INSIGHT: Structuring Investments and Doing Deals in AI and Health Care

Artificial intelligence is changing today’s health care industry and serving as the foundation for its future. Whether it’s the use of AI to enhance the efficiency and accuracy of health care administrative processes, the application of AI to influence clinical diagnostic and other care systems, or a myriad of other uses already in place or in development, AI, also known as “machine learning” is everywhere.

In recent years, the size and pace of deals involving health care technology and AI has intensified. In the health care industry of the future, winners are likely to be those companies that are able to effectively deploy and utilize the power of AI in ways that benefit of their consumers, payors, other customers and their investors.

AI Deal Flow as of 2019

Investments, mergers, and acquisitions in which AI is at the core of the business are occurring across the health care continuum, including those involving hospitals, physicians,and other care providers, insurers and other payers, but also applications in biopharma and medtech sectors.

Funding of AI in health care is at a historic high, with nearly $600M of investment in Q2 of 2018 alone. The investments are in a range of applications including robot-assisted surgery, automated image diagnosis systems, administrative workflow applications, fraud detection, cybersecurity and others. The underlying technologies use deep learning and predictive analysis, natural language processing and other applications—all of which have AI at their technological core.

Swiss-based healthcare giant Roche’s 2018 acquisition of Flatiron Health for $1.9B provides a case study of the value of AI to traditional players. Roche concluded that its pharmaceutical division could benefit from Flatiron’s systems and data focused on cancer and cancer care. The acquisition permits both companies to accelerate progress toward data-driven personalized health care.

The deal is just the tip of the iceberg for likely future deal flow.

AI Deal Making Terms and Conditions

Because AI in health care is relatively new, there are no manuals or well-defined bodies of AI-specific deal precedent that can be consulted to inform transactions. Instead, M&A counsel must adapt existing approaches focused on key deal-making fundamentals i.e.:

• understanding the value proposition;
• designing the due diligence investigation;
• addressing representations, warranties and risk allocation; and
• recognizing unique issues in AI (and non-AI) deals.

Understanding the value proposition and money flow is essential. The value in AI companies can be in software, hardware and data, in intellectual property, but also in the business as a going concern.

Understanding the value proposition will drive a well-focused due diligence investigation that’s typically includes: (1) intellectual property, (2) people, (3) regulatory compliance, (4) liability management, and (5) understanding the company’s operations and use of AI.

For example, in any technology transaction, intellectual property investigation focusing on software and data is vital. Diligence inquiries will relate to the protection of software and algorithms, patent and copyright validity, third party rights in the IP, infringement issues and others.

Similar data-related questions (i.e., who owns the data, have required consents been obtained etc.), and the company’s people (i.e., are they under contract, do they have restrictive covenants, have they executed invention assignments and NDAs and others) are essential to effective diligence.

Regulatory Concerns

Due diligence on regulatory compliance will implicate a number of legal frameworks, so determining the applicable rules and regulations to the application and business model is essential to understanding risk. For example, although many AI applications are administrative in nature and therefore don’t involve patient care, they will still commonly be subject to regulations governing data and data privacy, including HIPAA, GDPR, and other privacy and security requirements.

Business models in which AI is used to support providers in the delivery of patient care will potentially be subject to the above referenced data/privacy concerns, but also FDA, licensure, health care regulatory, reimbursement and other requirements because of their nexus to patient care.

And innovative applications such as those focused on health and wellness, health-related conditions and others that are outside the traditional health care delivery space are potentially subject to the foregoing, along with additional requirements such as a growing body of federal and state consumer protection laws and rules.

The due diligence investigation in AI transactions must also consider legal liability risk in an emerging field in which existing theories of product liability are combined with emerging theories based in cyberliabilty and other risks. In AI-related deals in health care, buyers and sellers must consider and attend to novel legal claims and manage that risk through insurance or other means.

In AI M&A transactions, old-school concepts are being refined in the form of due diligence informed representations and warranties related to the technology, the underlying data, the protection of proprietary information, compliance with AI-related regulatory regimens, protection of liability risk and company operations. Those representations and warranties will, in turn, be backed up by indemnity obligations.

AI has the potential to revolutionize health care by creating efficiencies, improving outcomes, pushing care to new settings and in other ways. For investors and businesses in health care, finding the right AI partners early will help position for long-term success.

In the context of deal-making, AI-specific due diligence coupled with a deep understanding of the unique role of AI in an ever-growing number of companies in the market-place is essential. AI is already everywhere in health care, and AI-related M&A transactions are poised to grow exponentially in the future.

In Part 1 of this series, Polsinelli authors looked at the particular privacy and security concerns and ethical implications of AI in health care and give best practices.

This column does not necessarily reflect the opinion of The Bureau of National Affairs, Inc. or its owners.

Author Information

Bruce A. Johnson is a health care regulatory and transactional lawyer based in Polsinelli’s Denver office who works with traditional and innovative health care companies on the application and deployment of digital health in the delivery of health care services.

Bill Mahood is the practice chair of Polsinelli’s national Mergers, Acquisitions and Divestitures practice. He advises technology and other clients in a broad array of acquisition, divestiture, and joint venture transactions.

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