See original article here
https://www.discoveriesinhealthpolicy.com/2023/12/hhs-scandalously-bad-x-2-costbenefit.html
See AI review of Google (etc) letters here
https://brucedocumentblog.blogspot.com/2023/12/ai-comparison-of-hhs-onc-proposal.html
You
20231214 Press Etc re ONC AI Final.pdf
Attached, you have a number of new press articles about a final rule from HHS about electronic health records and artificial intelligence. From reading these articles, please provide ten important takeaways - and focus specifically on rules or implications for applications that involve or impact artificial intelligence in health care.
ChatGPT
ChatGPT
The document is a comprehensive collection of press releases and articles regarding the U.S. Department of Health and Human Services (HHS) finalizing the Health Data Technology and Interoperability: Certification Program Updates Algorithm Transparency and Information Sharing (HTI-1) rule. This rule is focused on advancing health IT interoperability and transparency, particularly in relation to artificial intelligence (AI) in healthcare. The document highlights various aspects of the rule, its implications, and reactions from different stakeholders in the healthcare sector.
I will now summarize the key takeaways related to AI in healthcare from this document.
Here are ten important takeaways from the HHS final rule on electronic health records and AI in healthcare:
1. Algorithm Transparency: The rule introduces first-of-its-kind transparency requirements for AI and predictive algorithms in certified health IT.
2. Scope of Impact: ONC-certified health IT is used by over 96% of hospitals and 78% of office-based physicians in the U.S., significantly impacting healthcare delivery.
3. Assessment and Decision Making: Clinical users will gain access to essential information about AI algorithms, assisting in assessing their fairness, validity, effectiveness, and safety.
4. USCDI Version 3 Adoption: The United States Core Data for Interoperability Version 3 will be the new standard in the Certification Program, enhancing patient data accuracy and promoting equity.
5. Information Blocking Revisions: The rule revises certain definitions and exceptions related to information blocking, supporting better information sharing.
6. Interoperability-Focused Reporting Metrics: New reporting metrics for certified health IT developers will provide insights into the usage of certified health IT in care delivery.
7. Voluntary Commitments from Healthcare Companies: Leading healthcare companies have made commitments to align with the rule, focusing on safe and responsible AI deployment in healthcare.
8. Risk Management Framework: There's an emphasis on comprehensive tracking of AI applications, evaluating potential harms, and mitigating risks.
9. Non-Regulatory AI Tools: The rule impacts certified HIT developers primarily, but healthcare providers using non-regulated AI tools are also advised to stay informed.
10. Future Regulatory Actions: This rule is part of a broader governmental effort to ensure the safe, secure, and responsible use of AI in healthcare, indicating potential future regulations.
These takeaways reflect a significant step towards ensuring transparency, safety, and efficacy in the use of AI technologies in the healthcare sector.
You
I use a vender's software for tumor recognition on pathology slides in our pathology department. The software is currently sold as "RUO" research use but we have validated for local clia lab use in a supportive, not diagnostic, manner. Does this rule apply to our applicaiton in our path lab in our hospital, or does it only apply to stuff integrated in an EHR.
ChatGPT
ChatGPT
The HHS final rule primarily applies to health IT systems that are certified under the ONC Health IT Certification Program, which typically includes electronic health records (EHR) systems. In your case, since you're using a vendor's software designated for research use only (RUO) and validated locally for CLIA lab use in a non-diagnostic, supportive capacity, it may not fall directly under the scope of this rule. The rule is more focused on software integrated into EHR systems and its transparency requirements are geared towards those systems. However, it's important to stay informed about any potential broader implications or future regulations that might affect all types of health IT, including specialized applications like yours in pathology.
You
How does the final rule affect the use of Ciinical Decision Support software? I understand that in some circumstances CDS is also regulated by FDA as a medical device, so it is confusing if there are both FDA rules and HHS ONC rules. ??
ChatGPT
ChatGPT
The HHS final rule and FDA regulations intersect but have distinct focuses regarding Clinical Decision Support (CDS) software. The FDA regulates CDS as a medical device in specific circumstances, primarily when it provides specific treatment or diagnostic recommendations. The HHS final rule, under the ONC Health IT Certification Program, would apply to CDS that's integrated into certified EHR systems, emphasizing transparency, interoperability, and information blocking aspects.
Therefore, a CDS could potentially be subject to both sets of regulations, depending on its functionality and integration with EHR systems. It's important to understand the specific requirements of each regulatory framework and how they apply to your particular CDS software.
You
20231214 HHS ONC FINAL RULE AI EHR (INSPECTION)916pp.pdf
Can you summarize any remarks in this final rule about interaction between HHS CDS rules and FDA CDS (medical device) rules?
ChatGPT
ChatGPT
The HHS final rule addresses the interaction between HHS CDS rules and FDA CDS (medical device) rules by focusing on transparency and risk management in Clinical Decision Support (CDS) systems. It introduces requirements for transparency about the design, development, and evaluation of CDS tools, especially those using AI and predictive models. The rule emphasizes the need for health IT developers to disclose information about these tools, including their training data and fairness assessments. This aligns with broader goals of ensuring safety, effectiveness, and non-discriminatory practices in healthcare technologies. However, the rule does not directly modify FDA's regulatory approach but complements it by enhancing transparency and user awareness in the use of CDS systems.
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You
You have (prior prompt) the final rule circa 900 pdf pages, from HHS about electronic health records and artificial intelligence. From reading this, please provide ten important takeaways - and focus specifically on rules or implications for applications that involve or impact artificial intelligence software and services in health care.
ChatGPT
ChatGPT
I have reviewed the HHS final rule on electronic health records and AI in healthcare, which spans over 900 pages. I will summarize ten key takeaways focusing on the implications for AI software and services in healthcare. This will take a little time to analyze the document in detail, so I appreciate your patience.
After reviewing the extensive HHS final rule document, here are ten important takeaways related to AI in healthcare:
1. Algorithm Transparency: The rule mandates transparency for AI algorithms in certified health IT, requiring disclosure of design, development, and performance aspects.
2. Certification Program Updates: Revisions to certification criteria for health IT, including for decision support systems and predictive models, are introduced.
3. Interoperability Enhancement: The rule emphasizes improving interoperability, particularly in the context of AI applications in health IT.
4. Risk Management Practices: Developers must apply risk management for predictive decision support interventions, focusing on aspects like fairness and safety.
5. Information Blocking Provisions: Revised definitions and exceptions related to information blocking support better data sharing, crucial for AI integration.
6. USCDI Version 3 Adoption: Adoption of this standard aims at improved data accuracy and equity, impacting AI applications' data handling.
7. Decision Support Interventions (DSI): New certification criteria for DSIs are established, focusing on health equity and transparent AI use in decision-making.
8. Predictive DSI Definition: A new definition for Predictive Decision Support Intervention is adopted, with specific certification requirements.
9. Public Transparency: Developers are required to make summary information about their AI risk management practices publicly available.
10. Governance Requirements: Policies and controls for governance are specified, ensuring data integrity and ethical use in AI applications.
These points highlight the rule's focus on enhancing transparency, interoperability, and responsible AI use in health IT.
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