Digital Health Report
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Q1 2020 DIGITAL HEALTH REPORT During this unprecedented time, digital health is disrupting our healthcare Machine Learning Legal Issues delivery system for the better. Digital health companies are working hard to for Digital Health Companies in provide innovative telehealth, digital screening, and testing services. They Commercial Transactions are leveraging AI to assess symptoms, model outcomes, and identify possible treatments for COVID-19. Digital health companies are providing physicians and other frontline responders with new tools to fight this pandemic. The Wilson Sonsini digital health team is here to support you during this time. Providing a cross-functional team approach for our digital health clients, we can help you: • understand the latest regulatory changes; • draft and enter into contracts; • protect your ground-breaking intellectual property (IP); and • obtain financing for your next round of innovation. Please feel free to reach out to your By Rob Parr and Scott McKinney for digital health companies, and Wilson Sonsini team member or a traditional contract approaches may not member of the digital health practice With its potential to revolutionize properly address intellectual property, industries and products of all types, group for assistance. risk allocation, data use, and other “machine learning” (ML) is a hot important issues that are unique to ML topic. ML, a sub-category of artificial or artificial intelligence more generally. intelligence, refers to software In This Issue This article is intended to highlight for algorithms that are programmed to digital health companies that wish to analyze data, learn from that analysis, Machine Learning Legal commercialize ML-enabled technologies and improve themselves. ML has gained Issues for Digital Health (ML Providers) five key areas in Companies in Commercial significant traction in the digital health commercial contracts where we routinely Transactions ..................... Pages 1-4 space, with numerous digital health companies developing ML products help clients identify and address certain How to Incorporate FDA into designed to help predict, detect, and issues unique to artificial intelligence Your R&D .......................... Pages 5-7 treat illness, increase the efficiency of and ML. delivering healthcare, and find solutions Preparing for Your First Sale: 1) Input Data. Input data refers to How Digital Health Companies to other complex challenges facing data that ML technologies process Can Plan for Healthcare health providers, payers, and patients. Business ........................... Pages 8-9 to generate a given output. ML Artificial intelligence technologies like Providers benefit from obtaining How HITRUST Can Help Get ML present some unique legal challenges vast amounts of input data, You to a Series A............ Pages 9-12 Continued on page 2... Q1 2020 DIGITAL HEALTH REPORT Machine Learning Legal Issues . (Continued from page 1) because the more input data ML prevent the ML Providers from this manner. Non-negotiated technologies process the “smarter” storing, processing, and using data terms often allocate to the those technologies become. This the input data in the manner data user all risks associated is especially true for ML Providers that they plan to. ML Providers with using the data and may also whose products focus on preventing, should also consider obligating include specific data use terms diagnosing, or treating medical data licensors to provide input governing what a data user can conditions given the importance data on an aggregated and and cannot do with the data. of generating accurate results. We de-identified basis because that For ML Providers whose ML often find that agreements do not aggregated and de-identified data products are designed to function adequately and clearly address the is more likely to be exempt from as diagnostic or treatment tools data provider’s and data recipient’s laws that govern the collection, for certain medical illnesses, rights to input data, so ML Providers use, disclosure, and protection of assuming all risks associated should be careful to obtain proper sensitive data such as personal with the use of source data could input data licenses or usage rights to information. This is especially mean that the ML Provider avoid claims of intellectual property important for ML Providers is taking on significant risk. misappropriation or infringement. whose ML technologies are Before downloading or using designed to process patient data data that is available under non- (a) Negotiated Terms. ML Providers that would be subject to the negotiated terms, ML Providers may obtain input data pursuant Health Insurance Portability should carefully evaluate the to negotiated contract terms, Accountability Act of 1996 corresponding data use terms to such as from their customers (HIPAA) unless that data is de- ensure that the ML Provider’s or other commercial data identified in accordance with intended use of the data complies providers. In these negotiated HIPAA’s specific requirements. with all applicable data use transactions, ML Providers Finally, ML Providers who rights and restrictions. It is also should consider seeking rights obtain input data under in many ML Providers’ interests to modify, restructure, and negotiated terms should also to track the source(s) for input reorganize input data, and consider trying to obtain specific data obtained under standardized to use input data, including representations, warranties, and terms to evaluate potential when aggregated with other covenants regarding the input risks associated with using the data, to enable ML Providers data as further described in data and better enable ongoing to train and improve their ML Section 4(c) below. compliance with applicable technologies and to create license terms. output data (further described in (b) Non-Negotiated Terms. ML Section 3 below). ML Providers Providers may also obtain input 2) ML Technology Improvements. should also consider trying to data from many different online Absent clear contractual terms get perpetual rights to store sources under standardized, non- describing each parties’ rights and use the input data because negotiated contractual terms. to improvements to, changes to, it can be difficult to track These kinds of terms typically and derivatives of the underlying data sources and to separate apply to health-related, open ML technology that arise from individual data elements from sourced scientific or research an engagement between an ML larger data sets. ML Providers data made available in online Provider and an ML technology should also carefully review any repositories and to data obtained licensee (Improvements), there confidentiality terms in their from third party websites. ML is legal uncertainty around who contracts with data providers to Providers should exercise caution would be deemed to own those ensure those provisions do not when obtaining input data in Improvements, especially when the 2 Q1 2020 DIGITAL HEALTH REPORT licensee may exert some control over a given output. Although this in their license agreements to the operation of the ML technology.1 output data may be protectable as obtain certain protections for the Accordingly, ML license agreements intellectual property, primarily transaction. The ultimate terms should state clearly who owns all under trade secret law2, in some of that protection will depend on Improvements as a contractual cases the protection offered to ML the ML technology being licensed, matter between the parties and output data by intellectual property its intended application and the properly effect transfer of ownership laws and other legal doctrines applicable deal dynamics, including from one party to the other with is ambiguous or altogether non- the parties’ respective bargaining appropriate contractual assignment existent. As a result, similar to the power. That said, at a minimum, language. approach we described in Section these general guidelines may be 2 above for Improvements, ML helpful for ML Providers to consider The ML Provider could start with license agreements should clearly when contracting with customers/ the position that it will exclusively and expressly identify who owns the licensees: own Improvements. This is often the output data as a contractual matter most practical approach from the between the parties and include (a) Representations and Warranties – ML-Provider’s standpoint because appropriate assignment language ML Provider representations and Improvements may arise from an to effect the desired allocation warranties about the accuracy, aggregation of inputs and actions of ownership rights. ML license quality, or performance of the that cannot be attributed solely to agreements should also address any ML technology and output data one party or licensee and may not rights the non-owner obtains to present some unique challenges be readily separable from or useful use the output data. If output data in transactions involving the independent of the baseline ML is sensitive or valuable to an ML licensing of ML technology. technology. If a licensee of ML Provider, then an ML Provider who These things can be difficult to technology pushes for ownership to owns that output data pursuant gauge when the ML technology Improvements during a negotiation, to its ML agreement could try to learns during an engagement, then the ML Provider should grant the licensee of that output particularly given the opacity evaluate whether to accommodate