Week 7: Implementation Frameworks¶

Section 3: Application | Learning Outcome 3¶

Theme: From evaluation to action: Structured approaches to AI deployment

Core Question: "How do we govern it?"

Learning Objectives¶

By the end of this session, students will be able to:

Apply clinical AI implementation frameworks to deployment decisions
Design validation strategies appropriate to healthcare AI
Develop governance structures for AI oversight
Create readiness assessment criteria for AI adoption

Content¶

7.1 Implementation Science for Healthcare AI¶

Why Technically Sound AI Often Fails in Practice

The gap between AI development and successful deployment: - Technical performance ≠ clinical utility - Laboratory conditions ≠ real-world conditions - Algorithm success ≠ workflow success - Individual tool success ≠ system success

Implementation Frameworks

Moving from software deployment to sociotechnical systems thinking: - Technology is only one component - People, processes, and culture matter - Change is organisational, not just technical - Implementation is a process, not an event

Stakeholder Engagement

Essential stakeholders in AI implementation: - Clinical end users (multiple disciplines) - Patients and consumers - IT and informatics - Clinical governance - Executive leadership - Regulators - Vendors

Each brings legitimate interests and concerns.

Change Management Principles

Create urgency and vision
Build coalition of support
Communicate continuously
Enable action and remove barriers
Generate short-term wins
Consolidate and embed change

7.2 Clinical Validation Requirements¶

Levels of Evidence for Healthcare AI

Adapted from traditional evidence hierarchies: - Analytic validation (does it measure what it claims?) - Clinical validation (does it perform in clinical data?) - Clinical utility (does it improve outcomes?)

Local Validation: Why External Validation Isn't Enough

Published performance may not reflect local performance: - Different populations - Different workflows - Different data systems - Different clinical practices

Local validation is essential before deployment.

Prospective vs. Retrospective Evaluation

Approach	Advantages	Limitations
Retrospective	Fast, cheap, large samples	Historical data may not reflect future
Prospective	Real-world validity	Slow, expensive, smaller samples

Often need both: retrospective first, then prospective confirmation.

Silent Mode Deployment

Testing before trusting: - AI runs but outputs not shown to clinicians - Compare AI recommendations to actual decisions - Identify discordance and investigate - Build confidence before "going live"

Ongoing Validation

The work doesn't end at go-live: - Continuous monitoring of performance - Detecting drift over time - Responding to changes in population or practice - Regular recalibration or retraining

7.3 Governance Structures¶

AI Governance Committee

Composition: - Clinical leadership (multiple disciplines) - Consumer representative - IT/informatics - Clinical governance/safety - Legal/risk management - Ethics expertise - Executive sponsor

Charter: - Approval authority for AI deployment - Ongoing oversight of deployed AI - Incident review and response - Policy development

Clinical Informatics Leadership

Need for expertise in: - AI/ML technical understanding - Clinical workflow analysis - Implementation science - Data governance - Health informatics

Vendor Management

Clear accountability for performance
Access to model information (appropriate transparency)
Update and maintenance provisions
Exit clauses and data portability
Liability allocation

Incident Reporting and Learning Systems

Clear definition of AI incidents
Low-barrier reporting
Investigation processes
Feedback loops for improvement
Connection to safety and quality systems

Policy Frameworks

AI-specific policies or integrated into existing governance?
Approval and procurement policies
Use policies for clinicians
Monitoring and review policies
Incident response policies

7.4 Readiness Assessment¶

Organisational Readiness

Culture - Innovation orientation - Learning culture - Safety culture - Tolerance for change

Capability - Data infrastructure - Informatics expertise - Clinical leadership - Governance capacity

Capacity - Resources for implementation - Ongoing operational support - Training capacity

Technical Readiness

Infrastructure: computing, connectivity, integration
Data: quality, accessibility, governance
Integration: EHR, workflows, other systems
Interoperability: standards compliance

Workforce Readiness

Skills: technical literacy, AI understanding
Attitudes: openness, appropriate scepticism
Training: needs assessment, capacity to deliver

Patient and Community Readiness

Awareness of AI in healthcare
Trust in AI applications
Concerns and preferences
Engagement in governance

Aeromedical Thread¶

Implementation Challenges for Retrieval Services¶

Multi-Site Operations

Consistent deployment across bases
Training across distributed workforce
Version control and updates
Performance monitoring at scale

Variable Referring Hospital IT

Integration with diverse systems
Data availability and quality
Communication of AI outputs
Interoperability challenges

Aircraft/Vehicle System Integration

Hardware installation and certification
Power and connectivity
Display and interface design
Interference and safety considerations

Cross-Jurisdictional Governance

When operating across state boundaries: - Multiple governance frameworks - Different regulatory environments - Coordinating oversight - Incident reporting across jurisdictions

Learning Activities¶

Pre-Class Preparation¶

Implementation Case Studies
Read provided AI implementation case studies
Identify success factors and implementation challenges
Readiness Self-Assessment
Complete organisational readiness assessment tool for your service
Identify major gaps and strengths

In-Class Activities¶

Implementation Planning Workshop (Groups, 45 mins)
Groups develop deployment roadmap for a case study AI system
Include: validation, governance, training, go-live, monitoring
Present and receive peer feedback
Governance Structure Design (Groups, 25 mins)
Design governance structure for AI in a health service
Define committee composition, charter, and processes
Consider how this fits with existing governance
Readiness Gap Analysis (Pairs, 20 mins)
Review each other's readiness assessments
Identify common themes and differences
Discuss strategies to address gaps

Post-Class Activities¶

Implementation Plan Draft
Draft implementation plan section for capstone project
Include: validation approach, governance, training, monitoring

Practical Exercise 7: Local Validation Simulation (Colab)¶

Objective¶

Understand the validation requirements for deploying an AI model in a new clinical environment.

Scenario¶

A vendor offers a deterioration prediction model trained on US hospital data. Before deploying it in your Australian health service, you need to validate its performance locally.

Part A: Load the "Vendor" Model¶

import joblib
vendor_model = joblib.load('vendor_deterioration_model.pkl')

# What do we know about this model?
print("Model type:", type(vendor_model).__name__)
print("Expected features:", vendor_model.feature_names_in_)

Part B: Prepare Local Validation Data¶

local_data = pd.read_csv('australian_hospital_data.csv')

# Check compatibility
print("Local data columns:", local_data.columns.tolist())
print("Missing required features:", 
      set(vendor_model.feature_names_in_) - set(local_data.columns))

Part C: External Validation¶

local_predictions = vendor_model.predict(local_features)
local_probabilities = vendor_model.predict_proba(local_features)[:, 1]

print("=== Vendor-Reported Performance (US data) ===")
print("AUC: 0.85, Sensitivity: 0.78, Specificity: 0.82")

print("\n=== Local Validation Performance (AU data) ===")
print(f"AUC: {roc_auc_score(local_labels, local_probabilities):.3f}")

Part D: Go/No-Go Decision Framework¶

validation_report = {
    'model_name': 'Vendor Deterioration Predictor v2.1',
    'validation_date': '2024-XX-XX',
    'local_sample_size': len(local_data),
    'vendor_claimed_auc': 0.85,
    'local_validated_auc': local_auc,
    'performance_gap': 0.85 - local_auc,
    'acceptable_threshold': 0.75,
    'recommendation': 'PROCEED' if local_auc >= 0.75 else 'DO NOT PROCEED',
    'conditions': ['Requires 3-month silent running', 
                   'Subgroup monitoring required',
                   'Clinical override mandatory']
}

Deliverable¶

Completed validation report suitable for governance committee review.

Indicative Resources¶

Required Reading¶

Sendak, M.P., et al. (2020). A Path for Translation of Machine Learning Products into Healthcare Delivery. EMJ Innovations.

Session Summary¶

This week provided frameworks for implementing AI in healthcare settings:

Implementation requires attention to people, process, and culture—not just technology
Local validation is essential before deployment
Governance structures must provide effective oversight without blocking innovation
Readiness assessment helps identify gaps before implementation
Aeromedical services face additional complexity from distributed operations

Next Week: We'll examine how to integrate AI into clinical workflows and manage the human-AI relationship.