For discussion

From Process Fixes to Patient-Centred Future Care Models Powered by AI

Outside-in transformation. AI as care-model enabler.

01

The Problem Today

Inside-out perspectives dominate

  • Improve registration workflow.
  • Streamline discharge and reduce counter waiting time.
  • Automate call centre FAQs and appointment reminders.
  • Reduce duplication between teams within existing operating frames.
  • Most ideas improve the current system rather than reframe care experience.

02

Why This Engagement

To move from incremental to transformation

  • Previous workshops have focused on departmental pain points and workflow optimisation.
  • This facilitation engagement targets future care models and pathways across hospital, community, and virtual settings powered by AI.
  • The intent is to imagine new patient-centric care models, not just better existing steps.
  • Outputs will be used to seed a repeatable methodology and scaling approach.

03

The Ask

Co-develop a scalable process transformation methodology

  • Methodology to move away from siloed process improvement and towards patient-centred transformation.
  • Start with pathology, radiology, and customer service as pathfinder areas.
  • Imagine what an AI-enabled Digital AMC should feel like across the full care journey.
  • Develop practical artefacts that support broader organisational scaling.

04

What Needs To Change

Five directional shifts for transformation

Starting Point

Current pain points → future care ambition

Perspective

Inside-out → outside-in

Unit of Analysis

Department efficiency → patient journey

Conversation Frame

Improve registration? → What should care feel like end-to-end?

Role of AI

Use cases → enabler for new care models

Outcome

Workshops only → scalable methodology

05

Futures Lab Approach

Identify implementable future care models

The process grounds teams in today's patient journey, stretches thinking through probable/plausible/preferable futures, converts selected concepts into tangible Protocasts for live simulation and scaled implementation.

Step 1

Patient Journey

Step 2

Futuring

Step 3

Protocast

Step 4

Showcase

Step 5

Transfer

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Futuring Logic

Progress from probable to preferable futures

Probable

Near-term ideas: routing, pre-registration, reminders, scheduling, virtual consults.

Plausible

Challenge assumptions: registration disappears, diagnostics before consult, proactive concierge models.

Preferable

Empower with a sense of agency: select and shape desirable futures aligned to Digital AMC ambition.

Decision Rule

Not every plausible future should be pursued, patient trust and human oversight remain critical.

07

Project Phases

Phase structure and activities by stage

Phase 1 · Workshops

  • Pre-workshop prep: journey maps, pain points, feedback data, current AI initiatives.
  • Workshop 1: ground current journey, identify opportunities, generate probable and plausible futures.
  • Workshop 2: select preferable futures, protocast, and prepare showcase concepts.

Phase 2 · Showcase

  • Teams prepare and present patient problem, preferable future, protocast, AI/human roles, and backcast map.
  • Apply evaluation matrix: patient value, strategic fit, AI relevance, feasibility, safety, scalability.
  • Prioritise concepts and define candidate starter projects with recommended next steps.

Phase 3 · Transfer Toolkit

  • Deliver reusable toolkit: method overview, templates, facilitation flow, and pitfalls guidance.
  • Include light facilitator transfer so SH teams can run future journey transformation sessions.

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Phase 1 · Workshop 1

Welcome to the Future

  • Map end-to-end journey from the patient's point of view.
  • Identify friction points: confusion, waiting, repeat information, avoidable visits.
  • Generate probable future concepts as a warm-up.
  • Challenge current assumptions with bold "what if" prompts to raise the stakes.
  • Form plausible AI-enabled future care concepts with cross-function integration.

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Phase 1 · Workshop 2

Creating the Future

  • Move from plausible to preferable futures using explicit desirability and trust criteria.
  • Create concept prototypes: service/journey, AI-human role split, safeguards.
  • Backcast to 5-year, 3-year, 18-month, and immediate starter actions.
  • Prepare showcase-ready concept packages for decision and prioritisation.

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Phase 2 · Showcase

Evaluate concepts with a shared decision matrix

Patient Value

Does this materially improve patient/caregiver experience?

Strategic Fit

Does this support Digital AMC vision?

AI Relevance

Is AI meaningfully enabling a better care model?

Care Model Shift

Does this go beyond improving current process steps?

Safety & Trust

Are escalation, safeguards, and human oversight clear?

Scalability

Can this extend beyond one journey or department?

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Phase 3 · Toolkit

Package methods and artefacts for repeatable scaling

  • Consolidate workshop materials, canvases, and facilitation guides.
  • Document method flow, decision points, and common pitfalls.
  • Include light train-the-facilitator transfer for selected team.
  • Enable future journey redesigns beyond initial pathfinder domains.

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Phase 1-3

Outputs from discovery to action

Future Concepts

Probable, plausible, and preferable care model options.

Protocasts

Visible concept artefacts with AI-human operating logic.

Backcast Maps

Capability, dependency, governance, and starter project pathways.

Showcase Pack

Evaluation-ready concept narratives and prioritisation inputs.

Toolkit

Reusable method kit for broader SH transformation journeys.

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Engagement Outcomes

What this unlocks for us

  • A clearer outside-in narrative for AI-enabled care transformation.
  • Stronger cross-functional alignment around patient-centred priorities.
  • Prioritised future concepts with safety and trust considerations built in.
  • A practical path from workshop ideas to scalable pilots.
  • Institutional learning assets for future domains and journeys.

14

Next Steps

Refine and confirm the scope

  • Align on priority patient journeys.
  • Define clear boundaries for each scenario with MSFT technology.
  • Confirm participant mix across clinical, operational, and service functions.
  • Confirm availability of pre-work evidence (journey maps, pain points, feedback, current initiatives).
  • Tie up contractual or procurement matters.

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