Apixio’s Prospective Risk Adjustment Suite integrates with all the leading EHRs (Epic, Cerner, athenahealth, NextGen, and more), leveraging EHR and FHIR® APIs for read-and-write capabilities.

Concurrent risk adjustment happens after the patient encounter but before claims processing. During this period, provider staff can review clinical documentation to ensure it meets HCC coding requirements and satisfies MEAT criteria:

• Monitor for signs, symptoms, and disease progression and regression
• Evaluate test results, medication effectiveness, and response to treatment
• Assess and address test results and medical records; discuss with patients
• Treat with medications, therapies, and other modalities

Concurrent risk adjustment also allows clinical staff to search for under-documented risk opportunities and ensure all codes and conditions are complete and accurate.

The difference between prospective and retrospective risk adjustment is that prospective looks to prevent coding issues, while retrospective catches issues that were made.

Prospective risk adjustment involves a proactive strategy to manage chronic diagnoses (recapture) and suspected conditions identified through labs, medications, reports, documentation, and other data sources. These are assessed, prioritized, and resolved during the patient encounter at the point of care.

Apixio provides initial and follow-up coding and auditing services for retrospective risk adjustment, utilizing our AI-powered platform to ensure fast, accurate, and reliable results.

The V28 model is a revised 2024 CMS-HCC risk adjustment model with several changes from the V24 model, driven by the need to reflect better specificity and clinical relevance of diagnoses under the ICD-10 system. There was an expansion in the HCCs from 86 in V24 to 115 in V28, but 2,236 ICD-10 codes were no longer mapped to HCCs.

Artificial intelligence (AI) enables coders to analyze structured and unstructured data and quickly identify evidence in clinical documentation of missing codes previously unknown. In addition, AI can also identify codes without clinical evidence in the documentation that needs to be audited. To enhance this process, AI utilizes Natural Language Processing (NLP) and Machine Learning (ML). These techniques aid in grasping the clinical context and refining models for improved accuracy.

Retrospective HCC coding is the process of uncovering any HCC codes missed during the submission based on evidence in the documentation and codes not substantiated in the documentation.

Retrospective risk adjustment enables health plans and risk-bearing providers to retrospectively analyze previous claims. This process helps detect unreported or inaccurately submitted HCC codes that are substantiated by medical records.

Many companies offer risk adjustment solutions for healthcare, including actuarial and consulting firms, and healthcare technology companies, including Apixio, for retrospective and prospective risk adjustment.