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Demonstrating Outcomes for your Value-Based Payment Requirements

Outcomes Analytics

As Value-Based Payment models kick in across the United States, Behavioral Health Providers face a shifting focus from volume-based to a greater emphasis on client outcomes, evidence based practice, cost and population management. These challenges are lining up in a context that is continuously expanding, growing from a pure behavioral health focus to a larger integrated focus involving physical health and overall client functioning. Through experience gained in working with Behavioral Health Providers across the country towards meeting these challenges, it is increasingly clear that some of the greatest challenges in pursuing these goals lie in the areas of data capture, information management and data analysis.  
Providers need concrete techniques and methods that are practically deployable, meaningful to practicing behavioral health staff, and effective at providing insight and opportunities for improvement, in order to ramp up the outcomes and population management segments of their practices. The techniques and examples have been gleaned from actual provider experiences and are applicable to program directors, research analysts, senior leadership, and quality directors seeking to develop and deploy techniques for analyzing, demonstrating, and improving outcomes in support of continuous quality improvement.


These examples were developed by leveraging business intelligence technology. The framework is methodological and the techniques are applicable to a broad spectrum of providers regardless of their EMR and software infrastructure.

Outcomes  - Understanding Initial vs. Latest results for CAFAS  (example above) 
behavioral health outcomes analysis
Outcomes  - Understanding Client History for Diastolic - Integrated Care  (example above) 
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The techniques used to create theses models were developed in the context of behavioral health practice environments that are typical of the integrated healthcare setting. These include: 1) a behavioral health provider teaming with primary care physicians from a local hospital or, 2) a behavioral health provider using on-board staff to provide medical services and monitoring.


In each case, the providers started out by capturing physical health attributes that could be meaningfully used in the context of studying and managing outcomes, and to develop processes for improving overall client well-being and continuous quality improvement. 

Outcomes Analytics in a Value-Based Payment Environment

In a Value-Based Payment environment, the provider must understand treatment costs, outcomes for patient populations, obtain a larger picture of their patient population across the spectrum of care and healthcare utilization, as well as clinical and organizational process impacts on cost, such as staff productivity, kept percentage rates, etc. Most providers are only beginning to make significant inroads into the overall Value-Based Payment equation.


Understanding patient populations is at the heart of Value-Based Payment, providing a means for the provider to target high utilizer, co-morbid, and at risk populations with care models tailored to their specific needs. Gaining an understanding of the characteristics of specific populations, their demographics, utilization, outcomes, and co-morbidities, is the foundation for meaningful predictive analytics.

CCBHC reporting is an example of  integrating physical and behavioral health as well as care-coordination can leverage analytics in support of partner reporting requirements. Tracking HEDIS measures is another case of where out of the box reporting will fall short.

Analysis of Population Responses to Treatment using Severity Shifts (example above) 
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Key data model enabling characteristics includes analysis model for determining initial and latest per program(s), analysis model for determining to assess scores by severity level, table-driven outcomes specification and comprehensive dimensionality.

• Initial vs latest following treatment

• Changes in averages

• Number improved / declined

• Change in severity level

• Built-in T-Tests to determine significance

• Value of treatment per program (program evaluation)

• Analysis of outcomes interactions and co-morbidity

• Automation of mandated reporting

• Automated distribution to stakeholders (outcomes analytics dashboards)

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