Inaccurate reporting on patient diagnoses can have a dramatic impact on your Medicare reimbursements.
This article explores the role of accurate HCC coding in risk adjustment, offering actionable steps to ensure you are receiving the maximum possible reimbursement.
Hierarchical Condition Category, or ‘HCC’ coding, is a risk adjustment model in which codes carry a specified risk score used to assess the risk linked to a patient’s health management and the projected cost of providing care. HCC coding is subject to guidelines and data extraction requirements, where clinicians and physicians need to ensure that all diagnoses and other information are correctly recorded and submitted for risk-scoring analysis.
Diagnoses suggestion tools used to determine the appropriate HCC risk adjustment code can include any suitable and medically recognized approach. However, HCC codes also cover grouped categories, such as ‘diabetes with chronic complications,’ which could refer to several patient diagnoses, each with a different cause or complication.
The standardized nature of HCC risk adjustment codes aims to ensure that risk scoring is current, accurate, and supported by appropriate medical records. To meet this objective, the data extraction method used by a healthcare provider must be comprehensive, ensuring diagnoses and events are not excluded from reporting–which can result in incorrect or missed payments issued by health plans and payers.
HCC coders specializing in diagnosis capture and advanced data extraction software can meet this criterion, incorporating diagnoses documented by a physician, clinician, ambulatory service, or other third party. Records must comply with mandatory requirements, such as being supported by signatures or other verifications and detailing the type of provider issuing the service, making the diagnosis, or reporting the condition.
Anticipating the future financial resources necessary to meet the needs of a patient and forecast reimbursement values helps healthcare providers plan, budget, assess resource allocations, and issue timely and complete claims. Data has further uses in communicating the complexity of a patient’s overall health and measuring cost performance against outcomes.
The Centers for Medicare and Medicaid Services (CMS), requires all healthcare organizations to identify HCC codes that indicate diagnoses, events, or conditions for each patient, and that they are reported annually. One complication is that any documentation or records submitted that do not specify a diagnosis or have incomplete data may impact reimbursable values, often meaning that quality care is not fully compensated due to administrative errors.
Optimizing data capture to extract data from multiple healthcare providers results in better documentation, improved care for patients presenting with chronic diseases, accurate reimbursements, and greater precision during HCC risk adjustment assessments.
The CMS requires services to submit reports once per year, with corroborative records that indicate the reason for the diagnosis. While some HCC risk adjustment codes do not carry an assigned risk adjustment value, they may remain reportable.
Collating all the relevant data is essential to:
Risk adjustment factors are used to calculate reimbursements and predict cost considerations involved in patient care management. The underlying data is also invaluable in stratification, where the CMS and other bodies may sort data into groups to extract meaningful insights and metrics.
About the Author
Sunil Nihalani, M.D is the founder and CEO of Inferscience. He spent over 10 years practicing internal medicine and gastroenterology before shifting his focus to tech – building solutions that are trusted by leading healthcare providers to streamline risk adjustment workflows and improve the accuracy of HCC coding.
