The use of external data for better Hierarchical Condition Category (HCC) risk adjustment is a topic of growing importance if not the most important one in the current healthcare trends. With the growing need to upgrade by maximizing technology, it is logical to consider everything and anything that would make coding highly accurate and less redundant.
HCC risk adjustment is a method used to assess the health status and expected healthcare costs of patients for accurate reimbursement in healthcare systems such as Medicare Advantage (MA) plans. HCCs are diagnostic categories that capture and classify a patient’s health condition/s based on the International Classification of Diseases (ICD-10) codes. The HCC system assigns a risk score to each patient, reflecting their projected healthcare costs. And these risk scores are what physician providers should be on the lookout for accurate reimbursement claims.
Traditionally, HCC risk adjustment has relied on the clinical and diagnostic information available within a healthcare organization’s own data systems. However, this internal, traditional data source may not capture the complete picture of a patient’s health status and associated risk. This is where external data sources offer valuable insights that can enhance the accuracy of HCC risk adjustment models and give providers a better chance for earnings.
One important source of external data is claims data. Claims data includes information from insurance claims submitted by different healthcare providers for services rendered to patients. By analyzing claims data from various sources, such as hospitals, pharmacies, and laboratories, a more comprehensive view of a patient’s healthcare utilization and treatment history can be obtained. Claims data can provide additional diagnostic codes, procedures, medication history, and specialty referrals that may not be readily available in a healthcare organization’s internal data.
Another valuable external data source is electronic health records (EHRs) from other healthcare providers. EHRs contain detailed clinical information about patients, including laboratory results, imaging reports, and specialist consultations. Access to EHR data from external sources allows for a more complete understanding of a patient’s medical history, including diagnoses and treatments that may have been conducted outside the primary healthcare organization.
Social determinants of health (SDOH) data is another crucial external data source that can improve HCC risk adjustment. SDOH factors, such as socioeconomic status, education level, housing conditions, and access to transportation, significantly impact an individual’s health outcomes and healthcare utilization patterns. Integrating SDOH data into HCC risk adjustment models provides a more comprehensive understanding of a patient’s health risks and can help identify individuals who may require additional support or interventions to manage their conditions effectively.
Pharmacy data is yet another external data source that can enhance HCC risk adjustment. Medication history, including prescription drugs and over-the-counter medications, can provide valuable insights into a patient’s chronic conditions, disease management, and adherence to treatment plans. Incorporating pharmacy data especially from multiple physician providers allows for a more accurate assessment of a patient’s real-time health status and associated healthcare costs.
The integration of external data for HCC risk adjustment does pose several challenges, but not without available solutions. One significant challenge is data interoperability and standardization which physician provider organizations are now being pushed to upgrade to. Healthcare organizations and data sources often use different data formats and coding systems, making it difficult to harmonize and merge data from multiple sources. To address these concerns, government efforts to establish data exchange standards and promote interoperability, such as the use of Fast Healthcare Interoperability Resources (FHIR), are now being supported to overcome these challenges.
Privacy and data security are also critical considerations when accessing and sharing external data for HCC risk adjustment. Compliance with relevant privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), is essential to protect patient confidentiality and ensure the responsible use of sensitive healthcare data.
Despite these challenges, leveraging external data for HCC risk adjustment has several benefits. It allows for a more comprehensive and accurate assessment of a patient’s health status, leading to fairer reimbursement and resource allocation for physician providers. Improved risk adjustment accuracy benefits both patients and healthcare organizations by ensuring that individuals with complex and costly health conditions receive the necessary care and support. Another support that can be utilized is by using HCC Assistant coding tools to ensure efficient and accurate coding of diagnoses and procedures.
In conclusion, incorporating external data sources into HCC risk adjustment models has the potential to enhance the accuracy and fairness of reimbursement in healthcare systems especially for physician providers who need to comply with government agency requirements and at the same time, improve their quality of services to gain more monetary earnings.
Organizations need to consider as part of their pipeline – if they have not done so – is to connect with trusted IT providers that can help solve their data strategy needs such as integrating into an EHR or FHIR database, to ensure that data parsing and interoperability is done seamlessly.
If you’re looking for a vendor to help with clinical data extraction, you don’t have to go t0o far. At Inferscience, we not only analyze the data in your EHR, we also have the ability to access patient data from external sources. Our 360° data approach pulls patient records that were generated at different healthcare providers, including lab and clinic results. We then present you with HCC coding suggestions based on analysis of a patient’s complete clinical history, for review at the point of care.
Inferscience saves manual documentation review time and helps improve HCC risk adjustment workflows.
