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1. NZ328870 - Disease management method and system

Office New Zealand
Application Number 328870
Application Date 29.09.1997
Publication Number 328870
Publication Date 28.05.1999
Grant Number 328870
Grant Date 09.09.1999
Publication Kind B
IPC
G06F 19/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
G06F 17/60
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
60Administrative, commercial, managerial, supervisory or forecasting purposes
CPC
G06F 19/325
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
324Management of patient independent data, e.g. medical references in digital format
325Medical practices, e.g. general treatment protocols
G16H 10/60
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
10ICT specially adapted for the handling or processing of patient-related medical or healthcare data
60for patient-specific data, e.g. for electronic patient records
G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
30for calculating health indices; for individual health risk assessment
G16H 50/50
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
50for simulation or modelling of medical disorders
G16H 50/70
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
70for mining of medical data, e.g. analysing previous cases of other patients
Applicants GlaxoSmithKline LLC
Inventors Boyko, David Alexander
Wong, Bruce Jan On
Gallo, Edward Francis
Langer, Dennis
Press, Bruce
Stavrakas, Spyros
Agents AJ PARK
Priority Data 96 27074 30.09.1996 US
Title
(EN) Disease management method and system
Abstract
(EN)
A computer implemented method for disease or condition intervention management using information about patients existing in at least one database involves the steps of: a) processing, based on predetermined criteria, the patient information in the database to extract patient information for a group of patients relating to an identified disease or condition; b) defining a predictive model, including: i) defining, using the information available in the database, a set of events or data relevant to the identified disease or condition; ii) converting the extracted patient information and the defined events or data into files comprising event-level information; iii) defining a time-window for providing a timeframe from which to judge whether specific ones of the defined events should be considered in subsequent processing; iv) identifying a set of variables as potential predictors; v) processing the event-level information, using the time-window and the set of variables, to generate an analysis file; (vi) performing statistical analysis on the analysis file to generate the prediction model and a set of rules for use in identifying at-risk patients diagnosed with or who may develop the identified disease or condition, the prediction model and rules being a function of a subset of the set of variables; c) applying the prediction model and the rules to the same or new set of event-level information to identify at-risk patients for the identified disease or condition, or to identify patients who may be at risk for developing the identified disease or condition; d) preparing an intervention list from the identified at-risk patients and selecting, for at least one at-risk patient, an intervention; e) distributing or facilitating the distribution of the intervention to the patient; and optionally f) recording and tracking an intervention result for each at-risk patient based on the respective selected intervention; and optionally g) updating the historical data in at least one database with each intervention result corresponding to said database; and optionally h) repeating step b(ii); and optionally i) re-applying the prediction model and rules to the event-level information extracted from the data in the updated database.