Healthcare AI Micro-Credentials: An Open-Source Curriculum Initiative 🏥🤖

Welcome to the collaborative curriculum repository for the Healthcare AI Micro-Credentialing cyberinfrastructure.

As predictive algorithms and decision-support tools are rapidly integrated into clinical and administrative workflows, there is an urgent need to train the healthcare workforce on AI safety, algorithmic bias, and clinical oversight. Traditional multiple-choice assessments are insufficient for evaluating the nuanced clinical reasoning required in the age of AI.

This repository serves as the central hub for crowdsourcing, developing, and peer-reviewing an open-access, role-specific curriculum designed to be deployed on our logic-based, AI-assisted credentialing platform.

🎯 Project Vision

Our cyberinfrastructure utilizes a node-based visual editor, localized Large Language Models (LLMs), and mandatory "rationale assessments" to evaluate a learner's underlying thought process when interacting with clinical AI.

However, the technology is only as good as the clinical expertise behind it. Our goal is to build a massive, globally searchable repository of high-fidelity, peer-reviewed clinical simulations spanning the entire hospital ecosystem.

👥 Who We Are Looking For

We are issuing an open call to the global medical education community. We need subject matter experts to help us author branching scenarios for the following functional domains:

• Physicians & Medical Providers (Surgical, Medical, Intensive Care, Diagnostics)
• Advanced Practice & Midlevel Providers (NPs, PAs, CNMs)
• The Nursing Workforce (Inpatient, Outpatient, ICU, Informatics)
• Allied Health & Therapeutics (PT/OT, Respiratory, Pharmacy)
• Imaging, Radiology, & Laboratory Services
• Health Information, Billing, & Revenue Cycle
• Quality, Safety, Compliance, & Hospital Operations

🛠️ How to Contribute

You do not need to be a software developer to contribute! We are looking for clinical logic and scenario narratives.

1. Propose a New Scenario (Submit an Issue)

The easiest way to contribute is by opening an Issue. Navigate to the Issues tab and select the New Clinical Scenario template. You will be asked to provide:

• Target Audience: (e.g., ICU Nurses, Triage Physicians, Medical Coders)
• Core AI Concept: (e.g., Automation Bias, Alarm Fatigue, Algorithmic Opacity)
• The Narrative Arc: A brief description of the clinical setup.
• The Critical Decision Node: What must the learner decide?
• The Rationale Prompt: What specific justification are we demanding from the learner?

2. Collaborate on Node-Mapping

For accepted scenarios, we will collaborate in the discussion threads to map out the "Correct Pathways," "Recovery Loops" (where learners safely see the consequences of minor errors), and "Terminal Fail-States" (where severe clinical or safety breaches occur).

3. Review and Refine

Help us peer-review existing scenario drafts to ensure they meet the highest standards of evidence-based practice and accurately reflect localized or international clinical guidelines.

📄 Example Contribution

Here is an example of what a successful scenario contribution looks like:

Department: Emergency Medicine Concept: Triage and Automation Bias Node 1 (Setup): Manage the arrival of a trauma victim from a multi-car accident. Node 2 (AI Input): An AI triage tool suggests "Moderate Risk" based on initial electronic vitals. Node 3 (Decision): The clinician detects abdominal distension. Do they override the AI to move the patient to surgery, or wait for secondary imaging as suggested by the AI? Rationale Assessment: "You prioritized the automated triage score over your physical assessment of abdominal swelling. Defend your reasoning regarding clinical stewardship and the consequences of automation bias in high-pressure environments."

📚 About the Platform

The scenarios developed here will be integrated into a web-based cyberinfrastructure featuring:

• No-Code Visual Authoring: Transforming your text-based logic into interactive branching simulations.
• Local Sovereign AI Engines: Protecting institutional data and evaluating learner logic in real-time.
• Adaptive Delivery: Delivering your scenarios via classic text, interactive Socratic chat, or immersive visual environments.

📝 Citation and License

The curriculum content generated in this repository is licensed under the GNU Affero General Public License v3.0.

If you use this framework or platform architecture in your research, please cite our upcoming publication:

Vald, G., Sermet, Y., & Demir, I. (Upcoming). Scalable Micro-Credentials for AI Literacy in Healthcare: An Open-Source Framework and Call for Collaborative Curriculum Development. International Journal of Educational Technology in Higher Education.

📬 Contact

For institutional partnerships or questions regarding the cyberinfrastructure deployment, please reach out via GitHub Issues or contact the research team at Tulane University.

Core Team: Gabriel Vald, Yusuf Sermet, Ibrahim Demir (School of Science and Engineering, School of Medicine, Tulane University)