Risk adjustment requires organizations to determine the true acuity of their patient population and prioritize accordingly. Any strategy for assessing that risk must start with a foundation of the most robust set of up-to-date clinical data possible.
Recapture is only a small part of this process and doesn't account for many common scenarios such as patients who haven't been seen or patients whose conditions haven’t been adequately documented. Successful risk-based arrangements incorporate the full picture of risk - including new and suspected diagnoses as well as previously documented conditions - into pre-encounter and point-of-care workflows.
Insight into both known and potential conditions is critical to managing many patients at once and prioritizing accordingly. Provider and payer organizations separate the patients they serve into distinct groups of similar complexity and care needs for better care coordination and population health initiatives. Precise and efficient risk adjustment targeting focuses attention on the right patients at the right time for the right reasons. The highest risk patients most in need of intervention take priority while interventions with low likelihood of succeeding are minimized as much as possible.
Organizations equipped with a more complete picture of risk upfront yield a much higher return on investment from these initiatives. Staffing and operations can be realigned according to the true risk of their population. Outreach plans and education programs can be designed around the most prevalent diseases among the patient population. Patients who have high-probability suspected conditions but haven't yet been seen by the provider can be scheduled proactively. And more patients can be enrolled in care management programs earlier.
The list of all relevant conditions for each patient can be used during pre-visit prep as an education opportunity to establish documentation and coding best practices. This more proactive workflow not only increases accuracy of reimbursement but also helps to shift the overall organizational focus from correcting to improving and reduces the need for future retrospective chart reviews.
At the point of care, the full scope of data is critical to targeted treatment plans. The list of relevant conditions for each patient, including suspect conditions ranked by probability, should be available to providers within their workflow to review and assess during the encounter. Providers can use this data as the starting point for developing a personalized care plan and to inform patient monitoring, diagnostics, and treatment.
When providers are armed with an understanding of the true complexity of each patient's health during the encounter, they can target interventions more precisely. Getting patients on a more targeted treatment plan sooner is especially crucial for vulnerable high-risk patients with multiple chronic conditions.
Including new and suspected conditions in providers’ workflow at the point-of-care also aides in early detection of disease. Early detection is key to successful intervention and mitigating disease progression, helping patients live longer, healthier lives overall while avoiding costly care.
With the full picture of patient health, providers can also ensure that all relevant diagnoses are captured to the highest level of specificity. Accurate documentation and coding is particularly important in HCC risk adjustment arrangements as it has a significant impact on appropriate reimbursement and care coordination.
Value-based care is a long-term game. Organizations that invest in a comprehensive understanding of the risks within the populations they serve upfront will be able to tailor their programs to optimize profitability and the care that their patients receive.
Payers in value-based contracts are expected to move beyond their traditional role as insurance purveyors and assume the broader responsibility of improving outcomes and care coordination. Claims data is not a sufficient foundation for successfully fulfilling this role. By leveraging the valuable insights and real-time availability that clinical data offers, plans can carry out the level of proactive outreach and crises prevention that value-based care management demands.
AI is no replacement for human judgement but it can go a long way towards simplifying and streamlining data management and analysis. AI-enabled chart review helps providers and plans that coordinate care for complex populations work smarter, not harder, by shifting the focus from volume of charts targeted to precision targeting of charts.
The Centers for Medicare & Medicaid Services (CMS) initiated the Hierarchical Condition Category (HCC) model in 2004 to adjust payments to Medicare Advantage Organizations (MAOs). The model has been more prevalent in recent years as HCCs are becoming more widely recognized as one of value-based programs' most important components. This heightened visibility is due in large part to the growth and success of Medicare Advantage.