and captures evidence of the meat in real time.
Pre-order. Before submission, a validation step verifies that all coded conditions are supported by the annotation. Unsupported “history” codes get stuck here.
The payback is interruption after fewer visits. If the record is accurate the first time, the volume of queries may drop, along with after-hours charts.
Behavioral Health Spotlight
The CMS 2024 report to Congress notes that V28 reconfigured mental health HCCs to include separate categories of bipolar disorder without psychosis and moderate-to-severe major depression. It rewards specificity.
A note can satisfy the flesh without bloat. Major depressive disorder, recurrent, moderate; Review of the PHQ-9, continuation of medication, and follow-up within six weeks document assessment, evaluation, and treatment. In the case of bipolar disorder, recording the current phase, adherence and plan captures the clinical picture. The goal is the correct words, once recorded.
Tools that help without adding burden
Technology can support this work by removing steps rather than adding them.
- Templates and checklists that the request for HÚS evidence keeps the documentation consistent without exaggeration.
- Ambient AI writers you can take notes from the conversation. A multicenter quality improvement study found that after 30 days, clinician burnout decreased from 51.9% to 38.8% and after-hours documentation was reduced by 0.90 hours. The results are encouraging, not definitive, and review by the clinician is still essential.
- Dashboards it may also surface conditions that need to be reassessed this year as RAF scores are reset annually.
- Copy-paste protection limits helps prevent note bloat that undermines accuracy.
Measuring impact and avoiding pitfalls
Track EHR after-hours, query rates, query acceptance rates, and pre-claim discrepancy rates. A decrease in query volume with stable or improving accuracy is the pattern to watch.
Two safeguards matter: follow AHIMA-compliant query practices and align workflows with CMS 2027 policy for unattached chart review records. Resist any process that only provides diagnoses. The goal is to accurately reflect the patient, not inflate the score. CMS verifies diagnoses based on the medical record through RADV.
A complex example
Imagine a small primary care clinic with a list of suspects for depression, chronic kidney disease, and heart failure. On-visit prompts record MEAT evidence and a pre-claim check flags an unsupported ‘history’ prior to submission.
Frequently asked questions
Is expected risk adjustment a substitute for improving clinical documentation?
Not. This moves most of the work earlier in the encounter, which can reduce the number of back-end queries. CDI expertise continues to matter in education, auditing and complex matters.
How does this affect mental health diagnoses?
V28 created more specific mental health categories, including bipolar disorder without psychosis and moderate to severe major depression. This makes the severity and condition documentation supported by MEAT all the more important.
How can a small practice start?
Start with a set of conditions and a simple pre-visit summary. Add an on-visit MEAT message and then a pre-claim check. Measure the volume of queries and expand only when it seems easier.
