Tag: Artificial Intelligence

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Health Law Highlights

Artificial Intelligence Highlights from FTC’s 2024 PrivacyCon

Summary of article from Sheppard Mullin Richter & Hampton LLP, by Carolyn Metnick, Gianfranco Spinelli:

PrivacyCon’s takeaways for healthcare organizations highlighted key considerations for the use of AI in healthcare, focusing on privacy themes, Large Language Models (LLMs), and AI functionality. The study identified four privacy concerns: potential for data misuse, personal nature of data, lack of awareness and consent in data collection, and surveillance by the government. It also highlighted security, privacy, and safety concerns in LLM platforms, particularly with third-party applications, urging developers to prioritize these aspects. The fallacy of AI functionality, where users trust AI blindly without data validation, was identified as a major issue, especially in healthcare where it can lead to misdiagnosis. The post concluded by emphasizing the need for healthcare organizations to establish governance and compliance committees to address these complex challenges and facilitate responsible AI development with privacy and ethical considerations in mind.

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Health Law Highlights

Doctors Are Getting on Board With genAI, Survey Shows

Summary of article from Healthcare IT News, by Andrea Fox:

A survey of 100 US physicians revealed that 81% believe generative AI can enhance care team interactions with patients. The majority (89%) of physicians require transparency about the sources of clinical decision support (CDS) data from vendors. However, physicians overestimate patients’ readiness for AI in healthcare, with 66% believing patients would be confident in AI-assisted decisions, contrasting with 48% of patients expressing confidence. The survey also highlighted a lack of clear AI usage guidelines in healthcare organizations. Despite initial skepticism, adoption of AI in healthcare is growing, with companies like Wolter Kluwer integrating AI into their products to aid clinical decision-making.

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Health Law Highlights

Navigating HIPAA Compliance in the Age of AI: Privacy and Security Considerations in Healthcare

Summary of article from HackerNoon, by mcmullen:

Artificial intelligence (AI) is revolutionizing various aspects of healthcare, but it also presents privacy and security risks, particularly in the context of data breaches. Compliance with the Health Insurance Portability and Accountability Act (HIPAA) is crucial when integrating AI into healthcare. To remain HIPAA compliant, healthcare organizations must understand AI algorithms, regularly update policies, and implement robust security measures. Despite the challenges, the implementation of AI in healthcare, when done responsibly and ethically, offers significant potential benefits for patient care and research.

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Health Law Highlights

Forecasting the Integration of AI into Health Care Compliance Programs

From Robinson Cole, by Kathleen Healy, Josh Yoo:

Healthcare entities need to incorporate AI standards into their compliance programs to manage and mitigate legal risks. Executive Order No. 14110 outlines key principles for AI including confidentiality, security, transparency, governance, and non-discrimination. The National Institute of Standards and Technology (NIST) provides a Risk Management Framework for AI and a playbook to help organizations manage AI risks. Key federal privacy and security laws like HIPAA and Section 5 will impact the use of AI in healthcare. It’s vital for healthcare entities to monitor evolving AI laws and regulations, inventory existing and upcoming AI use, educate themselves on updates, and adapt their compliance plans accordingly.

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Health Law Highlights

Behavioral Health Industry Reshaping As a Result of AI

From The National Law Review, Jean Marie R. Pechette, Neal D. Shah, Joelle M. Wilson, Catherine Kozlowski, Matthew T. Lin:

Artificial Intelligence (AI) is significantly influencing the field of behavioral health, offering potential advancements in diagnostics, treatment, and patient outcomes. The application of AI technologies ranges from virtual mental health assistants and predictive analytics to AI-enabled chatbots for therapy and AI-integrated Electronic Health Records (EHR) for diagnosis and treatment. These technologies are expected to expand further as trust in AI systems grows.

Despite the transformative potential of AI in behavioral health, the legal and regulatory implications are uncertain. The US lacks a comprehensive federal law that regulates AI development and use. However, efforts are underway to address potential risks, including promoting transparency, ensuring fairness, and protecting privacy and security of health information.

Key legal risks associated with the use of AI in behavioral health treatment include data privacy, algorithm bias, and professional liability. Data privacy risks involve ensuring compliance with HIPAA, 42 CFR Part 2, and state privacy laws.

Providers can mitigate these risks to some extent by obtaining informed consent from patients before using AI tools, vetting third-party vendors offering AI solutions for adherence to data privacy and security rules, seeking transparency from developers and vendors about the data on which AI tools were trained, and reviewing the scope of professional liability coverage before adopting AI-enabled tools.

While AI holds significant promise for transforming behavioral health care, it’s crucial to anticipate and address the evolving regulatory frameworks and legal risks associated with AI applications. AI regulation is a moving target, and anticipating and mitigating legal risks will be key to fostering a trustworthy and secure environment for both practitioners and patients.

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Health Law Highlights

Inside the Healthcare Industry: The Growing Importance of Intellectual Property Valuations

From J.S. Held, by Magi Curtis, Noor Al-Banna, Greg Campanella:

Healthcare and life sciences companies are increasingly recognizing the importance of Intellectual Property (IP) in their strategic growth initiatives, investments, and licensing of data. A study by Ocean Tomo found that approximately 90% of the value of companies in the S&P 500 comes from intangible assets, such as brands, technology, patents, data, and software. This has led to two major trends in the healthcare industry.

Healthcare organizations are also becoming more thoughtful in managing their IP. They are using IP analysis not just for accessing capital, but also to provide a baseline for management to understand the incremental value generated by different strategic approaches. The rise of AI platform development technology in healthcare, life sciences, and medical device industries is another trend that is accelerating. However, healthcare organizations need to be cautious about regulatory issues around AI use and the data it’s trained on.

Data is a significant IP asset that healthcare organizations can leverage. Anonymized information and technical data related to processes, procedures, and methodologies can be licensed or sold to healthcare technology, life science, and medical device companies. This data can also be used to train AI platforms, adding further value. However, healthcare organizations need to be aware of the potential costs and dangers related to the use of this data.

Healthcare organizations need to recognize the value of their brands and negotiate license fees for their use in joint ventures and partnerships. This can be achieved by establishing a rate card, a price list for the use of an organization’s name for a specific type of service. The earnings from these license fees can be reinvested into the system, research, and more.

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Health Law Highlights

Six AI Applications to Transform Your Clinical Operations

From D Magazine, by Dr. Harvey Castro:

Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize the healthcare industry by enhancing clinical outcomes, improving access to care, and elevating the patient experience. The integration of sophisticated AI applications is expected to increase healthcare efficiency, accuracy, and personalization globally. AI can automate routine tasks, allowing human expertise to focus on direct patient care. The potential benefits include earlier disease detection, reduced error rates, optimized resource allocation, and cost-effective solutions.

Challenges exist around transparency, data access, and over-reliance on technology. However, steady progress in AI validation is laying the foundation for new standards of evidence-based medicine. The future of healthcare will likely be defined by the fusion of clinical wisdom and machine insights, paving the way for innovative solutions to improve lives.

AI has the potential to transform clinical operations in several ways:

  • Rewriting Medical Language: Large language models can tailor medical vocabulary to fit the patient’s understanding. For example, they can convert discharge instructions into a coloring book for a young patient or translate complex medical and legal language related to lawsuits.
  • Virtual Nursing Assistants: AI-powered virtual assistants can optimize nursing workflows by performing basic triage, reviewing patient records, answering common questions, and scheduling appointments. This allows healthcare professionals to focus on more complex care needs. 
  • Medical Imaging Analysis: AI has shown proficiency in analyzing complex medical images and detecting anomalies, rare diseases, and cancers. This technology can free up radiologists’ time for more challenging cases while providing faster second opinions.
  • Virtual Clinical Assistants: AI assistants can augment clinicians during patient visits by providing real-time diagnostic and treatment suggestions. They can also summarize records and prompt providers to address preventative care gaps.
  • Predictive Analytics and Outreach: Machine learning can analyze vast amounts of data to identify individuals at high risk for emergent or costly conditions early on, enabling proactive healthcare delivery. This can improve patient outcomes and reduce healthcare costs.
  • Personalized Treatment Matching: AI can leverage real-world outcomes data to recommend treatments and care pathways most likely to benefit each unique individual. This personalized approach can enhance treatment effectiveness for complex conditions.
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Health Law Highlights

The FDA and the Future of AI Oversight

From Manatt, Phelps & Phillips, LLP, by Nicholas Bath Jr., Rachel Sher, Daniel Weinstein:

The U.S. Government Accountability Office (GAO) issued a report in January 2024 highlighting challenges faced by the U.S. Food and Drug Administration (FDA) in effectively regulating artificial intelligence (AI) and machine learning (ML) in medical devices and other emerging health care technologies. The report emphasized the need for clear regulations that balance safety, transparency, consumer protection, and innovation, especially considering the rapid evolution of AI/ML technology and its potential applications and risks.

Over the past five years, federal regulation of AI/ML has increased, particularly in the health care sector. In 2023, the FDA issued its first-ever AI/ML device draft guidance, aiming to provide a forward-thinking approach to the development of machine learning-enabled device software functions.

Despite the FDA’s efforts, the approach to AI/ML regulation has been criticized as uncoordinated and overly broad, potentially hindering technology development and rollout, and causing confusion among stakeholders. State legislators, regulators, and medical boards are beginning to introduce state-level policy, adding to the regulatory complexity.

Given the legislative gridlock, some stakeholders have proposed a novel approach to ensure the safety and effectiveness of AI/ML-enabled medical devices through public-private assurance laboratory partnerships. These labs would be testing grounds to validate and monitor AI/ML in medical devices. The proposal, while controversial, is expected to garner more attention in the coming months as the Congressional Bipartisan AI Task Force develops its comprehensive report and policy proposals to bolster the federal government’s ability to regulate AI/ML.

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Health Law Highlights

New AI Technique Significantly Boosts Medicare Fraud Detection

From Medical Xpress, by Florida Atlantic University:

  • Medicare is frequently targeted by fraudulent insurance claims, with the estimated annual fraud amounting to over $100 billion. Traditional methods of detecting fraud, which involve manual inspection of claims by a limited number of auditors, are often insufficient due to the volume and complexity of the data.
  • A study conducted by the College of Engineering and Computer Science at Florida Atlantic University explored the use of big data and machine learning models to detect Medicare fraud. However, handling imbalanced big data and high dimensionality, where the number of features is extremely high, presents significant challenges.
  • The researchers tested two big Medicare datasets, Part B and Part D, using a method called Random Undersampling (RUS) and a novel ensemble supervised feature selection technique. RUS works by randomly removing samples from the majority class until a specific balance between the minority and majority classes is achieved.
  • The results showed that the combined use of RUS and supervised feature selection outperformed models that used all available features and data. The best performance was achieved by performing feature selection, then applying RUS. This approach led to data reduction, more explainable models, and significantly better performance.
  • The study’s findings could have substantial implications for Medicare fraud detection, offering computational advantages and enhancing the effectiveness of fraud detection systems. If properly applied, these methods could significantly reduce costs related to fraud and improve the standard of health care service.
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Health Law Highlights

HHS Finalizes Regulation of Certain AI

From Manatt, Phelps & Phillips, LLP, by Alex Dworkowitz, Alice Leiter, and Randi Seigel:

  • The U.S. Department of Health and Human Services (HHS) has finalized a rule to regulate the use of artificial intelligence (AI) in health care.
  • The rule applies to predictive algorithms used in electronic health record (EHR) systems. It requires transparency in the use of AI, including information about the purpose, funding sources, training data, fairness measures, and validation process.
  • The rule aims to promote the development of fair, valid, and safe algorithms and address concerns about biased decision-making.
  • The regulation currently applies to developers of certified EHR software and may foreshadow future regulations for health care providers.
  • The rule also includes updates to the ONC Health IT Certification Program and provisions to improve interoperability and secure exchange of health information.