Table of Content
The first model is called CDPS and is used by state Medicaid programs for making capitated payments to Medicaid HMOs for disabled enrollees. Before sharing sensitive information, make sure you’re on a federal government site. Technical documentation for calculating potentially avoidable event measures can be accessed via the link to Technical Documentation of OASIS-Based Measures in theDownloadssection below.
As defined by the Centers for Medicare and Medicaid Services , risk adjustment predicts the future health care expenditures of individuals based on diagnoses and demographics. Risk adjustment modifies payments to all insurers based on an expectation of what the patient's care will cost. For example, a patient with type 2 diabetes and high blood pressure merits a higher set payment than a healthy patient, for example. Risk-adjusted outcome measures are identified in theHome Health Outcome Measures Table that is available in theDownloadssection below. The risk adjustment methodology, using a predictive model developed specifically for each measure, compensates for differences in the patient population served by different home health agencies. In addition to the issues identified above, an agency-level HCC measure is impractical for use in national implementation, given that the CAHPS surveys collect deidentified data.
Demystifying Home Health Risk Adjustments - hhvna.com
Measures based on OASIS data are calculated using a completed episode of care that begins with admission to a home health agency and ends with discharge, transfer to inpatient facility or, in some cases, death. Risk adjustment allows for proper cost adjustments as well as setting a standard of premiums for high-risk enrollees. This is because people who are sick or have chronic conditions will be more expensive to treat than someone with few or no health issues. Overall this risk adjustment aims to provide the appropriate funding based on the severity of an enrollee’s health condition. Lastly, the ACG model was developed with a completely different approach than the other two. This model assigns diagnosis codes using 32 ambulatory diagnostics groups based on how the condition might affect an enrollee’s health and resource needs.
The continued health and safety of hotel guests is of the highest priority for hotels. Hotels with an Clean & Safe label have demonstrated enhanced protective measures to help mitigate the spread of COVID-19 within their properties. Depending on the plan level , there may be separate calculations due to plans’ varying actuarial value. Actuarial value is the total average of costs for covered benefits. For example, if a plan has an actuarial value of 80%, it means the enrollee is responsible for the remaining 20% of costs on all covered benefits. This model uses a concurrent model, using current year data to predict this year’s costs, unlike the CMS-HCC that uses prospective modeling.
Risk adjustment resources
Risk adjustment is a critical tool in public reporting of quality measures. Its aim is to level the playing field so that providers serving different patients can be meaningfully compared. We used a theory and evidence-based approach to develop risk-adjustment models for the 10 publicly reported home health quality measures and compared their performance with current models developed using a data-driven stepwise approach. Overall, the quality ratings for most agencies were similar regardless of approach. Theory and evidence-based models have the potential to simplify risk adjustment, and thereby improve provider and consumer understanding and confidence in public reporting. Most HH QRP measures are assessment-based measures created using the OASIS assessment tool data.
Guidance for the statistical risk model, variable specifications, the variable selection process, and the performance of the risk adjustment model for the claims-based Potentially Preventable Readmissions measure calculated for the home health Medicare FFS population. First, the authors did not report that they tested for excessive correlation. Because Chen et al used state fixed effects and race variables together, there may have been excessive correlation because racial composition varies by state. Their inclusion of additional variables beyond those currently used in national implementation of HHCAHPS may also result in some excessive correlation for which testing results should be reported. Second, the authors controlled for agency profit/nonprofit status and the number of years that an agency had been certified by Medicare, both of which may reflect aspects of an agency’s care orientation that are rightfully reflected in the agency’s publicly reported scores.
Risk Adjustment: What Is It & How Does It Impact …
You can find detailed specifications for the claims-based measures in theDownloadssection below. CMS utilizes a range of data sources to calculate quality measures. HH QRP measures derive from three data sources, Outcome and Assessment Information Set assessment, Medicare fee-for-service claims, and the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Home Health Care Survey. OASIS and HH CAHPS data collection and reporting are requirements for providers participating in the HH QRP. Medicare FFS claims data are submitted by HHAs to receive payment for services provided for Medicare FFS patients. Risk adjustment is a a modern technology that accounts for known and/or discovered health data elements to level-set comparisons of wellness among members.
All diagnosis codes are organized into 805 diagnostics groups, which are then organized into 189 conditional categories for a broad picture view. Hierarchies are applied to every major category and count only the highest cost diagnosis. For example, if someone had two conditions under the cardiovascular category, the higher cost condition would be counted. HHS is committed to making its websites and documents accessible to the widest possible audience, including individuals with disabilities.
Manager, Risk Adjustment Solutions
Even if we did have patient identifiers, individual-level HCC scores would not be available on a timely basis for use in regular public reporting. Process measures evaluate the rate of home health agency use of specific evidence-based processes of care. The HH process measures focus on high-risk, high-volume, problem-prone areas for home health care.
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The calculation of an enrollee’s risk score begins with their demographics and HCCs; a.k.a. the medical codes for their conditions. The authors disagree with previous research concluding that the Home Health Care Consumer Assessment of Healthcare Providers and Services publicly reported data are insufficiently adjusted for patient comorbidities. In addition to helping Priority Health receive proper reimbursement and lower the cost of care for our members, risk adjustment and accurate condition capture has many benefits for you and your patients. This scrutiny of medical records is a compliance measure to ensure our payments from CMS are based upon reliable and accurate records from physicians and facilities. Aside from payment inequities, undocumented, inaccurate or missed diagnoses can lead to members not receiving the quality of care they need to lead healthy lives. To help you understand the impact of these updates, please join Zeb Clayton and Chris Attaya for a National Webinar on Wednesday, August 11th 2021 at 11am PT / 2pm ET.
This page contains brief descriptions of each measure type and how the data for that measure is calculated. TheDownloadssection below provides links to technical documentation, tables identifying which Home Health Quality Measures are risk-adjusted and reported publicly, and additional resources. The method that the Center for Medicare and Medicaid Services uses to adjust payments to health plans for both commercial and Medicare plan members depends on accurately capturing claim diagnosis codes affiliated with an HCC . By risk adjusting plan payments, CMS can make accurate payments to health plans for enrollees with differences in expected medical costs.
There is an entirely separate risk model for enrolled with ESRD (end-stage renal disease). This model uses separate calculations for long-term care vs. new enrollees. This model has been adjusted multiple times to account for changes in medical coding.
For example, the likelihood of disability, reduced life expectancy, or needs for specialists, therapy, or hospice care is all highly considered under this model. This model is often referred to as the Case Mix model because it is used for both risk adjustment and research. Risk Adjustment Factors — known as RAFs — are the average risk scores for specific HCCs. They’re used in combination with demographics to determine an individual’s final risk score. The higher a person’s RAF, the more likely it is that they’ll end up in high-risk adjustment programs or see increased premiums due to their diagnosis and demographic information. These codes have all been assigned a specific value for risk adjustment.
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