Disease registries can be used to identify aberrant coding patterns. Analyzing the disease registry data can help identify under- and over-coding areas. For example, patients may be entered in a diabetes registry based on prescribed medications e. Diabetes coding, for presumed diabetic patients included in the diabetes registry, should be analyzed at least annually to identify any coding patterns suggestive of gaps in HCC reporting.
Once aberrant coding patterns are identified via data mining, chart review should be performed. The purpose of the chart review is to determine if there is a gap in either coding, clinical documentation, or patient care that should be addressed.
Some examples of disease registries that correlate with HCC conditions include:. Managers should explore which disease registries are maintained in their organization or state, identify the data sources used to derive registry information, and consider how those registries might be utilized to monitor compliance in their HCC program.
This data set can be used to research disease prevalence by demographic elements. Like disease registries, payers can use data mining to help identify gaps in documentation and diagnosis capture. Reporting below the prevalence rate could mean under-coding while reporting higher than the prevalence could mean over-coding. By creating algorithms, payers and providers may identify opportunities for HCC capture.
Using rheumatoid arthritis as an example, medication data can be used to identify patients with active prescriptions for methotrexate. Laboratory data can identify patients that have a Rheumatoid Factor test with a positive result. This data can then be compared with diagnosis codes in the claims data. Patients with positive medication and laboratory data but without rheumatoid arthritis diagnoses can be targeted for further review.
Another approach to examine reporting patterns is using claims data to identify anomalies. For instance, a patient with HIV might be expected to receive multiple healthcare services. Therefore, if a patient has HIV reported only once in the calendar year, then the risk adjustment coding professional may initiate a health record review to confirm the presence of this HCC. Similarly, it would be unusual for a patient to have multiple strokes in a brief period.
If the code for CVA is reported for multiple office visits, then the risk adjustment coding professional should examine health record documentation to determine if a diagnosis code for stroke residuals or sequelae is more accurate.
These data-driven methods are extremely useful to focus resources on detecting and correcting aberrant coding patterns that result in incomplete or inaccurate HCC reporting. It is best practice for both payers and providers to identify and address areas of concern, before the end-of-year final submission, to ensure compliance with HCC standards.
The final area of focus in risk adjustment coding is a robust audit and monitoring program. High quality data and coding accuracy promote compliance.
Given the complexity of documentation and coding for accurate HCC capture, it is best practice for both healthcare provider organizations and payers to conduct regular monitoring for correct coding. Risk adjustment coding leaders should monitor for the following common coding errors:. Coding leaders should review these problem areas with risk adjustment coding professionals.
Not only are leaders ensuring opportunities for additional HCCs are leveraged, but they are also ensuring opportunities to correct erroneously reported HCCs are identified.
The balance is essential to ensure overall coding compliance. A common opportunity would be related to malignancies where the coding leadership would ensure that documentation clearly supported active or historical status to avoid inappropriately capturing a HCC that could lead to an inflated RAF. Table 5 provides characteristics for three types of RADV audits. The goal of RADV audits is to ensure that the health status submitted by the plan is supported by health record documentation and meets reporting guidelines.
Expert coding professionals are utilized to validate reported HCCs with submitted health record documentation. The reviewer will determine if the HCC is supported or unsupported. The results from the RADV audit should be analyzed by coding management to identify patterns of incorrect coding or health record documentation insufficiencies.
These lessons should be communicated to leadership, providers, and coding staff. Revised guidance and procedures should be incorporated into the HCC management plan. A best practice strategy for risk mitigation from both the payer and provider perspective is to conduct an internal mock RADV audit.
There are two approaches that may be utilized to execute a mock RADV. The first is to mimic the CMS process as closely as possible by selecting a random sample of patients for a full-scope retrospective chart audit. The second approach is to select a targeted random sample. The targeted patient population may be patients that have a specific HCC or set of HCCs, demographic characteristic, or previously identified areas of concern. Organizations that have never participated in a targeted CMS-RADV audit should consider the first option to achieve a baseline measurement.
Ideally, conducting each on alternate years, or based upon opportunities identified in the results, will be most beneficial. The most ideal time of year to conduct a mock RADV would be in the third quarter of the year.
The mock RADV data can be used to close gaps and rectify identified opportunities in the base year. This helps ensure a complete and accurate RAF for the benefit prediction year.
Risk adjustment coding requires health plan management, provider group management, physicians, non-physician providers, and highly skilled coding professionals to work together to capture the health status of their patient membership. Each player is critical for success under the risk adjustment programs.
Health plan management and provider group management must provide leadership that supports the risk adjustment coding department to execute initiatives to improve health record documentation and risk adjustment coding. Risk adjustment coding professionals must follow best practice guidelines to ensure accurate coding and reporting of HCCs on a yearly basis.
By working together, the health plan and provider organizations can ensure compliance and optimal financial results under HCC risk adjustment models. Centers for Medicare and Medicaid Services. March 21, Fee, James P. Pope, Gregory C. Anne B. Each diagnostic category is a set of ICDCM codes that relate to a reasonably well-specified, clinically meaningful disease or medical condition that defines the category.
Diagnostic categories that will affect payments should have adequate sample sizes to permit accurate and stable estimates of expenditures.
Costs are additive across hierarchies and disease groups, but not within hierarchies. Vague diagnostic codes should be grouped with less severe and lower-paying diagnostic categories to provide incentives for more specific diagnostic coding.
The model should not measure greater disease burden simply because more diagnosis codes are present. Predicted costs are not increased by the number of times a particular code appears or the presence of additional, closely related codes indicative of the same condition.
Providers should not be penalized for recording additional diagnoses. This requires that no HCC should carry a negative payment weight and higher-ranked diseases in the hierarchy should have at least as large a payment weight as lower-ranked disease. If diagnostic category A is higher-ranked than category B in a disease hierarchy, and category B is higher-ranked than category C, then category A should be higher-ranked than category C.
Because each diagnostic code potentially contains relevant clinical information, the model should categorize all ICDCM codes. Diagnoses that are subject to discretionary coding variation, inappropriate coding, or that are not credible as cost predictors should not increase cost predictions. Source: Centers for Medicare and Medicaid Services. Model considers serious manifestation of a condition rather than all levels of severity of a condition.
Includes most body systems and conditions. Risk adjusted payment is based on assignment of diagnoses to disease groups, also known as Condition Categories CCs.
Model is most heavily influenced by Medicare costs associated with chronic disease. Condition Categories are placed into hierarchies, reflecting the severity and cost dominance. Beneficiaries get credit for the disease with the highest severity or the one that subsumes the costs of other diseases. Hierarchies allow for payment based on the most serious conditions when less serious conditions also exist.
Interactions allow for higher risk scores for certain conditions when the presence of another disease or demographic status e. Disease interactions are additive factors and increase payment accuracy. Models include five demographic factors: age, sex, disabled status, original reason for entitlement, Medicaid or low-income status. These factors are typically measured as of the data collection period. Medicare Managed Care Manual. In the HCC models, HCC conditions are hierarchical, meaning diagnoses that are clinically related are ranked by severity in a hierarchy.
For example, there is a hierarchy for diabetes see Table 3. Only one of the three diabetes HCCs may be reported for a patient per year. When a hierarchy is not applicable, the HCCs accumulate for a patient.
For example, a male with heart disease, stroke, and cancer would be assigned three separate HCCs, and his RAF would include the sum of the relative factors for all three categories e. Thus, HCC models are additive across hierarchies and disease groups, but not within hierarchies.
Certain combinations of diseases have been determined to increase the cost of care. For example, a patient with diabetes and CHF has higher expected costs than a patient that has only diabetes or a patient that has only CHF. Disease interactions result in higher risk scores when the disease pairs are present. The model includes disease-disease interactions as well as disability-disease interactions.
Primarily used for Medicare Advantage Part C reimbursement. Concurrent: Uses data from the current benefit year to predict costs for that same year. Includes a special needs plan for individuals with severe or disabling chronic conditions. Our reference manual provides valuable and comprehensive risk management guidance and resources for healthcare practices.
The Risk Management Manual for Healthcare Practices provides an overview of the elements that a practice-based risk management program might include. The manual provides valuable information about the basic framework of a risk management program, answers to frequently asked questions, and links for obtaining more information on numerous topics.
Healthcare providers and their staff members can use this manual to identify the risk management elements that are essential to developing and maintaining their own customized risk management programs. Log In to View. Prevention Center.
What Makes Us Different? Who Are You? Healthcare Practice Manual The Risk Management Manual for Healthcare Practices provides an overview of the elements that a practice-based risk management program might include.
0コメント