What’s the Risk – Sharing Data for Secondary Use
Healthcare organization are now recognizing the potential of sharing PHI – but are also very aware of the inherent risks.
When it comes to using health data for secondary purposes, privacy implications, legal implications and public relations ramifications are all major concerns for providers, payers and the pharma industry. Privacy Officers know that leveraging protected health information (PHI) or personally identifiable information (PII) requires them to tread carefully. Safeguarding patient privacy is of paramount importance and the repercussions for a breach can be costly, both in dollars and reputation. The role of Privacy Officers in healthcare organizations more important than ever before. In addition to being privacy champions, these individuals must now help their organizations navigate the regulatory landscape and manage risk to minimize financial and reputational costs.
Privacy Officers know that sharing data for secondary use is inherently an exercise in risk management. By effectively assessing the data’s exposure to risk, proper measures can be taken to safeguard individual privacy. It’s about achieving the right mix.
This paper offers up six ways that Privacy Officers can limit risk when releasing data for secondary uses. By doing so, Privacy Officers can be confident that their organization’s data sharing practices effectively protect privacy, comply with current legislation, and are defensible should a breach occur.
Situation: California’s Consumer Privacy Act inspired Comcast to evolve the way in which they protect the privacy of customers who consent to share personal information with them.
Situation: Integrate.ai’s AI-powered tech helps clients improve their online experience by sharing signals about website visitor intent. They wanted to ensure privacy remained fully protected within the machine learning / AI context that produces these signals.
Situation: Novartis’ digital transformation in drug R&D drives their need to maximize value from vast stores of clinical study data for critical internal research enabled by their data42 platform.
Situation: CancerLinQ™, a subsidiary of American Society of Clinical Oncology, is a rapid learning healthcare system that helps oncologists aggregate and analyze data on cancer patients to improve care. To achieve this goal, they must de-identify patient data provided by subscribing practices across the U.S.
Situation: Needed to enable AI-driven product innovation with a defensible governance program for the safe and responsible use
of voice-to-text data under Shrems II.
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