How 7 UX Research Tasks Reduce 5 High-Risk Vulnerabilities in Healthcare Agents

The Idea in Brief: Health agents are increasingly augmenting pharmaceuticals/doctors in drug discovery, diagnosis & prescription/healing pathway recommendations.

Photo by National Cancer Institute on Unsplash

12 Types of Health Agents Revolutionizing Healthcare:

• Cardiac AI Agents: HelloHeart, AliveCor, Moving Analytics, HeartVista, Cardiac Design, HKAI
• Diabetic AI Agents: Signos, Sweetch, Virta Health, Dario Health
• Cancer AI Agents: Paige.AI, COTA, CureMatch, Epic Sciences, Nirmai
• Neurodegenerative AI: Synapticure
• Musculoskeletal AI: IncludeHealth
• Lab Testing: DNANexus
• Urology: Verana Health
• Evidence Analytics: Atropos Health
• Ambient Patient Care: care.ai
• Patient Interaction: Pager Health, Redox, Hartford Health
• Radiological AI: Hyperfine, Ultromics, Aidence, Hologic, Rad AI
• Drug Discovery AI: BenchSci, Cerevet, Colossal

Why is this important? Vulnerabilities Addressed:

Healthcare agents are critical in diagnostics and treatment recommendations, making them vulnerable to 5 risks. ( 5 Risk beads in the Healthcare liability necklace )

1. Bias: Gender, racial, or ethnic biases in data.
2. Diagnostic Reliability: False positives or false negatives.
3. Explainability: Transparency in data interpretation.
4. AI Law Compliance: Adherence to HIPAA, FDA, EU AI Act.
5. Security: Protection of PII/Biometric data.

Risk Outcomes: Failures can lead to:

• Reputational Liability: Brand damage. Constant negative press coverage
• Financial Liability: Loss in sales, stock, and valuation.
• Legal Liability: Lawsuits and settlements.

Reinforcement learning from human feedback used in ChatGPT. A simple thumbs up and down can help guide understand the users' feelings about the AI agent they are using.

Mitigation via 7 UX Research Tasks:

Image/Sensor Data Bias Detectors

• Mitigates Bias and Diagnostic Reliability.
• Risk if not done: Bias issues, leading to mistrust and regulatory scrutiny.

Research Document Data Quality Assessments

• Ensures data integrity.
• Risk if not done: Poor data quality affects reliability and compliance.

Diagnostic Algorithmic Audits

• Validates algorithm performance.
• Risk if not done: High false positive/negative rates, legal challenges.

Ethical AI Threat Matrix Watchtower Design

• Monitors for ethical risks.
• Risk if not done: Ethical breaches, reputational damage.

Empathy Modeling of Agent Responses

• Enhances user interaction and trust.
• Risk if not done: Poor user experience, loss of trust, brand hit.

Human-in-the-Loop — RLHF Statistical Analysis

• Ensures ongoing improvement and reliability.
• Risk if not done: Stagnation in model performance, compliance issues.

Legal Compliance Audits for High-Risk Processes

• Ensures adherence to laws and regulations.
• Risk if not done: Legal penalties, financial losses.

Implementing these UX research strategies not only mitigates risks but also ensures that healthcare agents can continue to provide accurate, ethical, and compliant services. This ultimately benefits both the healthcare industry and patients, fostering trust and improving outcomes.

Keep a look out for my next blog post which will break down RLHF Analytics and how to practically apply it ethically in Healthcare Agents.