Micromedex Spotlight on Human Work in an AI Era

Free Artificial Intelligence (AI) tools promise speed at the point of care, but clinical decisions demand more than convenience.

This article explains why these free tools often lack the clinical validation, transparent reasoning, evidence rigor, and safety-by-default design that patient safety requires, leaving clinicians and institutions to carry the real burden of accuracy and accountability. You'll see why human judgment, oversight, and shared governance remain essential whenever AI supports clinical decisions.

Key highlights:

• The latest data on accuracy, reliability and transparency of these free AI tools
• High risk tasks for free clinical AI tools

Read the full blog >

Check out how AI-powered search in clinical decision support can advance smarter, faster insights, giving time back to clinicians to focus on the human work they care about.

Explore AI in clinical decision support >

AI is reshaping how nurses work, giving them more time for what matters most: their patients. But as this guidance for early-career nurses makes clear, critical thinking, evidence-based decisions, and patient-first care remain deeply human responsibilities that no tool can replace.

Key highlights:

• 5 tips for early career nurses using AI

Read the guidance for nurses >

Each quarter, Micromedex publishes insights on healthcare trends from our subject matter experts.

There's no shortage of Artificial Intelligence (AI) headlines right now, but we find the human side of the story far more compelling, and far more important.

In this edition, our experts recognize the people behind the technology, the human experts who provide ongoing oversight, nuanced expertise, and the clinical judgment no algorithm can replace.

This focus on the human work is also a reminder of why healthcare teams adopt AI in the first place: to give clinicians, nurses, and pharmacists more time for direct patient care, human connection, and empathy. We hope you enjoy exploring our insights:

• Algorithmovigilance: The human work behind trusted clinical AI
• Hidden risks of free AI tools: Why clinical judgment still leads over convenience
• How MCP bridges the gap between AI agents and the clinical knowledge curated by human experts
• Human responsibilities: Tips for early career nurses using AI

Use the table of contents to jump to the area that interests you most.

As AI becomes a fixture in clinical decision support, its trustworthiness depends on more than smart design. It relies on the ongoing human work of monitoring and evaluating algorithms over time.

This article explains why clinicians, leaders, and governance teams must actively watch for drift, reduce bias, and validate performance in real-world settings as care standards evolve. Through the practice of algorithmovigilance, you'll see how sustained human oversight keeps clinical AI reliable, safe, and accountable.

Key highlights:

• 5 steps to algorithmovigilance

Read the full article >

The model context protocol (MCP) vendor that a health IT organization chooses today will define the quality, safety, and defensibility of their clinical AI for years to come. This article is for the leaders who understand that distinction, and want a framework for making the right call. Read on for five key considerations for MCP success in healthcare.

Key highlights:

• What is MCP in the context of healthcare
• Why it matters now more than ever
• How human expertise cannot be replicated by AI models
• Five key considerations for MCP success in healthcare

Read the full article >