Risk adjustment has incentive insurance companies to provide memberships to patients who need it the most by “adjusting” the risk of enrolling chronically ill people along with healthier patients, ensuring that providers are adequately compensated according to each patient’s underlying health patterns, and the care provided.

To ensure a mutually beneficial risk adjustment program, payers and providers must work together and equip each other with the right tools and information to play an active role in the process. Most providers lack the knowledge and expertise and may not have the time to grasp all the technicalities of coding and submitting reports. On the other hand, payers may not have access to clinical data crucial in determining the validity of claims and risk predictions.

Efficient exchange of data is the backbone of successful risk adjustment, and leveraging data through AI-driven technology ensures accuracy within the risk adjustment process to help companies achieve the HCC Risk Adjustment Pass rate of 100%.

Here are some of the ways streamlining your data exchange can improve the risk adjustment process of your organization:

Targeting the right pool of members

Timely data collection, accurate diagnoses, and efficient coding are the three most crucial components of a risk-free risk adjustment program. With the help of artificial intelligence software, providers can share timely reminders with patients that have fallen behind on their annual health exams (leaving a gap in their clinical data for that year), or have been missing their scheduled appointments, so that information on each patient can be collected to create an accurate RAF score. It is essential for patients with multiple conditions with more complex requirements

Providing support through Clinical Decision Support Systems

True to its name, a Clinical Decision Support System allows providers to make better decisions on the go by providing alerts for patient-specific data, such as the interaction of multiple drugs in their system, as well as a patient history of a specific treatment or drug, so providers can more accurately choose the treatments for their patients.
This patient-specific data can guide providers and create more accurate RAF scores to help meet payers’ risk adjustment requirements.

Providing administrative support

The tedious nature of data collection is a common pain point for providers and payers. It consumes a lot of time and resources, creating unnecessary waste and frustration. Tools like Natural Language Processing (NLP) support administrative efficiency by automating data extraction and organization, improving the communication and data exchange between payer and provider systems.

Using NLP to “read” encounter data

An essential part of ensuring that your risk adjustment score is accurate is sharing clinical data supporting your claims. Up to 80% of clinical encounter data is from doctors’ notes and comments. NLP captures the unstructured encounter data and automates the analysis of it, translating it into meaningful information that can be used to chart conditions and support claims data.

Automation of workflows to achieve operational efficiency

Traditionally, payers hired experts to review charts by manually combing through hundreds of clinical documents.
Using AI-powered software to automate chart reviews for each patient speeds up the process and allows payers to identify underlying health patterns of a population and predict future conditions that more accurately report future health needs of those clusters of patients.

Coding and charting assistance

As providers face increasingly complex charting and coding requirements, AI-driven platforms can guide physicians and other providers by suggesting appropriate codes based on algorithms using real-time data.
Advanced NLP uses clinical data to extract relevant sets of diagnoses and HCC codes for each patient, streamlining the process.

Risk adjustment program is a delicate process, dependent on accurate data exchange from payers and providers. Artificial Intelligence has the potential to transform the way both providers and payers collaborate during the risk adjustment process, ensure accurate HCC coding, and minimize the burden to ensure a smooth payer-provider relationship.

Persivia’s CareSpace® platform uses a unique blend of data aggregation, analytics, and clinical tools backed by AI technology to enhance the performance of providers and payers and reduce clinical burden while ensuring high HCC risk adjustment rates. With the ability to combine structured and unstructured data using NLP, CareSpace® delivers highly accurate patient insights and HCC scores at the point of care to help improve the quality of care.

Reach out to us for a demo to learn how CareSpace® can help you under perform the Risk Adjustment Solution.