- Lead, coach, and develop a distributed support team, setting clear expectations for ticket quality, investigative rigor, client communication, prioritization, escalation discipline, and follow-through
- Define role clarity across front-line support, technical escalation, cross-functional support paths, and leadership coverage
- Personally engage in high-impact escalations involving APIs, HL7/FHIR/C-CDA, SFTP, patient search, query behavior, lab workflows, EHR integrations, and healthcare data exchange
- Help the team distinguish between break-fix issues, configuration problems, implementation gaps, product defects, training needs, enhancement requests, and scope or commercial questions
- Translate complex technical findings into clear, calm, client-ready updates for technical and non-technical stakeholders
- Define what Support owns and when work should route to Implementation, Product, Engineering, Solution Architecture, or Client Experience
- Create practical intake rules, handoff gates, escalation paths, severity definitions, and prioritization frameworks
- Build a business-impact-based triage model so the team prioritizes strategic clients and high-severity issues without relying on first-in-first-out or “loudest client wins” logic
- Partner with Implementation, Solution Architecture, and Client Experience to clarify warranty boundaries, post-go-live ownership, client health context, and escalation risk
- Build dashboards and operating rhythms to track ticket volume, backlog age, severity mix, response and resolution times, escalations, reopen rates, and client-level patterns
- Proactively monitor transaction volumes, shareback performance, error trends, latency, and other operational signals that may indicate client risk before a ticket escalates
- Use analytics to identify repeat issues, capacity constraints, misrouted work, and opportunities to automate, deflect, reroute, or address root causes upstream
- Lead practical AI adoption across Support, focused on triage, context retrieval, documentation, draft response support, escalation detection, and repeat-issue deflection
- Build AI-ready knowledge infrastructure: current runbooks, reusable troubleshooting workflows, structured client context, Markdown instruction files, and quality-reviewed knowledge bases
- Establish human-in-the-loop review standards so AI improves accuracy, consistency, and speed without creating client or compliance risk
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