Payers stratify premiums for a given benefits package by the member's relationship to the policy holder, and if permitted, by age or gender. This stratification discourages enrolling higher-risk, hence unprofitable, members. Risk-adjustment (RA) aligns premiums with the cost of caring for members with documented conditions, removing that incentive and encouraging the enrollment of higher risk members.¹ The magnitude of these adjustments can be thousands of dollars per member each year and can easily exceed the typical profit margin of plans.¹ Therefore, complete, accurate and ongoing sizing of these opportunities drive the payer's strategy.² ³
Gaps are closed by confirming each member's "open" conditions from claims or reviews of medical records, which can be expensive. However, confirmations supply needed revenues for their care, and sophisticated risk-adjustment analytics can identify undiagnosed conditions for earlier, more effective care.² ³ Let's examine how this process should work.⁴
Finding diagnosis gaps can save money, increase revenue, and improve care
RA (Risk Adjustment) encourages providers to document chronic conditions annually, find potential conditions not yet diagnosed, prioritize interventions for the right patient at the right time using the right technique(s), or not to intervene if the gaps are likely to close on their own.
Complete and precise coding of diagnoses requires proper documentation and certified training that most billers, providers and many coders lack.⁶ Diagnosis issues are widespread, yielding inaccurate risk scores, hence revenues;⁵ 40% or more of chronic conditions are not reported.⁷ Awareness of gaps at the point of care can support they are coded in the patient chart, thus avoiding costly rework, and provide important clinical context to inform the patient's care plan. This effort can reduce waste in care, slow disease progression and reduce complications.³
Precision targeting of interventions
Many gaps close themselves, many will seldom close and many are pursued with needlessly expensive interventions. Selecting the right (or perhaps no) intervention at the right time is "precision targeting."
Pulse8's patent-pending method is Dynamic Intervention Planning. Here's how it works.
We "confirm" conditions as risk-adjustable using software certified by the regulating authority and remove them from the patient's list of gaps. We seek chronic conditions that "persist," hence need reverification. We mine the breadth and depth of the patient's data using proprietary algorithms that "suspect" potentially risk-adjustable conditions.³
Pulse8's Dynamic Intervention Planning evaluates the evidence and determines if there is enough to warrant a suspected diagnosis and estimates the odds, from a clinical perspective, that the condition is real. This "clinical confidence," along with other factors, creates an "expected value" for each gap. We monetize each gap according to the regulatory authority, so Finance and Actuarial have a reliable estimate of impact on revenue.
What intervention, if any, should be taken to close a gap? Here we use predictive models that look at the patient's and provider's past behavior, socio-demographics, along with other factors, to assess the odds of the gap closing on its own. Then, given the type of intervention considered, stack-ranked so the most valuable/likely gaps are addressed first, omitting money-losing interventions.⁴ Clients can fine-tune the lists by budget, value optimization and dozens of other factors.
Impact of Dynamic Intervention Planning
Health plans must make every intervention count and not spend on gaps that will close on their own. Those following this methodology consistently find more conditions per member, with more revenue, while reducing 20-30% of interventions versus prior vendors.
Pulse8 specializes in developing systems to save payers time and money, including precision targeting of interventions and Dynamic Intervention Planning. If you would like to learn more, please visit our website.
¹ National Health Council. Risk Adjustment. 2021. https://nationalhealthcouncil.org/wp-content/uploads/2019/12/NHC_%20Risk_Adjustment_Brief.pdf
² Mitigating waste in healthcare with analytics and interventions. Veradigm. Updated January 4, 2019. Accessed March 18, 2021, https://veradigm.com/veradigm-news/mitigating-waste-in-healthcare-with-analytics-and-interventions/.
³ Wrathall J, Belnap T. Reducing Health Care Costs Through Patient Targeting: Risk Adjustment Modeling to Predict Patients Remaining High Cost. EGEMS (Washington, DC). April 20, 2017. 5(2):4. doi:10.13063/2327-9214.1279
⁴ Pulse8. Analytic Logic: Risk Adjustment Analytics, Financial Reporting, and Risk Mitigation V18.11.0. Accessed March 25, 2021.
⁵ Medicare risk adjustment. foresee Medical. Accessed March 18, 2021, https://www.foreseemed.com/medicare-risk-adjustment.
⁶ Weed K. What is a RAF Score? RCxRules. Updated 2021. Accessed March 18, 2021, https://www.rcxrules.com/blog/why-are-my-raf-scores-low#:~:text=A%20RAF%20score%2C%20or%20risk,to%20care%20for%20a%20patient.
⁷ Cassano HJC. Factor HCC with a Two-pronged Approach to Risk Adjustment. AAPC Updated August 1, 2012. Accessed March 20, 2021, https://www.aapc.com/blog/24215-factor-hcc-with-a-two-pronged-approach-to-risk-adjustment/.