Barbara C Zellerino

US Patent:

20130166318, Jun 27, 2013

Filed:

Sep 15, 2011

Appl. No.:

13/820036

Inventors:

Catherine D. Lathem - Komoka, CA
Keith C. Mitchell - Atlanta GA, US
Cathy L. Plunkett - Kennesaw GA, US
Cheryl A. Pederson - Eden Prairie MN, US
Mary E. Kundus - Hudson WI, US
Mark A. Schroer - Burnsville MN, US
Barbara C. Zellerino - Newburgh IN, US
Garri L. Garrison - Atlanta GA, US
Monica L. Sanders - Portland ME, US
JaeLynn C. Williams - Sandy UT, US

Assignee:

3M INNOVATIVE PROPERTIES COMPANY - ST. PAUL MN

International Classification:

G06F 19/00
G06Q 50/22

US Classification:

705 2

Abstract:

Computer-implemented methods of processing medical data are described. The methods comprise selecting cases associated with a first parameter from medical data associated with a plurality of cases, determining a subset of cases associated with a medical facility-acquired condition, and determining another subset cases not associated with the medical facility-acquired condition. Estimated costs associated with those cases associated a medical facility-acquired condition are generated, along with expected costs for these same cases in the absence of the medical facility-acquired condition. Methods further allowing comparisons to data related to normative collections of medical facilities are also disclosed. Computerized systems for processing medical data and computer-readable storage media comprising instructions that when executed in a processor cause the processor to process medical data are also described.

US Patent:

20210157979, May 27, 2021

Filed:

Apr 18, 2018

Appl. No.:

16/605929

Inventors:

- St. Paul MN, US
Barbara C. Zellerino - Newburgh IN, US

International Classification:

G06F 40/205
G06F 40/289
G16H 10/60

Abstract:

At least some aspects of the present disclosure direct to systems and methods of extracting medical entry information from medical documentation. A method comprises the steps of: identifying patient information needed for a predefined medical entry; finding the patient information in documents associated with the patient, wherein finding the patient information includes annotating the documents with a natural language processor to detect phrases and words corresponding to the patient information in the patient documents and analyzing the documents with a machine learning processor trained using the annotated documents to detect the patient information in the patient documents; and exporting the patient information found as medical entry fields.