Companies across digital health, life sciences, insurance, and other health verticals are seeing massive growing repositories of healthcare, social, lifestyle, demographic, geographic, and other types of data. These types of data reveal tons of insights about populations at the individual level. For example, analyzing wearable data in combination with prescription data can provide a holistic picture of a person’s health and habits; this analysis can produce better preventive care. By doing this at scale, companies can uncover new insights, identify patterns, and improve healthcare outcomes for potentially millions of people, or even billions. However, this combination of sensitive data types also raises significant privacy concerns that must be addressed with the utmost care and sensitivity.
Let’s start off with the most sensitive data asset for all humans: healthcare data. Healthcare data is one of the most personal and highly revealing types of information available, containing medical records, health histories, and genetic information. Combining this with non-healthcare data such as social media activity, purchasing history, and location data, allows researchers to gain a deeper understanding of a person's health and lifestyle, which can be invaluable for healthcare research and outcome improvement. However, this combination also poses a significant threat to people's privacy, as it exposes a wealth of intimate information about individuals.
To ensure the privacy of individuals today, companies contract consultants to analyze data and produce a set of privacy risks. These consultants and companies work together to remediate datasets to produce a safe and actionable dataset. However, this process generally takes 8+ weeks because of the heavily manual, human-led processes. Companies cannot afford to wait this long and people cannot afford to receive delayed care. That’s why we need automation to preserve privacy yet take advantage of all this data.
High data quality and patient privacy need to be accommodated simultaneously, which is no easy task. Doing this will enable companies to be flexible in their approach; they’ll be able to experiment freely and understand what is possible and what isn’t possible. Most importantly, this will shift the narrative from seeing patient privacy as a blocker to seeing it as an enabler to build trust and win over customers. . For example, people are more willing to participate in healthcare research when they a re confident that their personal information is being protected. Without this trust, companies risk losing valuable insights and opportunities to improve healthcare outcomes and their bottom lines.
In conclusion, the combination of healthcare and non-healthcare data has the potential to revolutionize healthcare research and innovation. However, this must be done with great care and sensitivity to protect people's privacy. The use of automated privacy technology like Integral is critical to achieving this goal, ensuring that personal information is protected, and that people's privacy rights are respected. By prioritizing privacy and building trust with the public, researchers can unlock the full potential of combined healthcare and non-healthcare data and improve healthcare outcomes for all.
