Unlocking Big Data for Healthcare
There are powerful opportunities facing healthcare today. Big Data Analytics (BDA) is being leveraged by a broad spectrum of healthcare organizations to solve ongoing challenges. BDA is opening doors when it comes to minimizing expenses, saving time, improving patient health and advancing precision medicine. With such obvious benefits, it should come as a surprise that BDA is still in its infancy.
PHI is the most sensitive of all data – and unlocking it for BDA is no easy feat. Fears around patient privacy hamper progress. As health data repositories grow, more and more patient data is being linked from multiple sources. The risk of re-identification grows along with the size of health data being created. Petabytes of health data are sitting in siloes that could hold important answers to improving patient outcomes and minimizing the cost of healthcare delivery. Storage of the data is the easy part. Deciding how to unlock the data so it can be used for analysis or shared with an ecosystem of partners is the greater challenge. De-identification allows for the unlocking of health data for secondary use, but only a risk-based method can address the sensitivity and multitude of data. Understanding the privacy risks and finding the balance between data access and data anonymity is central to BDA’s future success.
Health Information Systems (HIS) vendors are in a unique position to capitalize on their data assets and deliver on the promise of BDA. With data on billions of healthcare claims and millions of patients, they have the foundation to build the integrated data repositories needed to generate new insights into healthcare’s biggest challenges. This white paper looks at how BDA can influence healthcare for the better, discusses the inherent risks in large and linked datasets, and offers guidance on the steps companies can take to protect patients and themselves.
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.
This course runs on the 2nd Wednesday of every month, at 11 a.m. ET (45 mins). Click the button to register and select the date that works best for you.