Risk Adjustment

How to Conduct Clinical Suspecting to Capture HCCs

Meleah Bridgeford
Written By
Meleah Bridgeford
Director of Risk Adjustment, Payer Solutions

As a primer for this post, a quick definition of the process being discussed: Clinical suspecting is the process of identifying possible conditions that are indicated in clinical data but remain undocumented.

Suspecting insights can be applied bilaterally to clinical managed care as well as risk adjustment. Clinicians will understand that better managed care can be provided to a member when that member’s health history is more informative. Gaps in a member’s history stand to be clarified when suspecting unveils procedures performed or inaccurate coding within a chart. Likewise, the same inference can result in the opportunity of capturing new Dx codes that risk adjust to HCCs. By reevaluating member data, plans can conserve revenue through preventive interventions and ensure accurate reimbursement in future CMS payments.

 

MOR and YOY are a Start

 

Most payers will use CMS’ Model Output Report (MOR), which provide the HCCs that have already been captured for the member. For example, if a member joined in 2018, the MOR will have a list of what HCCs the member had for 2017 dates of service. Although the MOR will not have the data on what providers the member saw in 2017, it does provide direction when targeting HCCs for 2018 dates of service.

Another common method is to look at conditions YOY to identify members who lack key data. Suspect analytics can target specialists the member saw or the provider the member saw most. Using provider score cards, for example, can clarify which providers tend to under-code certain chronic conditions. Then a retrieval project can be employed to pull charts from the provider office and provider education can be conducted to teach how to accurately document the codes.

When looking at your data for clinical suspecting, it’s important to have a robust data set and the right population set to run the statistics. With any statistical model, the larger your population set, the more accurate your results will be. Some plans choose to rely on a vendor however, because they have multiple clients and they can run statistics around larger population sets. For smaller plans in particular, this is an advantage because it’s harder for them to get correlations (the link between the procedure code and the likelihood of the diagnosis) because they will have a lot of false positives that can skew the data they’re looking at.

Some procedures are straightforward of course, such as the procedure code for the monitoring of Human Immunodeficiency Virus (HIV), while others will be more complicated and difficult to identify. For example, one procedure code that can result in a lot of false positives is type-2 diabetes. Although a member is being tested for it, that doesn’t necessarily mean they have it. It’s important therefore, to consistently stay up to date on model changes and integrate new HCCs and procedure codes as they’re announced.

 

Analytics & Retrospective Chase List: Best Practices

 

When looking for opportunities, it’s important to have complete access to your analytics data. You should be able to see demographics, providers, encounters, RAF details, HCCs captured and their HCC reconciliation.

A crucial, yet often overlooked, aspect of analytics is having a “clean” provider database. The chase list should be populated with up to date, verified data on addresses, phone numbers, and fax numbers, along with any other relevant information. It’s not uncommon for payers to have provider contact information that’s inconsistent with what the provider relations or provider management groups have. The more accurate your data, the higher your retrieval rate and the increased likelihood that you’ll capture the HCCs.

You should also be able to segment your member data. For example, you can look at member behaviors the previous year and determine whether or not you were able to close care gaps.

Generating a retrospective chase list is an analytical process that takes into account lab tests and results, diagnosis codes that correspond to those labs, and looking at members who are missing YOY recaptures of chronic conditions. To ensure success, it’s important to take stock of your providers and those that might yield higher results. On the flip side, if you have a provider who consistently under-codes, you would want to retrieve the chart for all of the member’s encounters with the provider.

At Episource, our clients have full transparency into our process and visibility into their projects at every step of the way. With epiAnalyst, we not only identify clinical suspects and the risk score, but we add our confidence points which explain the likelihood that an opportunity exists to ensure your success. To learn more, contact us today.

 

Meleah Bridgeford
Meleah Bridgeford
Director of Risk Adjustment, Payer Solutions
Meleah Bridgeford is the Director of Risk Adjustment, Payer Solutions at Episource. Meleah oversees risk adjustment analytics and services and plays a vital role in supporting our epiEncounter submissions solution. She has over 10 years’ experience within healthcare organizations as well as vendor/consultant organizations.

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