Artificial intelligence (AI) is playing a bigger role in healthcare administration, especially in tasks like front-office phone automation and answering services. Simbo AI is one company that uses AI to help make communication in medical offices more efficient. As medical practice administrators, owners, and IT managers in the United States start using AI tools, it is important to know how to manage risks and involve stakeholders properly. This article talks about ways to include stakeholders and use feedback methods to improve AI Risk Management Frameworks (AI RMF) and support responsible AI use.
The National Institute of Standards and Technology (NIST) created the AI Risk Management Framework (AI RMF) to help organizations manage risks from AI in a responsible way. The framework was released on January 26, 2023. It is voluntary, but it encourages organizations to think about risks to people, institutions, and society. The AI RMF focuses on making AI trustworthy during its design, development, use, and testing stages.
NIST made the AI RMF with help from many different groups. This included public comments, workshops, and asking for information. Because generative AI is becoming more common, NIST released an update in July 2024 called NIST-AI-600-1. This update focuses on the special risks that generative AI can cause. It suggests risk management steps that fit the goals of different organizations.
Healthcare providers and companies like Simbo AI that use AI-based answering services can use the AI RMF in their systems. Doing this helps them make sure their AI works safely and reliably.
In healthcare, stakeholders include medical practice administrators, clinicians, IT managers, patients, regulatory bodies, technology providers, and even patients’ families. Each group has their own views and worries about AI, such as privacy, accuracy, fairness, and transparency.
Since AI use in healthcare has many ethical questions, including stakeholders throughout AI’s life can help in several ways:
One example of good stakeholder involvement is how the AI RMF was created. It included feedback from government agencies, universities, businesses, and the public.
Healthcare organizations that use AI tools like Simbo AI’s automated answering services need clear feedback channels. These help improve AI and reduce risks. Some good strategies for collecting and handling feedback are:
Medical administrators and IT staff should often talk to AI system users using surveys and interviews. These methods help gather ideas about AI’s performance, problems, and what users need.
Inspired by NIST’s open comment process, healthcare groups can hold meetings or webinars where stakeholders share their thoughts and concerns about AI systems.
Tracking user data like call success rates, how accurate AI answers are, and patient satisfaction helps measure AI performance. Sharing these numbers with stakeholders helps them understand and improve AI.
Having a clear way for users to report problems with AI tools allows quick action to fix issues. Prompt reporting also helps avoid harm and improve AI programs.
Healthcare administrators should work with groups that set AI rules to make sure feedback and improvements follow the rules.
These steps together create a system of ongoing learning to make AI frameworks better. The AI RMF is designed to be updated over time based on feedback and new risks.
In a recent study, researchers Papagiannidis, Mikalef, and Conboy made a framework about responsible AI governance. This work relates to healthcare providers using AI tools. The framework talks about three parts:
Healthcare groups should try to include these practices when they set up AI governance systems, especially when using systems like Simbo AI.
AI and stakeholder involvement come together in workflow automation in medical offices. AI tools like Simbo AI’s phone automation help schedule appointments, answer patient questions, and send messages. This reduces staff workload so they can focus more on patient care.
However, automation also brings risks that need attention:
Using AI automation with good risk management helps keep patients safe and makes operations run more smoothly. Feedback loops help spot problems fast. This lets healthcare groups keep AI systems that follow ethical and legal rules.
Healthcare groups in the U.S., including those using Simbo AI, must follow many national and global rules. Laws about patient privacy, like HIPAA, affect AI governance and data handling rules.
NIST’s AI RMF matches these regulations by offering a voluntary but clear way to handle AI risks. NIST also started the Trustworthy and Responsible AI Resource Center in March 2023. This center gives practical tools, examples, and international views to help healthcare groups manage AI issues.
Keeping up with new policies and working with rule-making bodies helps medical offices use AI responsibly while gaining benefits from new technology.
Good AI governance means organizations must be ready in different ways:
Medical administrators can use the NIST AI RMF and governance ideas to review and improve how prepared they are for AI.
People trust AI more when healthcare groups use open methods. This means sharing AI performance numbers, explaining how decisions are made, and openly discussing risk management results.
Accountability tools like audit trails and incident reviews help make sure AI is used responsibly. Healthcare leaders should support these actions to keep trust and integrity in automated services.
Besides internal staff, patients and their families are important in AI governance. Clear communication about how AI tools manage private data and help with healthcare access increases patient confidence.
Getting feedback from patients on AI interactions through surveys or follow-up calls gives direct input to improve systems. This two-way communication supports inclusive governance and better fits patient needs.
AI is becoming common in healthcare work, especially in automated phone answering through companies like Simbo AI. Managing AI risks carefully is important. Involving stakeholders and having clear feedback methods are key ways to keep improving AI Risk Management Frameworks.
Using structural, relational, and procedural governance practices, healthcare groups can deploy AI ethically, follow laws, and improve patient care.
Medical administrators, owners, and IT managers in the United States should actively involve many stakeholders, create clear feedback channels, be transparent, and prepare their organizations well when adding AI tools. These efforts support responsible and trustworthy AI use in healthcare, helping technology meet both operational needs and ethical standards.
The AI RMF is designed to help individuals, organizations, and society manage risks related to AI. It promotes trustworthiness in the design, development, use, and evaluation of AI products, services, and systems through a voluntary framework.
It was created through an open, transparent, and collaborative process involving public comments, workshops, and a Request for Information, ensuring a consensus-driven approach with input from both private and public sectors.
The AI RMF was initially released on January 26, 2023.
NIST published a companion AI RMF Playbook, an AI RMF Roadmap, a Crosswalk, and Perspectives to facilitate understanding and implementation of the framework.
Launched on March 30, 2023, this Center aids in implementing the AI RMF and promotes international alignment with the framework, offering use cases and guidance.
On July 26, 2024, NIST released NIST-AI-600-1, a Generative AI Profile that identifies unique risks of generative AI and proposes targeted risk management actions.
No, the AI RMF is intended for voluntary use to improve AI risk management and trustworthiness.
It builds on and supports existing AI risk management efforts by providing an aligned, standardized framework to incorporate trustworthiness considerations.
NIST provides a public commenting process on draft versions and Requests for Information to gather input from various stakeholders during framework development.
The goal is to cultivate trust in AI technologies, promote innovation, and mitigate risks associated with AI deployment to protect individuals, organizations, and society.
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