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Selected Work

The work begins where health strategy most often breaks down.

Between a credible idea and consistent use at scale.

I have spent more than 20 years leading health initiatives across military health, employer health, digital platforms, senior living, consumer health, and preventive wellness. The environments differ. The operating challenge repeats: connect evidence, human behavior, product, data, and execution closely enough to produce a measurable result.

Each case below is viewed through two tests:

The evidence test

What had to be scientifically, analytically, or clinically sound?

The real-life test

What had to be true for people to use it, teams to deliver it, and the enterprise to sustain it?

Case 01

BUMED

Making prevention operational at national scale

Role
VP of Population Health
Scope
$25M national initiative | 400K beneficiaries

The enterprise problem

Prevention at national scale requires more than a set of wellness programs. Clinical medicine, population analytics, policy, care access, behavioral intervention, and executive accountability must operate as one system.

The mandate was to identify emerging health risk earlier, direct the right preventive response, and show whether the work improved both health and cost performance across a large, distributed population.

Why a conventional approach was insufficient

Aggregate risk data can describe a population without changing what happens next. Education can raise awareness without reaching the people most likely to benefit. Individual interventions can perform well while the larger system remains fragmented.

The work needed a clear path from risk identification to intervention, delivery, measurement, and leadership action.

What I led

I directed national preventive health operations across clinical medicine, behavioral data, analytics, policy, and care delivery.

The work included:

  • Building predictive approaches for earlier identification of metabolic and cardiovascular risk
  • Connecting risk profiles to targeted, evidence-based lifestyle interventions
  • Expanding preventive care access for service members and families
  • Aligning clinical, policy, analytics, and operational stakeholders
  • Establishing performance accountability for intervention effectiveness, health outcomes, and cost
  • Translating results for senior military and healthcare leaders to support policy and investment decisions
Risk identification
Intervention
Delivery
Measurement
Leadership action

The analytical model had to identify meaningful emerging risk, use multi-year population data appropriately, and connect the result to an intervention with a plausible pathway to better health.

Measurement had to move beyond participation and show whether targeted prevention changed health performance and cost.

The model also had to function across a complex national care environment. That required clear protocols, access pathways, policy support, cross-agency alignment, operating ownership, and reporting leaders could use.

Prediction mattered only if it changed a real decision for a real population.

What changed

The $25M initiative served 400K beneficiaries. Program reporting documented:

  • 30% improvement in health outcomes

  • 25% reduction in costs

What the work demonstrates

I can lead a health system in which science, policy, operations, and economics must move together. The result was not a single intervention. It was an operating model for predictive prevention at population scale.

Case 02

Premise Health

Turning digital engagement into retention and value

Role
VP of Wellbeing, Strategy & Integration
Context
Enterprise digital health transformation

The enterprise problem

An acquired wellness platform needed to become part of a much larger clinical and enterprise health model. The opportunity was not simply to integrate technology. It was to connect digital access, personalized engagement, care delivery, outcomes analytics, and client value.

Why a conventional approach was insufficient

A platform cannot create its full value when it remains a separate engagement layer beside care. Nor can digital activity be treated as proof of better health.

The product needed to fit clinical workflows, improve access, direct attention toward meaningful risk, and give operational and client teams evidence they could act on.

What I led

I led the integration of a SaaS wellness platform into Premise Health's clinical model, with strategic ownership across product, UX, clinical design, operations, and digital intervention.

The work included:

  • Introducing machine-learning precision engagement to identify populations at risk and guide outreach
  • Embedding predictive health-risk intervention into personalization and outcomes strategy
  • Integrating Epic APIs to strengthen the digital front door and improve access
  • Connecting product and UX decisions to clinical workflows and provider experience
  • Publishing internal analyses linking digital engagement with biometric improvement and reduced stress
  • Aligning preventive care, outcomes analytics, client strategy, and growth priorities
  • Translating results into value narratives that supported enterprise relationships, retention, and expansion conversations
Digital access
Personalized engagement
Care delivery
Outcomes analytics
Client value

Engagement needed to be linked to a defined health or behavioral result, not treated as a stand-alone success measure. Predictive outreach needed a relevant risk target, and product decisions needed to remain grounded in clinical and behavioral evidence.

The experience had to work for members, providers, operators, product teams, and clients. That required integration across digital entry points, care workflows, technology, service delivery, and performance reporting.

The platform became more valuable when it could help the right person reach the right next action and help the enterprise understand what followed.

What changed

Precision engagement and integrated delivery produced a:

50% improvement in retention

The work also established a stronger connection between digital engagement, measurable outcomes, client value, and enterprise growth conversations.

What the work demonstrates

I can integrate an acquired product into a broader care model and align technology, clinical practice, user experience, operations, analytics, and commercial priorities around a shared result.

Case 03

League

Designing AI around the behavior that drives outcomes

Role
VP Member Behavior
Context
Payer, provider, and employer health platforms

The enterprise problem

AI can make health experiences more responsive, but technical performance alone does not make personalization useful. The model needs a defined behavioral purpose, an explainable basis for action, and an experience that helps people take a meaningful next step.

Why a conventional approach was insufficient

Optimizing activity can increase clicks without improving adherence. Adding more data can make a model more complex without making the recommendation more relevant. A highly accurate prediction has limited value when users, clinicians, clients, or product teams cannot understand how it should change the experience.

The work had to connect responsible model development with behavioral science, product decisions, validation, and measurable use.

What I led

I led enterprise strategy translating behavioral science, clinical insight, and AI-powered personalization into scalable engagement systems.

The work included:

  • Partnering with product, data science, clinical, and client teams to define meaningful behavioral targets
  • Developing explainable models intended to predict health behavior and support targeted outreach
  • Connecting motivation, adherence, resilience, and engagement measures with clinical and operational data
  • Designing adaptive interventions using behavioral economics and cognitive reframing
  • Building validation into concept development and responsible model use
  • Translating technical and behavioral findings into decisions for executives, clients, and product teams
Model output
Intervention
Health behavior

The model needed a specific prediction target, a transparent validation approach, and a defensible connection between model output, intervention, and health behavior.

In internal validation, the model reached approximately:

85% predictive accuracy

Prediction had to improve what happened next. Personalization needed to account for motivation, context, and readiness, and it needed to fit product journeys across payer, provider, and employer settings.

An AI-driven behavioral intervention produced a:

22% improvement in adherence

What the work demonstrates

I can help an enterprise give AI a defined health purpose. That means asking not only whether a model predicts well, but whether the prediction improves a product decision, supports responsible action, and helps a person do something that matters.

Case 04

Senior Resource Group

Scaling a high-touch experience without losing consistency

Role
VP of Life Enrichment
Scope
32 communities | Average resident age of 85

The enterprise problem

The task was to create an evidence-based wellbeing and engagement platform that could scale across 32 luxury senior-living communities while remaining personal, relevant, and emotionally meaningful.

Scale required consistency. Adoption required local trust, identity, belonging, and experiences people genuinely wanted to join.

Why a conventional approach was insufficient

A larger calendar of activities would not create a differentiated health experience. Standardization alone could improve consistency while flattening the human qualities that made participation worthwhile.

The operating model needed to define what should remain consistent across the enterprise and where communities needed room to respond to local people and context.

What I led

I designed and scaled an evidence-based wellbeing platform that integrated hospitality, behavioral science, social connection, and health programming.

The work included:

  • Creating shared standards for program delivery, measurement, service quality, and experience
  • Designing the Zest and FreshZest platforms around movement, meaning, belonging, and participation
  • Treating social connection and purpose as meaningful elements of health, alongside movement and nutrition
  • Measuring sleep, daily movement, vitality, adoption, and participation
  • Using performance data to refine program design and operating execution
  • Aligning operations, brand, programming, and community leaders around a shared experience model
Enterprise consistency
Local meaning
Participation

The platform needed clear health and wellbeing principles, defined measures, and a credible way to learn from adoption and outcome data across communities.

The experience had to feel relevant to people with distinct histories, abilities, preferences, and social worlds. Teams needed standards they could execute without replacing human judgment with rigid scripts.

The model succeeded by pairing enterprise consistency with local meaning.

What changed

Across 32 communities, the platform achieved:

  • 90% resident adoption

  • 70% increase in participation

What the work demonstrates

I can turn behavioral insight into a repeatable enterprise experience without stripping away the human value that drives participation and return.

Additional enterprise scope

Global health strategy across five countries

ECC | Global Health & Wellness Manager

Directed an $8M global health portfolio across five countries, building measurement systems that connected participation, chronic risk, wellbeing, and cost. Participation increased 70% across a complex, distributed workforce.

Preventive health translated into experience

Sensei | VP, Preventive Medicine & Scientific Affairs

Lead the design and commercialization of integrated preventive health experiences, aligning scientific standards, behavioral design, diagnostics, operations, measurement, and guest value.

Lifestyle medicine made accessible

Sonima Wellness | Director of Sonima Wellness & Institute

Developed evidence-based health education, preventive wellness programming, curricula, and expert partnerships that translated complex science into practical public use.

The pattern across the work

I do not enter an enterprise problem from one discipline. I look for the break in the value chain.

  • Is the evidence credible?
  • Is the behavioral target clear?
  • Does the product fit the person and context?
  • Can teams deliver it consistently?
  • Can data guide the next decision?
  • Can leaders connect the result to health and enterprise value?

When those questions are answered together, health innovation can move beyond the pilot and perform at scale.

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