Behavior Predictor™ applying behavioral intelligence to drive healthy behaviors

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Changing behaviors requires both behavioral and data science. Behavior Predictor uses machine learning to identify, quantify, and address multi-dimensional drivers of health outcomes. As a result, Behavior Predictor can:

  • Pinpoint the most influential social and environmental determinants of health and individual motivators driving the most costly chronic conditions.
  • Quantify the opportunity of community health interventions and consumer experience investments by fusing analytics, behavioral science and public health expertise.

Behavior Predictor unlocks health and economic outcomes through analytics-powered personalization

  • Brings its own data: Combines individual-level datasets from multiple sources; including CDC surveys, consumer marketing surveys, geo-location data, the US Census,and proprietary PwC surveys.
  • Creates a “virtual laboratory,” customized to your population: A virtual population is optimized to represent a population’s specific geography, demographic, behavioral and health profile.
  • Predicts consumer behaviors: Machine learning algorithms predict consumer health behavior based on individual motivators, preferences, and social/geographic context.

Illustrating our capabilities in behavioral prediction and advanced segmentation

Optimizing community health ROI:

The issue
An integrated delivery network invested widely in community health, but did not know whether they were driving the full impact possible for each dollar spent.

Our solution
Using a combination of simulation modeling and machine learning PwC forecasted future health needs, drivers of future poor health, and ROI for 21 interventions in one target city.

Our impact
Community health investments can now be informed by the future impact of potential investments made today on health, wellbeing and medical costs .

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Behavioral segmentation within a disease category

The issue
A city needed assistance in analyzing their population and projecting the impact of several potential interventions to reduce  diabetes-related healthcare costs.

Our solution
Using Behavior Predictor, PwC took a three step approach: 1) Analyzed city data and identified population at top 15% risk for diabetes; 2) Discovered 3 segments within top risk tier, with distinct types of motivations and behaviors and 3) Identified most effective intervention levers for each distinct, high-risk segment.

Our impact
Through our work, we were able to project the health and financial impact of several community-level interventions and identified upwards of $40M in possible cost savings.

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Uncovering treatment behaviors, motivators, and preferences

The issue
A children’s hospital wanted to transform their patient experience, but needed insight into their key patient segments in order to drive impact.

Our solution
Using our machine learning tool Behavior Predictor, we developed a synthetic population of parents of children with key conditions treatable by the hospital. We clustered these populations to develop personas, which uncovered behaviors, motivators, and preferences for 3 key patient parent segments that each create a different level of profitability for the hospital.

Our impact
By coupling insight into social barriers, payer mix, healthcare utilization patterns and preferences, and behavioral motivators of each segment with financial and health outcome impact analysis, the hospital was able to prioritize top tactics that improve their patient experience in the long term.

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Contact us

Karen Young

US Health Industries Leader, PwC US

Vaughn Kauffman

Principal, Health Industries, PwC US

Craig Gooch

Principal, PwC US

Paul D'Alessandro

Principal, Health Industries, PwC US

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