Robert B Epstein

US Patent:

20220375574, Nov 24, 2022

Filed:

May 4, 2022

Appl. No.:

17/736890

Inventors:

- New York NY, US
Daniel Elgort - New York NY, US
Xiaojin Dong - Berwyn PA, US
Denis Whelan - New York NY, US
Richard Herzog - New York NY, US
Murray Becker - Belle Mead NJ, US
Robert Epstein - Belle Mead NJ, US
Irwin Keller - Belle Mead NJ, US
Ron Vianu - New York NY, US

International Classification:

G16H 30/20
G06T 7/00

Abstract:

Described are techniques for determining an accurate ground-truth Bayesian Inter-Reviewer Agreement Rate (BIRAR) from unreliable sources. For instance, a process can include obtaining an initial set of diagnostic imaging exams, wherein each diagnostic imaging exam includes a severity grade associated with an initial radiologist. For each diagnostic imaging exam, two or more secondary quality assurance (QA) reviews can be obtained for each diagnostic imaging exam, wherein the secondary QA reviews are associated with QA'ing radiologists different than the initial radiologist. One or more inter-reviewer agreement rates can be determined for the QA'ing radiologists, based on the secondary QA reviews associated with the QA'ing radiologists. A diagnostic error associated with one or more initial radiologists can be determined based at least in part on the one or more inter-reviewer agreement rates for the QA'ing radiologists and a subsequent diagnostic imaging exam obtained for a respective one of the initial radiologists.

US Patent:

20200334566, Oct 22, 2020

Filed:

Apr 16, 2019

Appl. No.:

16/386006

Inventors:

- New York NY, US
RICHARD HERZOG - NEW YORK NY, US
DANIEL ELGORT - NEW YORK NY, US
ROBERT EPSTEIN - BELLE MEAD NJ, US
IRWIN KELLER - BELLE MEAD NJ, US
MURRAY BECKER - BELLE MEAD NJ, US
JOHN PELOQUIN - NEW YORK NY, US
SCOTT SCHWARTZ - NEW YORK NY, US
GREG DUBBIN - NEW YORK NY, US
GRANT LANGSETH - NEW YORK NY, US
ELIZABETH SWEENEY - NEW YORK NY, US
MATTIA CIOLLARO - PITTSBURGH PA, US
ANDRE PERUNICIC - NEW YORK NY, US

International Classification:

G06N 20/00
G16H 50/20

Abstract:

In an embodiment, a computer-implemented process comprises accessing a plurality of digitally stored, unstructured medical diagnostic data; digitally displaying a first subset of the medical diagnostic data, the first subset of the medical diagnostic data including at least a first set of diagnostic reports, using a computer display device, concurrently with digitally displaying one or more quality control checklists that are specific to a medical discipline represented in the first set of diagnostic reports; receiving digital input specifying one or more errors in the first set of diagnostic reports and digitally storing the digital input in association with the first subset of medical diagnostic data; training a hierarchical Bayesian machine learning model using the digital input and the first subset of medical diagnostic data; evaluating the hierarchical Bayesian machine learning model, after training, for a second subset of the medical diagnostic data, the second subset being different from the first subset, to result in outputting one or more provider error rate data; applying a grading algorithm to the one or more provider error rate data to yield one or more output provider quality score values.