The life sciences industry has just scratched the surface of what emerging technologies like cloud, artificial intelligence, and machine learning can do and the efficiencies they can create. The healthcare ecosystem is uniquely positioned to take advantage of this technological evolution due to the large amounts of data it generates, but it also raises new concerns that life sciences companies' legal teams need to consider. .
The industry currently has medical devices such as asthma inhalers, knee replacement implants, and smart pills that are equipped with remote sensors to monitor usage habits, treatment progress, the integrity of the device itself, and more. Data can be sent back to the patient's care team. EY's April 2022 Tech Horizon survey found that 58% of life sciences executives across all subsectors, including pharmaceuticals and medical devices, say data and analytics is one of their top three investment priorities over the next two years. He said that there is a high possibility that the number of The expansion of the Internet of Medical Things (IoMT), breakthrough advances in AI models, and unprecedented expansion of available health data are enabling patients and healthcare professionals alike to improve overall health outcomes. New insights and actionable steps along the continuum of care are being fostered to improve outcomes.
We call this new technology-driven future the Intelligent Health Ecosystem (IHE). Hyperconnected systems built on hyperfluid data flows can optimize decision-making, improve outcomes, accelerate access to new innovations, and deliver personalized, patient-centered health experiences. However, each new technology introduces risks and challenges that require careful consideration of potential legal and regulatory pitfalls. To advance IHE solutions, life sciences organizations must reconsider their data governance and privacy compliance policies, incorporate privacy by design and security by design principles early in product development, and proactively engage patients. . Communicate our privacy practices and responsibilities to consumers, vendors, and strategic partners.
New data privacy considerations
Life sciences departments have always had to consider patient privacy concerns. Increasing consumer awareness and concern about the collection and processing of sensitive personal data is leading to new legislation and changing the calculus about what it takes to maintain robust privacy and data governance practices. Ta.
Comprehensive consumer privacy laws have already been enacted (e.g., California, Virginia, Colorado) or will soon be enacted in nearly a dozen U.S. states. Some laws, such as Washington State's My Health My Data Act, specifically focus on the processing of personal information. Health data. Requirements vary by state and may include risk assessments, contractual obligations regarding third-party data processing, and patient rights to access, correct, or request deletion of personal data. This all builds on the existing regulatory implications of international data privacy laws, which may also have an impact on cross-border data transfers and data localization requirements.
IHE solutions are enabled by high levels of connectivity and speed. Therefore, applicable privacy compliance and data governance practices may need to be updated to align with these evolving requirements. This allows, for example, patients to be aware of what data remote sensors are collecting and with whom that data is being shared. Interoperable systems that refine raw data to gain valuable insights must maintain access controls to ensure security, minimize disclosure, and reduce the risk of breach. For life sciences organizations to take full advantage of IHE innovations without exposing themselves to unnecessary risks, it is important to clearly communicate these obligations and provide training on how to address them.
Designed with privacy and security in mind
Remote sensors that collect patient health data and share it with medical teams quickly and accurately, whether it's monitoring the movement of medical devices or wearables monitoring patient vitals during drug clinical trials. It must be possible to do so, but safely. AI solutions that leverage large language models to answer patient questions must be trained on large datasets, but do not necessarily require personally identifiable information at the individual level to accomplish their goals. Not that I will. Such considerations should include implementing privacy by design and security by design principles early in the product development lifecycle and encouraging product teams to engage privacy and security experts, especially on complex issues. must be dealt with. The first relevant question is:
Do the systems/tools allow tagging of data and creation of associated metadata? Are security measures such as encryption, role-based access control, anonymization supported and utilized? Data subject rectification? /Can deletion requests be fulfilled quickly and easily? Does the system/tool ​​make it easy to set and enforce record retention schedules? If applicable, require patient consent before collecting sensitive health data? And is it as easy to withdraw consent as it is to provide it? Is the health flow in place? What systems, users, Is the data sufficiently mapped and understood to provide full transparency about what processing takes place?
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