How to Implement AI Governance for Better Results

A recent survey of 233 health system executives found that 88% of use #AI in some form, but only 18% have a mature governance structure and full AI strategy. Why getting departments to use AI in the same way, with the same safeguards, is good for healthcare: https://lnkd.in/gSuH7ACG

We surveyed 233 healthcare executives and found that 88% of health systems are using AI, but only 18% have mature governance. Click here for the full report: https://lnkd.in/gWxJPxnk https://lnkd.in/gSuH7ACG

While 88% of health systems are using AI, only 18% have mature governance. https://lnkd.in/esPhiut5

A good read on how AI will enhance or unlock the potential of Real World Data. Healthcare decision making still lies with clinically relevant RWD, with AI serving as an enabler and aid faster decisions in trials.

🚨 AI is everywhere in healthcare—but do we have guardrails? A new survey of 233 health system leaders found: ◾ 88% are already using AI in some capacity. ◾ But only 18% report having a mature governance framework. ◾ In many organizations, 10+ departments are experimenting with AI independently—without a unified oversight strategy. 👉 Current use cases include ambient listening for clinical notes, coding support, denial prediction, and even patient-facing chatbots. The value is clear—but so are the risks. Without safeguards, AI can amplify bias, hallucinations, and workflow disruptions. One chilling example: models recommending inferior psychiatric treatments based on a patient’s race. What’s needed now: 🔹 Inclusive governance—bringing together clinical, IT, finance, HR, and compliance voices. 🔹 Clear policies for data use and vendor vetting. 🔹 Adoption of toolkits like the American Medical Association’s eight-step framework and industry roadmaps from groups like the Coalition for Health AI (CHAI). Healthcare is moving fast with AI adoption, but governance is lagging. To realize AI’s promise safely, systems must prioritize guardrails as much as growth. 💬 Curious—how is your organization approaching AI governance today? Full Article: https://lnkd.in/e64akYkB #AI #SafeGuards #Governance #AMA #CHAI #Healthcare

How to develop AI policies that work for your organization’s needs What to include in an AI policy: The AMA toolkit includes a model AI policy document that health care organizations can download and modify to align with their existing governance structure, roles, responsibilities and processes. Health systems should also be aware of state and federal laws that could impact AI’s use. At minimum, an organization’s AI policy should articulate: ✅ Definitions for relevant terms such as generative AI and machine learning. ✅ AI risks, including the risks associated with a lack of transparency, patient safety and data privacy and security. ✅ Permitted uses of approved and publicly available AI tools. This includes describing permitted-use cases, such as using AI for developing drafts of marking materials and research summaries. ✅ Prohibited AI uses, such as entering patients’ personal health information into publicly available AI tools. ✅ Permitted uses of approved AI tools, such as requirements and guidelines that all team members should follow when using AI tools. ✅ Governance, evaluation and approval processes for AI tools. ✅ Description of AI accountability and oversight, including risk assessment and regulatory compliance. ✅ Policy for how long AI generated information and a patient visit recordings will be retained. ✅ Transparency, including guidelines on when and how clinicians and patients should be made aware that AI is being used. ✅ Training, for example, incorporating AI training in the annual and ad hoc training program for everyone in the organization who uses AI. https://lnkd.in/dQJEb8nK

An innovative new approach is advancing equity in healthcare AI by identifying and correcting biases in datasets before they are used. This is a critical step towards ensuring AI systems are not just accurate, but also fair and representative of all communities. The key to a data-driven healthcare ecosystem via AI isn't just about technology—it's about building a foundation of trust that benefits every patient. Read: https://lnkd.in/gP-P5PKk

An innovative new approach is advancing equity in healthcare AI by identifying and correcting biases in datasets before they are used. This is a critical step towards ensuring AI systems are not just accurate, but also fair and representative of all communities. The key to a data-driven healthcare ecosystem via AI isn't just about technology—it's about building a foundation of trust that benefits every patient. Read: https://lnkd.in/gagewEyr

An innovative new approach is advancing equity in healthcare AI by identifying and correcting biases in datasets before they are used. This is a critical step towards ensuring AI systems are not just accurate, but also fair and representative of all communities. The key to a data-driven healthcare ecosystem via AI isn't just about technology—it's about building a foundation of trust that benefits every patient. Read: https://lnkd.in/ev3P5cT4

An innovative new approach is advancing equity in healthcare AI by identifying and correcting biases in datasets before they are used. This is a critical step towards ensuring AI systems are not just accurate, but also fair and representative of all communities. The key to a data-driven healthcare ecosystem via AI isn't just about technology—it's about building a foundation of trust that benefits every patient. Read: https://lnkd.in/g-75ixMs