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Whether you are a Payer, Provider Group, or a Government entity, managing Risk in healthcare is a complex, resource intensive activity. Automated risk analytics help to significantly lessen the burden on already limited financial and clinical teams. Analytics provide direct insight into the efficacy and accuracy of your population health management program.

The Risk Insights application supports the most widely used risk models in the healthcare industry, including the Johns Hopkins ACG system, CMS HCC, and the HHS HCC models. Even if you work with other vendors, for risk adjustment services, having the ability to model and run scenarios on your own data can be invaluable to your business.

Let's explore together how RBI Healthcare Insights, single, fully integrated platform can support your organization's Risk Program needs across Medicare, Medicaid and Commercial lines of business.

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Not a one size fits all approach, RBI Risk Insights models are configurable to every client's specific needs - let's discuss how, so that we can put our solution's unique analytics capabilities to work for you.

The RBI risk analytics engine is powered by the Johns Hopkins ACG system. At its' core Risk Insights' risk modeling applies case-mix measures and statistical forecasting to predict future health resource needs.


Risk Insights' predict a large range of financial and clinical outcomes, some of which are detailed below:


RBI Risk Insights calculates actual and predicted member Total Cost in the year following a given observation period.

  • Total actual Medical costs​ plus

  • Total actual Pharmacy cost

  • Predicted Total Cost for the year following the observation period

Advanced visualizations permit drill down to the population, sub-population, morbidity, provider and patient levels. Costs can also be analyzed at any level offering virtually unlimited slice and dice  capabilities - eliminating spreadsheets 


RBI Risk Insights identifies patients with a risk of future unanticipated hospitalizations, using utilization markers such as:

  • Inpatient hospitalizations,

  • Emergency department visits,

  • Outpatients visits,

  • Dialysis services,

  • Nursing services, and major procedures 

RBI Risk Insights leverages a wide range of markers that are captured in the system in addition to the traditional predictive modeling variables to accurately predict future hospitalization


The High Risk for Unexpected Pharmacy Cost Model predicts the subset of the population who are consuming drugs beyond what's anticipated based on their diagnosis-based morbidity burden

  • Evaluate High Pharmacy Costs

  • Identify High Pharmacy Users

  • Identify quality issues including:

    • Poly-pharmacy

    • Poor data, e.g. missing Dx codes

  • Verify Care adequacy

  • See Gaps in Care


RBI Risk Insights leverages predictive models, and sophisticated tools that facilitate in depth analyses and powerful insight into the drivers behind high unexpected Pharmacy Costs


RBI Risk Insights uses administrative claims to identify gaps in medication adherence for chronic conditions where continuous medication use is warranted.

Adherence measures include:

  • Medication possession;

  • Continuous medication availability;

  • Prescribing gaps; and

  • Conditions indicated for but untreated with medication.

RBI Risk Insights provides predictive modeling, reporting and sophisticated dashboards to identify members with adherence gaps, so that you can take appropriate action to address them.


Higher than expected 30 day readmission rates may be perceived as a signal of lower quality of care.

  • The unplanned 30 day readmission model is calibrated so that scores are available at the time of admission to the hospital.

  • Patients are not required to have a prior hospitalization in order to receive a risk score for unplanned readmission

RBI Risk Insights 30 day readmission predictive model enables healthcare delivery systems to anticipate discharge planning needs for patients at risk of readmission in the event of an admission


RBI Risk Insights uses complementary markers to assess coordination of care.

The markers provide a means to flag the major contributors to potential care coordination issues such as:

  • Patient's principal source of care;

  • Count of unique practitioners providing outpatient care;

  • Generalist involvement; and

  • Identifying populations at risk for poor coordination.

Coordination of care challenges have a major impact on important care programs. RBI Risk Insights helps you get ahead and stay ahead of these challenges.



Model Updates and Deep Dive Analytics

The CMS HCC model is used in the Medicare Advantage program and is built upon diagnosis-based condition categories, which are then grouped into hierarchical condition categories (HCCs) that prioritize the relative risk of all health conditions. The HCC's are associated with different levels of reimbursement that reflect the risk posed to a patient with a health condition in a given category. A patient is only coded with the most severe condition in any given category.

HCCs are not strictly hierarchical, a patient's health conditions may also be additive and/or associated with interactions. Demographic and health risk factors are the two main components of the Medicare Risk Score calculation that determines the capitation rate for the member. The values associated with these factors are revised by CMS annually.


RBI Risk Insights keeps abreast of those changes so that you don't have to, and all relevant model changes are incorporated into our data models so that your reporting will always be up to date and accurate. In addition to keeping you in sync with CMS model changes, P3Insights provides reporting and advanced analytics capabilities on your data, providing insights that can be shared across stakeholders in your Medicare Advantage business.


Model Updates and Deep Dive Analytics

Challenges abound for participants in the Exchanges, where tracking and reporting across populations with such varied risk profiles is a huge task. 

The HHS HCC model is built upon diagnosis based condition categories, which are grouped into hierarchical condition categories (HCCs). Staying abreast of updates to the published HHS models is hard, so we do the hard work for you - providing model updates, and the capability to perform in-depth reporting and analysis on the data that you use to support your ACA Exchange business.

RBI Risk Insights leverages data from Claims, Enrollment, Member Demographics, Providers, and Social Determinants Of Health (SDOH) data sources. Using our advanced data modeling capabilities that are optimized for analytics, P3Insights surfaces your data offering unmatched flexibility, deep dive analysis and self service capabilities to everyone in your organization. 

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