Improving Value-Based Contract Performance with Flexible Analytic Attributions

Improving Value-Based Contract Performance with Flexible Analytic Attributions

Patient-Attribution

Data that is linked to your patients and needs. That’s good, right? The concept seems simple enough.

Yet, attribution – the process of assigning patients to a provider or organization – can range from basic to highly complex given the myriad of factors that come with appointing responsibility for a patient’s care.

Turns out, many of the challenges surrounding attribution are because consumers receive care from multiple providers. And because of this, there isn’t always clarity around who is primarily responsible for health and financial outcomes.

The best analytics are flexible with supporting both internal operational needs (like process improvement or care management activities), as well as external reporting needs (like appropriately defining the population for which you are being held financially accountable, as is the case with many pay-for-performance programs). Therefore, having an analytics platform with reliable and flexible patient-provider assignment is necessary for giving you data that you care about and can act on.

While there are hundreds of different models, to keep it simple, let’s look at two main attribution categories: retroactive and prospective.

#1 Retroactive

Assigns patients after care has been received. With this model, the payer determines the provider’s patients that they are accountable for after the performance period ends.

Historically, retroactive attribution has been more prevalent as it has the perceived benefit of greater accuracy because it is based on how care was actually delivered. The downside is its less timely, creating ambiguity throughout the year in understanding which patients a provider is financially accountable for.

Retroactive attribution can be defined differently contract to contract. Below are a few examples:

Patient Type (chronic, age) 
Provider Type (primary care, specialists) 
Concentration of Care (majority, plurality)
Type of Encounters (E&M visit, hospitalization) 
Cost of Care (amount billed, total cost of care)

#2 Prospective

Assigns the patient-provider relationship before care is delivered. For prospective attribution, payers provide providers a list of patients they are responsible for at the beginning of the performance period. Numerous approaches can be used to set the criteria for this assignment. Some examples include analysis of the previous years’ claims, patient attestations or hybrid approaches with prospective patient assignment with retroactive reconciliation.

The advantage of prospective attribution is that because of its timeliness, it reduces the uncertainty around which patients one is accountable for. The major drawback is that it can be less accurate, creating imprecise representations of performance while also creating the opportunity for providers to game the system by basing care not on patient needs but alternative agendas, simply to improve measure and payment rates.

A timely example of prospective attribution is CMS’ new Primary Care First Model. This model’s attribution process has, at a high-level, two steps to assign patients to a provider or practice:

  1. Voluntary alignment: Medicare beneficiaries attest the patient-provider relationship by specifying a provider that he/she considers primarily responsible for providing their health care.
  2. Claims-based: To assign any remaining beneficiaries, CMS examines the most recent 24-months of claims data based on the presence and plurality of chronic care management-related services, annual well visits, welcome to Medicare visits and eligible primary care visits.

For this program, CMS sends each practice a list of attributed patients at the beginning of each payment quarter to determine the risk group for performance measurement and associated payment amounts.

Where Flexible Analytics Comes In

As attribution is built into value-based care models, it is at the heart of performance measurement and payment. The value of analytics is that it helps you understand, evaluate, and improve the health of your population. Analytics that are built to a specific attribution model make it more likely that you’ll act on the data for the simple fact that you will be able to identify your patient population. Analytics, like KPI Ninja’s, that go a step further by supporting all the attribution models that in exist in your different value-based contracts, make it possible for you to be successful with all of your contracts, no matter how patient assignment is defined. Even further, we here at KPI Ninja understand that even the best pre-defined models need some level of customization at the local level. Which is why we have a built-in attribution self-service tool, so that you can easily manage your patients active and inactive status. To learn more about how we can help your unique attribution needs, schedule a one-on-one call with us.


Renee Towne

About the Author
Renee Towne
VP of Population Health at KPI Ninja, Inc.
Renee provides operational leadership of quality initiatives at KPI Ninja. Towne has a background in occupational therapy, education and experience in operational excellence across a variety of healthcare domains. Based on prior experience as a clinician that drove outcomes patient by patient, she is leaving a larger footprint by improving health care more comprehensively, population by population.


About KPI Ninja
KPI Ninja is a data analytics company that helps healthcare organizations accelerate their quality, safety, and financial goals with a unique combination of software and service. We are differentiated by our signature mix of technology, performance management consulting and healthcare expertise. We don’t merely offer software solutions but work shoulder to shoulder with clients to help them draw on the power of analytics and continuous improvement methodologies to become more efficient. In harmony with our data-centered ethos, we truly believe that our success is strongly co-related with yours.

Tags: , , , , ,