Case-study

Healthcare AI enablement: discovery run by the platform

SLKone deployed Bridge into a multi-site specialty provider to run the AI-readiness discovery across eleven functions in under three weeks, producing a typed, prioritized opportunity map anchored on revenue-cycle economics.

This case study is presented as a sanitized pattern. The client name and identifying details are withheld pending public sign-off.

Challenge

A multi-site specialty provider needed to translate a broad AI-readiness question into a prioritized, board-ready roadmap. The board had set a clear mandate on revenue-cycle economics, and leadership wanted both an opportunity map and a governance perimeter for safe adoption.

The constraints were familiar to multi-site provider groups:

  • Clinical, finance, revenue-cycle, operations, IT, and data each had a view of the problem, but no shared structure for comparing options.
  • Existing technology spending across AI and software was not catalogued in one place.
  • Discovery needed to be fast enough to inform the next planning cycle, without the team spending months in interviews.

Approach

We deployed Bridge as a delivery workspace for the engagement and used the platform to run the discovery itself.

  • A Bridge meeting agent sat in the discovery interviews and captured them live, then converted each transcript into a structured knowledgebase record with a role overview, an AI and software inventory, and a typed list of pain points and opportunities.
  • An organization-wide AI-readiness survey was co-authored with the client and distributed alongside a company-wide announcement.
  • Eleven functional interviews were completed in approximately three weeks, spanning clinical (physician and advanced-practice-provider voices), finance and revenue cycle, growth, patient access and contact center, operations, IT and infrastructure, and data.
  • Vendor intelligence, including an EHR vendor’s AI product roadmap briefing, was captured as structured records.
  • The roadmap and a guardrails, policy, and governance scope were framed in parallel, defining both the opportunity map and the rules for safe adoption.

Impact

  • A prioritized AI opportunity map covering clinical documentation, revenue cycle, contact center, and adjacent functions.
  • Specific operator-level insights, including the fit gap between ambient scribe tools designed around physicians and the daily volume carried by advanced-practice providers, and a granular read on prior-authorization failure modes.
  • A revenue-cycle priority anchored on the CFO’s board mandate, with eligibility verification and prior authorization broken down by failure mode and expected return.
  • A current contact-center automation baseline, including a measured containment rate of approximately 41 to 43 percent on existing-patient scheduling and medication calls in the first month after deployment.
  • A reusable knowledge graph owned by the client tenant, so the next phase of work starts from a structured base.

The pattern shows the SLKone delivery model in compressed form: Bridge runs the work, an embedded team supports it, and the output is a typed, queryable knowledge base the client owns.