Google patent application offers new details on company's predictive EHR aggregation system | MobiH…

A Google patent application published on Thursday by the US Patent and Trademark Office gives a few more details on a predictive EHR system first highlighted by the tech company last May. The application was filed by Google in July 2017, and has not yet been granted.

As described in the application, Google’s system can aggregate and store EHRs for a diverse population, while compiling each individual patient’s records into a single chronological document. A computer or computer system running deep learning models would then use this collection of data to guide predictions of future health events, and to better contextualize the collected data from an individual’s record to highlight pertinent past events on an EHR. Each of these would be displayed to the provider on a desktop, tablet or smartphone display, helping them identify areas of concern or intervene prior to future events.

Along with an outline of its major components, the application also suggests that the system would be capable of aggregating records from multiple institutions and data formats. Examples of future clinical event predictions cited in the application included an unplanned transfer to intensive care unit, ER visits or readmission within 30 days of discharge, inpatient mortality and atypical lab results related to a number of conditions.

A representative from Google told MobiHealthNews that the tech company was not providing any new comments on the system, but pointed back to the company’s prior statements in a May blog post.

Here, Dr. Alvin Rajkomar and Eyal Oren — both of whom were among the patent application’s coauthors — highlighted the results of a Google, UC San Francisco, Stanford Medicine, and University of Chicago Medicine investigation that employed 46,864,534,945 retrospective EHR data points collected from 216,221 adult patients hospitalized for at least 24 hours at two US academic medical centers. From these data, the team’s deep learning models were able to predict upcoming in-hospital mortality, 30-day unplanned readmission, prolonged length of stay, and all of a patient’s final discharge diagnoses with an accuracy that outperformed traditional predictive models across the board.