HCC RISK ADJUSTMENTS
Background on Hierarchical Condition Categories
Hierarchical Condition Categories (HCC) help determine the risk factor of members with chronic conditions in order to improve patient care. HCC correspond to International Categorization of Diseases (ICD) diagnostic codes and organize them into a payment model mandated by the Centers for Medicare and Medicaid Services.
HCC codes can be captured or uncaptured.
· When they are captured, it means patients have been seen for their needs.
· Conversely, uncaptured means patients have not been seen for one or more conditions that increase their health risk.
An uncaptured HCC code can be an “inference” or a “persistence.”
· An inference is a healthcare concern flagged by the analytics platform based on patient data. It is an alert of potential diagnoses that have not been made.
· A persistence is also an uncaptured diagnosis, but because of the persistent pattern of occurrences, is more indicative of a health risk than an inference.
THE CHALLENGE
Providers have many platforms to manage such as Supplemental Data, Member Prioritization, Member Journey Maps, and Trend Maps etc. These, together with the HEDIS measures, all need to be connected to ensure continuity in order to close gaps in patient care. Healthcare professionals need technology to capture various sources of data about their patients and meet requirements of various governing agency for quality assurance.
Our goal was to create an integration platform that gives healthcare providers visualized data from multiple sources such as medical billing codes (HEDIS), diagnoses (ICD), chronic condition risk factors (HCC), and medications to ensure compliance on standards of care. There were various challenges to solve for this overarching goal:
1) Develop two workflows that capture inference and persistence codes
2) Ensure that physicians do not overlook inferences because they don’t like that the analytics platform is telling them what to do
3) Provide an easy reference for physicians to look up continuously changing ICD codes
HOW I CONTRIBUTed
My contributions to resolving these three challenges:
1) Develop two workflows that capture inference and persistence codes
a. I interviewed physicians to document their existing workflows and used that research to design the UI in our product to parallel the doctors’ work processes.
2) Ensure that physicians do not overlook inferences because they don’t like that the analytics platform is telling them what to do
a. Here is the essence of psychology-guided service design. Doctors needed to be trained to use our system. I enhanced the training program to validate the emotional reactions of the doctors, and reduced their anxiety by showing them how these alerts needed the doctors’ expertise in articulating an effective treatment plan. In other words, making sure the doctors weren’t feeling replaced by the diagnostic inference system by framing it as an aide to enhance their success.
