Q1. What outcomes should we expect from a Protea strategy engagement, and how are they measured?

A. We co-define 3-5 measurable results (e.g., improved access, reduced time-to implement, ROI on digital investments, equity impact by subgroup). We suggest leading indicators to track (milestone delivery, adoption) and lagging indicators (clinical, operational, financial, equity metrics) via an OKR dashboard and quarterly reviews.

Q2. How do you ensure a strategy actually gets executed?

A. We help translate strategy into an operating model: governance (RACI + decision rights), funded initiatives, risk register, and a “PMO-lite” cadence. Each priority has a one-page charter (problem, scope, resources, timeline bands, KPIs) and an owner accountable for benefits realization.

Digital Health & Artificial Intelligence Solutions

Q1. How do you make sure AI tools are safe, fair, and compliant in real clinical settings?

A. We advise on running an end-to-end assurance workflow: data governance review, bias/equity testing across subgroups, human-factors validation, model cards + intended-use statements, security/privacy review, and post-deployment monitoring (drift, performance, equity). Documentation maps to internal compliance and external standards.

Q2. Should we build or buy—and how do we de-risk either path?

A. We help design a scored decision framework (strategic fit, TCO, interoperability, vendor viability, clinical risk, equity impact). For “build,” we guide you to set reference architectures and checkpoints; for “buy,” we help run vendor due diligence, sandbox evaluations, and success criteria tied to adoption and outcomes.

Care Coordination & Case Management Technology Solutions

Q1. What care coordination and case management problems do you solve first?

A. High-leverage gaps: referral leakage, SDOH needs capture, closed-loop referrals, eligibility/benefit checks, handoffs between acute/post-acute/community, and outcome visibility (utilization, readmissions, patient-reported outcomes).

Q2. How do you ensure provider teams and patients actually use the tools?

A. Co-design with front-line teams, map current vs. future workflows, streamline clicks, and embed nudges only where decisions happen. We pair go-live with role-specific training, quick-hit job aids, and an adoption scorecard (usage, completion, time-on-task, satisfaction).

Evidence Generation for AI & Digital Health Innovation

Q1. What evidence do payers, regulators, and health systems actually need?

A. A layered package: analytical validation (does it work as designed?), clinical validity/utility (does it improve decisions/outcomes?), implementation outcomes (adoption, fidelity, equity impact), and health-economic evidence (cost, budget impact). We align study endpoints to stakeholder decisions.

Q2. How do you design pragmatic, ethical evaluations without stalling operations?

A. We favor real-world designs (e.g., stepped-wedge, matched cohorts, phased rollout) with privacy-preserving data pipelines, IRB/ethics review where required, and pre-specified subgroup analyses to detect potential disparities and adverse outcomes early.