Public Health¶

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Bias and Data Quality ¶

Ethics: Transparency & Accountability ¶

Standardization¶

AI-Powered Protocol Standardization in Public Health¶

Standardization of protocols and controls is critical for ensuring consistent, high-quality public health practices across different organizations and regions. AI can significantly enhance this process while maintaining human oversight and expertise.

Using AI for Protocol Standardization¶

1. Protocol Analysis and Harmonization

AI can analyze multiple existing protocols to identify commonalities, differences, and best practices:

Example Prompt for Protocol Analysis:
"Analyze these 5 different COVID-19 testing protocols from various health departments. 
Identify:
- Common required steps across all protocols
- Key differences in methodology
- Best practices that appear in multiple protocols
- Potential gaps or missing elements
- Recommendations for a standardized protocol"

2. Natural Language Processing for Protocol Extraction

AI can extract structured information from unstructured protocol documents:

Convert narrative guidelines into step-by-step procedures
Identify required equipment, materials, and personnel
Extract critical values, thresholds, and decision points
Create standardized terminology mappings

3. Automated Protocol Validation

AI systems can check protocols for: - Completeness (all necessary steps included) - Consistency (no contradictory instructions) - Compliance with regulations and standards - Evidence-based practice alignment

Human-in-the-Loop (HITL) Approaches¶

Human-in-the-loop systems combine AI efficiency with human expertise to improve accuracy and reliability:

1. Review and Validation Workflow

AI Draft → Human Expert Review → AI Revision → Expert Approval → Implementation

2. Key HITL Components:

Expert Annotation: Subject matter experts review and annotate AI-generated protocols
Iterative Refinement: AI learns from expert corrections to improve future outputs
Decision Points: Critical decisions require human approval before proceeding
Quality Assurance: Random sampling and human verification of AI outputs

3. Practical Implementation Example:

# Pseudo-code for HITL protocol standardization
def standardize_protocol_with_human_review(raw_protocols):
    # Step 1: AI initial analysis
    ai_draft = ai_analyze_protocols(raw_protocols)

    # Step 2: Human expert review
    expert_feedback = request_expert_review(ai_draft)

    # Step 3: AI incorporates feedback
    revised_protocol = ai_revise_with_feedback(ai_draft, expert_feedback)

    # Step 4: Final human approval
    if expert_approves(revised_protocol):
        return finalized_protocol
    else:
        return iterate_revision_process()

Building Robust AI Infrastructure¶

A robust AI infrastructure for public health protocol standardization requires:

1. Technical Architecture Components:

Data Management Layer
Secure storage for sensitive health data
Version control for protocol iterations
Audit trails for all changes
HIPAA-compliant data handling
AI Processing Layer
Natural language processing models
Machine learning pipelines
Real-time validation engines
Integration APIs for existing systems
Human Interface Layer
Intuitive review interfaces
Collaborative annotation tools
Decision tracking dashboards
Mobile access for field workers

2. Governance Framework:

graph TD
    A[Data Governance] --> B[AI Model Governance]
    B --> C[Process Governance]
    C --> D[Quality Assurance]
    D --> E[Continuous Improvement]
    E --> A

3. Key Infrastructure Requirements:

Scalability: Handle increasing protocol volumes and complexity
Interoperability: Connect with existing health information systems
Security: Protect sensitive health information
Reliability: 99.9% uptime for critical health operations
Traceability: Complete audit trails for regulatory compliance

4. Implementation Considerations:

Change Management: Training staff on new AI-assisted workflows
Ethical Guidelines: Ensuring AI decisions are fair and unbiased
Regulatory Compliance: Meeting local and federal health regulations
Performance Monitoring: Tracking AI accuracy and human satisfaction
Continuous Learning: Regular model updates based on new evidence

Real-World Application Example¶

Standardizing Contact Tracing Protocols Across States:

Input: 50 different state contact tracing protocols
AI Analysis: Identifies core components, variations, and best practices
Human Review: Epidemiologists review AI recommendations
Standardized Output: Unified protocol with state-specific adaptations
Implementation: Rolled out with training and monitoring
Feedback Loop: Continuous improvement based on field experience

Best Practices for Implementation¶

Start Small: Pilot with non-critical protocols first
Maintain Transparency: Document AI decision-making processes
Preserve Expertise: AI augments, not replaces, human judgment
Regular Audits: Systematic review of AI performance
Stakeholder Engagement: Include all users in design and testing
Continuous Training: Keep both AI models and humans updated

Human-in-the-loop approaches that improve accuracy and reliability, combined with robust AI infrastructure, allow for more seamless integration into public health practices while maintaining the critical human oversight necessary for healthcare decision-making.

Case Studies end Examples¶

Awesome Lists¶

Awesome Healthcare

Awesome Healthcare Datasets

Diagnostics¶

AI has great promise for enhancing diagnostic capabilities, but have exhibited biases on race and ethnicity. There are calls to address these biases in big data and AI for health care by taking an open science approach

AI-enabled pulse oximeters overstate blood oxygen saturation in individuals with darker skin, exacerbating racial bias
AI techniques for detecting skin cancer have lower accuracy on dark skin.

Disease Surveillance & Prediction¶

AI-powered predictions change the landscape of population-level public health surveillance.

Machine learning (predictive AI) has been used for many years to analyze data and make predictions around disease spread and outbreak detection.

AI-systems can quickly analyze electronic health records (EHRs), social media platforms, online search queries, environmental data, genomic sequences, wearable devices,

Application Area	AI Technique(s) Used	Data Sources Leveraged	Specific Examples/Platforms	Key Outcomes/Impacts
Early Outbreak Detection	Machine Learning, NLP	Social Media, News Reports, Airline Travel Data, Official Health Reports	BlueDot, HealthMap	Early warning for COVID-19, Zika; faster public health response
Real-Time Monitoring	Machine Learning	EHRs, Lab Reports, Public Health Data	Various research models, hospital surveillance systems	Continuous tracking of disease spread and intensity
Epidemic Forecasting	Machine Learning, Deep Learning	Historical Disease Data, Climate Patterns, Population Mobility, Search Queries	Google Flu Trends (deprecated), CDC FluSight, Dengue/Influenza prediction models	Improved prediction accuracy for flu seasons, dengue outbreaks; better preparedness
Identifying High-Risk Populations	Machine Learning	Demographic, Socioeconomic, Health Data	Models for predicting vulnerability to specific diseases or severe outcomes	Enables targeted interventions and resource allocation to most vulnerable groups
Pathogen Genomic Surveillance	AI Algorithms, Machine Learning	Genomic Sequences of Pathogens	Systems for analyzing viral/bacterial genomes	Rapid detection of new variants (e.g., SARS-CoV-2), understanding transmissibility/virulence changes
Syndromic Surveillance (Unconv.)	NLP, Machine Learning	Online Search Queries (symptoms), Social Media Posts (symptom mentions)	Google Flu Trends, models analyzing Twitter/Facebook data	Early detection of community transmission before clinical reporting
Antimicrobial Resistance (AMR) Tracking	Predictive Analytics, ML	Clinical Data, Epidemiological Data, Lab Results	AI-driven AMR surveillance systems	Improved detection of AMR trends, rapid pathogen ID and resistance profiling, guidance for stewardship
Vector-Borne Disease Prediction	Machine Learning	Environmental Data (temp, humidity, rainfall), Satellite Imagery, Historical Case Data	AI models for Malaria, Dengue, Zika prediction	Prediction of high-risk areas for outbreaks, enabling targeted vector control measures
Resource Allocation Prediction	Machine Learning	Outbreak Data, Hospital Capacity Data, Supply Chain Information	Models forecasting demand for medical supplies, beds, personnel	Optimization of resource deployment during public health emergencies
Misinformation Monitoring	NLP	Social Media Content, Online News	AI tools tracking health-related misinformation	Identification of false narratives that could impede public health responses, enabling counter-messaging

Resource Allocation¶

Perhaps the most ethically fraught application of AI in health care is on resource allocation. Decisions made by algorithms for high-risk care have exhibited racial biases.

Training data based on historical spending for health care on black vs white patients resulted in an algorithm systematically biased toward spending on more on white patients than on black patients, resulting in a perpetuation and exacerbation of the health disparity.

Clinical Decision Support¶

Google's TxGemma, a collection of language models aimed at helping pharmaceutical companies with drug discovery.

TxGemma understands and predicts the properties of therapeutic entities — throughout the entire discovery process, from identifying promising targets to helping predict clinical trial outcomes.

https://deepmind.google/models/gemma/txgemma/

Google's Articulate Medical Intelligence Explorer (AMIE) A research AI system for diagnostic medical reasoning and conversations

Tu et al. (2024) Towards Conversational Diagnostic AI

Mental Health¶

Unregulated AI is dangerous to the mental health of vulnerable populations. AI has been accused of contributing to individuals suicide, exacerbating distress, and cyberbullying. Unlicensed and unaccountable AI chatbots are posing as therapists.

AI therapy companies are proliferating, but there is little oversight. Evidence of AI therapy effectiveness on mental health is still forthcoming, but some early research suggest AI may become a valuable tool and help to mitigate national and rural provider shortages.