HEALTHCARE AND HOSPITAL SYSTEMS
Digital Health Platforms · Clinical UX · SaMD-adjacent AI Workflows
End-to-end ownership of product vision, roadmap, operating model, and lifecycle execution for healthcare and hospital platforms, with accountability for adoption, auditability, safety, and production readiness in regulated clinical environments.
PRODUCTS DELIVERED:
Clinical and member experience surfaces with operational parity:
Defined end-to-end experience strategy across eligibility, onboarding, documentation capture, messaging, service, support, and longitudinal engagement. Paired with role-based internal tooling for care coordination, operations, administration, and revenue cycle adjacent workflows; so adoption is supported by real operating context. Anchored experience standards in clinical task models
(intake → assess → act → handoff), role-based permissioning, and explicit exception paths to prevent frontline teams from resorting to workarounds that introduce safety and compliance risk.
Consent-aware identity and segmentation platform (CDP grade, healthcare native):
Established a governed member profile and decisioning layer spanning identity resolution, preference, consent lifecycle management, segmentation, and propensity logic. Enabled omnichannel activation while enforcing PHI boundaries, purpose limitation, and allowed use constraints. Set consent-aware personalization standards including audience membership, eligibility flags, channel preferences, and suppression logic. To ensure targeting and outreach remain deterministic, explainable, and audit defensible.
Workflow platform capabilities exposed as stable interfaces:
Established core operational workflows as reusable platform primitives covering intake and triage, document handling, case status, state management, notifications, and orchestration. Defined API-first integration standards and repeatable delivery patterns so downstream teams could build safely without reinventing workflow logic. Standardized interface contracts covering idempotent operations, deterministic state management, and replay-safe event handling, ensuring integrations behave predictably under load, during backfills and retries, and through partial system outages.
SaMD adjacent AI and GenAI experiences with provenance UX:
Set experience and governance standards for AI assisted capabilities including summarization, documentation support, classification, triage, and decision support. Established the use of provenance and confidence UI, human in the loop escalation, and safe failure defaults as baseline design requirements. These standards increased clinical and operational throughput without introducing hidden risk. Institutionalized decision transparency patterns that clearly surface what information was used, when it was used, why it mattered, and what may be missing, alongside operational guardrails such as escalation, deferral, and clarification paths so clinicians are never forced into blind trust.
Clinical data made usable in product experiences (multi-modal, heterogeneous):
Defined product and workflow standards to operationalize healthcare data across EHR and EMR transactional records, including encounters, orders, medications, labs, and vitals. Extended these standards to downstream clinical, financial, and operational data, including claims and revenue cycle signals, eligibility data, FHIR and HL7 integrations, imaging workflows with DICOM-related metadata, lab and observation streams, and unstructured clinical text such as notes, documents, and patient messages. Established context-aware presentation norms that surfaced source system, timestamp, completeness, and recency, with explicit handling of conflicting or mismatched data and consent-aware access patterns. This ensured clinicians and operations teams could act confidently without misinterpreting stale, partial, or contradictory signals.
Developer and platform enablement treated as product:
Established developer and partner enablement as a first-class product capability. Defined onboarding experiences for SDKs and APIs supported by reference implementations, integration playbooks, sandbox, test harness environments, and clear golden path templates. Productized developer experience standards through versioned examples, contract testing, environment templates, integration readiness checklists, and reducing time to first success and preventing fragile one-off integrations.
ENGINEERING AND GOVERNANCE
Design controls translated into delivery mechanics (not paperwork):
Established a regulated-grade delivery system where audit evidence is generated as a byproduct of normal product development. Moving from PRD → risk assessment → traceability → verification and validation ready evidence. Set this system to align with SaMD expectations including ISO 13485, IEC 62304, and ISO 14971 patterns where applicable. This approach prevents iteration from becoming bureaucratic and institutionalizes a definition of done that includes risk controls, test artifacts, operational readiness, and ensuring compliance is built in rather than applied after release.
Risk and safety embedded into workflow architecture and UX:
Set enterprise safety controls across workflow architecture and user experience, including role-based access, least-privilege enforcement, PHI-safe logging, immutable audit trails for high-stakes actions, controlled change gates, and explicit exception taxonomies. Established decision-transparency experience standards so users can see what the system did, why it acted, and what to do when confidence is low or signals are incomplete. These patterns reduced silent failure modes and prevented unsafe automation as systems scaled.
AI governance that survives production:
Established production-grade AI governance spanning model acceptance criteria, including performance, calibration, latency, drift signals, and operational thresholds. Defined monitoring hooks and incident playbooks aligned to NIST AI RMF govern, measure, and manage practices. For GenAI capabilities, set standards for prompt and version controls, evaluation harnesses, guarded outputs, and escalation paths appropriate for regulated clinical contexts. This ensured LLM behavior was treated as a monitored production dependency rather than a standalone feature.
Data governance expressed as product behavior:
Defined data contracts and metadata expectations including provenance, freshness, allowed use, and retention, and embedded them directly into product workflows and experience controls. Ensured EHR, FHIR, claims, imaging, and text signals are presented with appropriate context and safe defaults. When data is missing, delayed, or contradictory, integrity is evaluated through completeness and latency rules, reconciliation workflows, and exception funnels. This replaces abstract “data quality” statements with operationally enforceable standards.
Platform standards enforced through interface contracts:
Established platform-wide standards for API specifications, event schemas, workflow state machines, analytics instrumentation, and operational SLOs. Institutionalized these standards as non-negotiable product requirements across platform ecosystem teams. Defined, at the system level, what “integration works” means, including deterministic states, replay-safe events where applicable, measurable reliability, and predictable degradation modes. This ensured experience quality remains stable under scale, partial outages, and demand spikes.
Privacy by design across journeys:
Established HIPAA-grade operating standards across customer and internal journeys, including consent-aware flows, minimum-necessary UX patterns, secure access behaviors, and governance for activation and segmentation, ensuring growth does not create compliance debt. Defined role-appropriate data exposure and PHI-safe telemetry practices to ensure monitoring and analytics remain effective without creating compliance debt or leaking sensitive information.
PRODUCT MANAGEMENT & ENABLEMENT
0→1 incubation and product definition:
Established a structured discovery and incubation model across clinical operations, support, compliance, and engineering. Created clear, investable product increments from ambiguous problem spaces. Defined service blueprints, workflow maps, prototype validation approaches, PRDs with measurable success criteria, and architecture-aligned roadmaps. Converted clinical and operational requirements into capability maps and sequencing logic that prioritized durable platform primitives over brittle, UI-first solutions.
1→n scaling and NPI readiness:
Defined and governed repeatable launch and scale patterns across products and programs. Established launch playbooks, enablement kits, training and communications models, support readiness standards, rollout gates, and adoption instrumentation. Standardized deployment progression from pilot to controlled expansion to broad release. Set explicit readiness checks, monitoring expectations, and escalation ownership so releases scale across roles, sites, and partner ecosystems without creating support cliffs or compliance gaps.
Product operating model and portfolio governance:
Established clear intake and prioritization mechanisms, planning cadences, decision forums, KPI trees, and ProductOps rhythms. Aligned roadmap sequencing to measurable outcomes including adoption quality, task completion, time to resolution, exception rates, and operational load. Made portfolio trade-offs explicit; using risk and impact frameworks, ensuring high-stakes workflows are optimized for safety, reliability, and outcomes rather than velocity alone.
Cross-functional execution at platform scale:
Set clear ownership boundaries across product, UX, research, engineering, and compliance. Established execution governance through defined RACI models, architecture review rituals, release gating, and change management. Institutionalized alignment rhythms that keep clinical safety, operational realities, and platform reliability coherent as scope, teams, and regulatory complexity expand.
Long-Term Digital Health Platform Roadmap:
Defined a multi-year roadmap for clinical and patient-facing platforms, sequencing experience modernization, interoperability, and AI enablement while maintaining regulatory compliance, safety, and operational sustainability.
OUTCOMES:
Reliability & scale: 99.9% platform uptime · 99.98% API uptime · release cycles up to +40% faster via repeatable platform standards, contract-first interfaces, and controlled changes.
Adoption & enablement: Adoption timelines accelerated by up to 24 months · developer onboarding satisfaction ~90% · developer productivity +45% via SDK + golden-path enablement + reference integrations.
Operational efficiency: Workflow effort −20% via self-service patterns, deterministic workflow state machines, and reduced manual exception handling; measurable reduction in support burden through better state visibility, safe escalation, and fewer “unknown status” cases.