Module 6: Best Practices, Terms, and Launching Your ML Journey #

1 Clinical Utility and Output Action Pairing #

Q1: What is clinical utility and why is it important in ML? #

Clinical utility refers to the real-world usefulness of a model’s predictions:

• A model must enable action that improves outcomes.
• Predictions that can’t lead to interventions or decisions have limited utility.
• This bridges the gap between technical performance and clinical relevance.

➡️ How can we ensure predictions are actually actionable?

Q2: What is Output-Action Pairing (OAP) and how does it help? #

OAP connects model outputs to a specific, predefined action:

• Defines what should happen when a model gives a certain prediction.
• Ensures the output aligns with clinical workflows and capabilities.
• Encourages careful thought about how predictions will be used.

➡️ What are some examples of OAP in clinical practice?

Q3: What are practical examples of Output-Action Pairing? #

• Sepsis risk prediction → Early IV antibiotic administration.
• Fall risk → Increase room monitoring and physical therapy.
• Readmission risk → Social work referral or discharge planning.

Clear linkage between prediction and intervention enhances adoption and trust.

➡️ How does OAP guide model design and deployment?

Q4: How does OAP influence model development choices? #

• Guides feature selection based on actionability.
• Helps prioritize precision or recall depending on the intervention.
• Encourages stakeholder involvement early to define clinical utility goals.

2 Taking Action - Utilizing the OAP Framework #

Q1: How can the OAP framework be applied systematically? #

The OAP (Output-Action Pairing) framework provides a structured approach:

• Start with the desired clinical action or intervention.
• Work backward to determine the prediction needed to support it.
• Design the model with this action-prediction link as the anchor.

➡️ What questions help clarify a good OAP strategy?

Q2: What questions can guide effective Output-Action Pairing? #

• What clinical decision is this prediction meant to support?
• Who will take action based on the output?
• What are the consequences of false positives or negatives?
• Is there an existing workflow where this model fits?

These guide the framing, design, and evaluation of the ML tool.

➡️ Can the OAP framework prevent wasted effort or misaligned tools?

Q3: What happens when models are built without OAP thinking? #

• Outputs may be ambiguous or non-actionable.
• Teams may build models no one knows how to use.
• Integration into practice becomes difficult or ineffective.

OAP increases the likelihood of real-world impact.

➡️ How does OAP support multidisciplinary collaboration?

Q4: How does OAP promote stakeholder alignment? #

• Encourages communication between clinicians, engineers, and operational teams.
• Helps align goals, expectations, and implementation details.
• Everyone shares a clear understanding of what the model is for and how it will be used.

3 Building Multidisciplinary Teams for Clinical Machine Learning #

Q1: Why are multidisciplinary teams essential in clinical ML projects? #

Healthcare ML requires collaboration across domains:

• Combines technical expertise with clinical knowledge.
• Ensures models are grounded in real-world workflows.
• Increases likelihood of successful design, deployment, and adoption.

➡️ What roles are typically involved in such teams?

Q2: Who are the key stakeholders in a clinical ML team? #

• Clinicians: define problems, validate utility, assess safety.
• Data scientists/engineers: model design, feature extraction, validation.
• IT and informatics staff: EHR integration, data access.
• Administrators and ethics leaders: compliance, governance, resourcing.

Diverse perspectives help balance performance with feasibility and ethics.

➡️ How do team dynamics influence project success?

Q3: What practices foster effective collaboration? #

• Shared language and goals: use tools like OAP to define objectives.
• Iterative feedback loops with clinicians.
• Respect for domain boundaries and active listening.

Successful teams recognize that technical and clinical inputs are equally critical.

➡️ What challenges can arise in interdisciplinary settings?

Q4: What are common barriers and how can they be addressed? #

• Misaligned incentives or timelines.
• Communication breakdowns or unclear roles.
• Resistance to change or model integration.

Solution: Foster trust, transparency, and frequent engagement across disciplines.

4 Governance, Ethics, and Best Practices #

Q1: Why is governance important in clinical machine learning? #

Governance ensures ML tools are:

• Safe, fair, and transparent.
• Aligned with legal and institutional standards.
• Routinely monitored and updated.

It defines who is accountable for model design, deployment, and oversight.

➡️ What are key components of ethical ML in healthcare?

Q2: What ethical principles guide responsible ML in medicine? #

• Fairness: equitable performance across patient groups.
• Transparency: clear communication of model limitations and risks.
• Accountability: defined roles for decision-making and error handling.
• Beneficence: focus on patient well-being and do-no-harm principles.

➡️ How do we institutionalize these principles?

Q3: What governance practices help enforce ethical use of ML? #

• Establish ML oversight committees with clinical and technical members.
• Create model review boards for performance and fairness evaluations.
• Define escalation plans for failures or unexpected behavior.

Governance should be proactive, not reactive.

➡️ What practical best practices support these efforts?

Q4: What are some operational best practices in clinical ML? #

• Regular audits and performance monitoring.
• Document model versioning, data lineage, and deployment status.
• Ensure interdisciplinary sign-off before going live.
• Build models with real-world constraints and fail-safes in mind.

5 On Being Human in the Era of Clinical Machine Learning #

Q1: What role do humans continue to play in clinical ML systems? #

Even with advanced ML, humans remain central:

• Clinicians interpret outputs in nuanced, value-laden contexts.
• Patients bring individual preferences and lived experiences.
• Human oversight is essential for ethical and compassionate care.

➡️ Why might fully automated decisions be problematic in healthcare?

Q2: What are risks of excessive automation in clinical ML? #

• Models may lack empathy or context-specific judgment.
• Overreliance can lead to de-skilling or clinician disengagement.
• Errors may go unchallenged if clinicians defer too heavily to automation.

Human clinicians provide interpretive judgment and ensure care remains individualized.

➡️ How can we design systems that support, not replace, human judgment?

Q3: How do we build ML systems that augment rather than replace clinicians? #

• Keep clinicians “in the loop”—with tools to override or question model outputs.
• Design interfaces for transparency and explanation, not just prediction.
• Support human strengths: empathy, narrative understanding, ethical judgment.

➡️ What values should guide human-machine collaboration in healthcare?

Q4: What values should ML practitioners center in their design? #

• Respect for human dignity and individual autonomy.
• Empowerment, not displacement, of healthcare professionals.
• Continuous attention to how technology shapes behavior and trust.

6 Death by GPS and Other Lessons of Automation Bias #

Q1: What is automation bias and why is it dangerous in healthcare? #

Automation bias is the tendency to:

• Overtrust machine-generated suggestions, even when flawed.
• Ignore or discount human judgment in favor of algorithmic outputs.
• Lead to harmful or fatal errors, especially in high-stakes domains.

➡️ What are real-world examples of automation bias?

Q2: What lessons do we learn from non-healthcare automation failures? #

Example: “Death by GPS”—drivers blindly following GPS into unsafe areas.

• Similar dynamics occur in medicine when clinicians follow flawed model predictions.
• Automation can make errors seem more trustworthy due to perceived objectivity.

➡️ How can we design systems to guard against this?

Q3: How can ML systems reduce risk of automation bias? #

• Provide confidence scores, explanations, and alternative scenarios.
• Train users to critically evaluate model outputs.
• Design alerts and interfaces that encourage reflective judgment, not blind acceptance.

➡️ What role do institutions and governance play?

Q4: How should organizations manage automation risks? #

• Regular audits for model drift and edge-case failures.
• Create feedback loops so users can flag concerning outputs.
• Promote a culture where questioning automation is encouraged.