Some of Google’s top AI researchers are trying to predict your medical outcome as soon as you’re admitted to the hospital.
A new research paper, published Jan. 24 with 34 co-authors and not peer-reviewed, claims better accuracy than existing software at predicting outcomes like whether a patient will die in the hospital, be discharged and readmitted, and their final diagnosis. To conduct the study, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them. The data span 11 combined years at two hospitals, University of California San Francisco Medical Center (from 2012-2016) and University of Chicago Medicine (2009-2016).
We included EHR data from the University of California, San Francisco (UCSF) from 2012-2016, and the University of Chicago Medicine (UCM) from 2009-2016. We refer to each health system as Hospital A and Hospital B. All electronic health records were de-identified, except that dates of service were maintained in the UCM dataset. Both datasets contained patient demographics, provider orders, diagnoses, procedures, medications, laboratory values, vital signs, and flowsheet data, which represents all other structured data elements (e.g. nursing flowsheets), from all inpatient and outpatient encounters. The UCM dataset (but not UCSF) additionally contained de-identified, free-text medical notes. Each dataset was kept in an encrypted, access-controlled, and audited sandbox.
Ethics review and institutional review boards approved the study with waiver of informed consent or exemption at each institution.