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January 16, 2020

Sharing Health Care Data With Digital Giants: Overcoming Obstacles and Reaping Benefits While Protecting Patients

Author Affiliations
  • 1Department of Medicine, University of California, San Francisco
JAMA. 2020;323(6):507-508. doi:10.1001/jama.2019.21215

In November 2019, The Wall Street Journal revealed that Ascension, a large nonprofit Catholic health system with facilities in 23 states, had allied with Google to share data about millions of patients, under the code name “Project Nightingale.”1 The goal was to store patient information in Google’s cloud, then apply machine-learning technology to deliver recommendations or predictions to clinicians and administrators at Ascension. Presumably, Google also hoped to gain insights and build products that could ultimately be scaled and monetized.

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    2 Comments for this article
    Potential for Patient Data Abuse - Preexisting Conditions
    Michael Heinzmann, B.S. | Retired, Pharmaceutical Industry
    Large data collection companies (e.g., Google, et. al.) would have the ability and potential to share patient data with insurers who might then match genetic, diagnostic, treatment, and/or billing data to identify preexisting condition(s). Such data would be invaluable for insurers to acquire. Health insurance could be denied based upon the use of this data without ever having to disclose the data's source. Until the U.S. codifies the right of everyone to obtain health insurance regardless of any preexisting condition(s), such health data will be a risk for patients with preexisting conditions to obtain health insurance. Without some type of universal health insurance program for everyone, this will remain an area of conflict between patients, insurers, and data collectors.
    Community Owned Regional Digital Utilities
    George Anstadt, MD | U of Rochester
    The commercial advantage of large data sets is not news; exploitation of information asymmetry (lack of transparency) is an entrenched cause of the high cost of US Healthcare (“The Price We Pay,” Marty Makary). The commercial bias can be eliminated by community data ownership within regional utilities - shared digital commons managed for the common good by a board of local stakeholders. Additionally, the non-medical determinants of health in each region can be added to the data, greatly increasing the ability of physicians and policy makers to increase health decision-making regionally. And, regionally aggregated patient health outcomes data is the most powerful quality improvement data for individual physicians, surgical teams, or health systems. Add the cost data, and you can measure and improve value (health created per dollar). Watcher and Cassel are correct: the great benefits of applying digital efficiency, deep learning, and enhanced decision making tools to medicine must be protected and realized. This can best be done as a shared resource. Community ownership and oversight of the data can remove the barriers: commercial exploitation and digital distrust.