![]() ![]() Missing documentation to support reporting of factors influencing health status and contact with health services Z00 – Z99 (e.g.We are able to identify areas of concern and make recommendations for improvement in documentation and coding practices.īelow are the top ten coding pitfalls to be aware of related to the identification of HCCs: UASI performs HCC coding, auditing, CDI and education for clients to ensure compliant practices and appropriate reimbursement. Specified heart arrhythmias, including atrial fibrillation.Diabetes with manifestation and/or complications. ![]() Therefore, it is critical to physician practices’ financial viability to invest resources into ensuring chronic conditions are being documented and coded comprehensively, according to CMS guidelines.īelow is an example of prevalent, often missed HCCs: If the coding is accurate, reimbursement from CMS or other payers utilizing HCCs is then accurate based on the patients’ severity of illness.Īs such, reimbursement can be negatively impacted for an entire fiscal year without thorough documentation and coding. As demographics typically remain the same, it is the physicians’ documentation and accurate code assignment in which they can have the greatest impact on future reimbursement. In the risk factor model, reimbursement is calculated annually based on the patient’s demographics and their chronic conditions (HCCs). Payments were thus driven by CPT codes and their relative value. In the historical fee-for-service model, reimbursement was based on the services physicians provided. Now, physicians will be compensated based on the estimated health cost according to risk profiles, as opposed to the prior fee-for-service methodology they are familiar with. However, with their increased impact on professional fee reimbursement due to the implementation of MACRA, HCCs have become a hot topic and, therefore, a focus point in every coding arena. We will also identify missed RAF scoring opportunities and tackle the under-documentation issues with a solid CDI program.Hierarchical Condition Categories (HCCs) have been around since 2004. Our experienced HCC coder will validate the medical record eligibility and assign the appropriate ICD-10 CM codes if the conditions meet TAMPER criteria (Treatment, Assessment, Monitor/Medicate, Plan, Evaluate, Referral) or MEAT criteria (Monitor, Evaluate, Assessment, Treatment). The HHS-HCC model is concurrent, meaning the model predicts future expenditures associated with the current year’s health events. For example, data from 2020 (base year) will be used to predict expenses in 2021 (prediction year). The CMS-HCC model is prospective, meaning the data is collected in the base year to determine expected costs for the following year (the “prediction” year). The second primary data source health status is based on the ICD-10-CM diagnosis code. ![]() Demographic data includes the patient’s age, gender, and other population-specific factors. ![]() HCC models use two primary sources of data to determine a patient’s Risk Adjustment factor (RAF), which are demographic characteristics and health status. These HCC models use patient data to predict the estimated future costs for individual patients. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |