Risk adjustment models are no longer confined to predicting costs for Medicare Advantage beneficiaries. Now they are more prevalent and widely adopted in various alternative payment models for value-based outcomes.
To calculate a per member per month (PMPM) capitated payment in managed Medicare plans, CMS uses a model that captures Hierarchical Condition Codes (HCCs) and assigns a Risk Adjustment Factor (RAF) to each patient. Different payment methodologies also use that model, for instance:
- Fee-for-service reimbursement (FFS) to compensate Accountable Care Organizations (ACOs) for sicker patients
- Individual and small group markets on health insurance exchanges
- Cost and quality measures for value-based purchasing for hospitals and providers
As you see, the implications of risk adjustment are far reaching. Timely assessment and accurate coding of patient problem lists can make huge differences in revenue for payers and providers alike.
Here are 5 steps that can improve clinical documentation at point of care leading to more accurate risk adjustment coding and more appropriate compensation for quality care.
1. Integrate clinical data
Access to clinical data in patient records is valuable and critical for risk adjustment. Integration of clinical charts from EMRs is the only way to demonstrate the complexity or severity of patient’s illnesses. Accurate risk adjustment depends on complete and correct data.
Call to action - Design an automated process that opens the data pipeline by connecting health plans to physician practices and hospital EMRs. Create an accessible health data platform that aggregates the different sources of patient health data, applies uniform structure, and provides clinical context for that data. Such a platform will allow health plans to maximize the use of information among various internal departments as well as serve to minimize physician abrasion - by reducing (and in some cases eliminating) repeated and random requests from payer for patient charts and data.
2. Group patients into risk cohorts
Traditional ways of risk management are time consuming, inefficient, and prone to errors. The inability to prioritize and categorize patients by their care needs costs providers valuable time and resources.
Call to action - Leverage a data and analytics platform that evaluates patient data (both claims and encounter) and assigns them to vulnerable subgroups that have greater care needs or, where costs incurred to deliver care are significantly higher than actual payments. Use a cohort builder to define patient groups based on their age, enrollment type, geography, access to care givers and care facilities, costs and outcomes, family history, tobacco use, or other socioeconomic factors. A cohort based analysis will identify and substantiate members with the highest-value undocumented medical conditions leading to an effective risk management process.
3. Create algorithms to improve clinical documentation
Professional coders speak their own language -- which often doesn’t translate into the clinical language that most physicians use. In 2018, the Office of the Inspector General reported 55 percent of regular medical services are improperly coded. This error rate results in massive under and over reporting. Up coding will result in over-payments and potential DOJ lawsuits while under coding will leave money on the table and reduce reimbursements.
Call to action - Use clinical terminology services and reference data management to crosswalk diagnosis codes (documented in problem lists) to their correct mapping of risk categories and risk factors. Implement natural language processing (NLP) rules that evaluate clinical notes (unstructured narratives, patient histories) and extract overlooked clinical factors, problems or comorbid conditions. Leverage technology to detect missing problems, flag incorrect risk classifications, and highlight areas of coding improvement that can be queued up for clinicians and their staff to review and act.
4. Provide actionable feedback to clinicians
In today’s age of digital healthcare, clinicians are overburdened with maintaining accurate documentation of patient encounters. Often times they are unaware of the economic impact of poor or inefficient documentation practices. If a patient is diabetic, simply documenting diabetes in the medical record is inadequate to demonstrate the complexity or severity of the condition. The documentation must indicate whether the patient is a type I or type II diabetic, whether it is controlled or uncontrolled, whether it is progressing and how it is being treated. Technology can play a vital role in closing these information gaps.
Call to action – Compare historical claims data with encounter data to monitor and reconcile encounter submission gaps. Review results of previous audits to reveal poor coding practices, missing documentation and other common pitfalls in clinical assessments. Use patient engagement strategies to proactively schedule high-risk patients for their annual wellness visit. Collaborate with provider practice advisors and share a list of all possible conditions that need to be evaluated, addressed and documented at the time of a patient’s annual wellness visit. Create educational material and checklists like coding best practices and patient assessment forms that prep the practice staff prior to a patient’s visit.
5. Define key metrics (KPIs) and continuously monitor them
Effective risk adjustment process needs real-time visibility to monitor and manage risk adjustment revenue against budgeted gap closures and key performance indicators (KPIs). Without defining performance measures and continuously evaluating them, health plans cannot determine the success of a performance improvement program or quantify the ROI.
Call to action – Define key performance metrics applicable to a beneficiary population - HCC Recapture Rate, Mean Risk Score Difference, Top Payment HCCs, Dollar-Gaps Ratio, etc. Create processes and analytic capabilities to measure and track progress on these key metrics and provide feedback to providers and operational leaders. Leverage predictive analytics to forecast risk and outcomes. The likelihood of achieving a performance target and the overall dollar impact of harnessing those opportunities often serves as a key motivator for providers treating high volume of patients with complex care needs.
By taking this 5-step approach to risk adjustment, health plans can incrementally improve performance and maximize revenue. Omni-HealthData, the most complete and unified healthcare data and analytics platform, can implement these 5 steps and help payers gain strategic advantage over their competitors in managing risk while improving reimbursements.
Learn more about Omni-HealthData at omnihealthdata.com