Patient predictive admission, discharge, and monitoring tool

Patent metadata

Field	Value
Publication number	US-2020013490-A1
Application number	US-201816490815-A
Country	US
Kind code	A1
Filing date	Mar 2, 2018
Priority date	Mar 3, 2017
Publication date	Jan 9, 2020
Grant date	—

Abstract

Predictions can be provided for admission, discharge, pathway, and units for a patient during a stay at a healthcare facility. In some aspects, a computing device receives initial data regarding the patient and generates an admission or discharge prediction indicating a probability that the patient will be admitted to or discharged from the healthcare facility. The computing device segments portions of the initial data into segmented data and stores the segmented data in one or more of a plurality of segmentation categories. The computing device compares the segmented data to one or more patterns of a predictive model and, based on the comparing, generates a total length-of-stay (LOS) prediction indicating a duration of time for which the patient will stay at the healthcare facility.

First claim

What is claimed is:

1 . A system for storing electronic data, the system comprising:
one or more processors; a raw patient database; a normalized and categorized patient database; a prediction database; a user interface; and a memory in communication with the one or more processors, the memory storing instructions, wherein, when executed by the one or more processors, the instructions cause the one or more processors to:
receive initial patient data;
store the initial patient data in the raw patient database;
extract, from the initial patient data, initial categorized patient data relating to one or more conditions of a patient upon arrival to a first unit of a healthcare facility;
store the initial categorized patient data in the normalized and categorized patient database;
after storing the initial categorized patient data in the normalized and categorized patient database, delete the initial patient data from the raw patient database;
based on the initial categorized patient data, generate a first prediction comprising a predicted characteristic of a patient stay at the first unit;
store the first prediction in the prediction database;
receive additional patient data based on the patient stay at the first unit;
store the additional patient data in the raw patient database;
extract, from the additional patient data, additional categorized patient data relating to one or more conditions of the patient upon discharge from the first unit;
store the additional categorized patient data in the normalized and categorized patient database;
after storing the additional categorized patient data in the normalized and categorized patient database, delete the additional patient data from the raw patient database;
segment portions of the initial categorized patient data and the additional categorized patient data into segmented data, wherein the segmented data comprises a plurality of time series, each of the plurality of time series comprises a plurality of indicators for a condition of the patient across a period of time;
based on the segmented data, generate a second prediction comprising a predicted characteristic of a patient stay at a second unit of the healthcare facility; and
store the second prediction in the prediction database.

2 . The system of claim 1 , wherein generating the second prediction comprises comparing the segmented data to one or more patterns of a predictive model.

3 . The system of claim 1 , wherein the characteristic of the patient stay at the first unit comprises an admit to the second unit prediction, a discharge prediction, a length-of-stay in the first unit prediction.

4 . The system of claim 1 , wherein the characteristic of the patient stay at the second unit comprises a length-of-stay in the second unit prediction, and/or a final disposition prediction.

5 . The system of claim 1 , wherein the condition of the patient comprises arrival location, clinical condition, and/or demographics data.

6 . The system of claim 1 , wherein generating the second prediction comprises:
comparing the segmented data to patterns of a plurality of predictive models; and combining results of the comparing with majority or weighted voting or statistical combination.

7 . The system of claim 1 , wherein the instructions, when executed by one or more processors, cause the one or more processors to:
after receiving the initial data, receive temporal data comprising variables relating to the patient; transform the temporal data into transformed data; and store the transformed data in one or more of a plurality of pattern categories.

8 . The system of claim 7 , wherein the transforming comprises:
converting a variable of the temporal data into a categorical variable; changing a scale or orientation of the variable; combining multiple variables of the temporal data; and transforming a coordinate system of the temporal data.

9 . A method for storing electronic data, the method comprising:
receiving, by a device comprising at least one processor and at least one memory in communication with the at least one processor, initial patient data; storing, by the device, the initial patient data in a raw patient database; extracting, by the device from the initial patient data, initial categorized patient data relating to one or more conditions of a patient upon arrival to a first unit of a healthcare facility; storing, by the device, the initial categorized patient data in a normalized and categorized patient database; after storing the initial categorized patient data in a normalized and categorized patient database, deleting, by the device, the initial patient data; based on the initial categorized patient data, generating, by the device, a first prediction comprising a predicted characteristic of a patient stay at the first unit; storing, by the device, the first prediction in a prediction database; receiving, by the device, additional patient data based on the patient stay at the first unit; storing, by the device, the initial patient data in the raw patient database; extracting, by the device from the additional patient data, additional categorized patient data relating to one or more conditions of the patient upon discharge from the first unit; storing, by the device, the additional categorized patient data in a normalized and categorized patient database; after storing the additional categorized patient data in a normalized and categorized patient database, deleting, by the device, the additional patient data; segmenting, by the device, portions of the initial categorized patient data and the additional categorized patient data into segmented data, wherein the segmented data comprises a plurality of time series, each of the plurality of time series comprises a plurality of indicators for a condition of the patient across a period of time; based on the segmented data, generating, by the device, a second prediction comprising a predicted characteristic of a patient stay at a second unit of the healthcare facility; and storing, by the device, the second prediction in the prediction database.

10 . The method of claim 9 , wherein generating the second prediction comprises comparing, by the device, the segmented data to one or more patterns of a predictive model.

11 . The method of claim 9 , wherein the characteristic of the patient stay at the first unit comprises an admit to the second unit prediction, a discharge prediction, a length-of-stay in the first unit prediction.

12 . The method of claim 9 , wherein the characteristic of the patient stay at the second unit comprises a length-of-stay in the second unit prediction, and/or a final disposition prediction.

13 . The method of claim 9 , wherein the condition of the patient comprises arrival location, clinical condition, and/or demographics data.

14 . The method of claim 9 , wherein generating the second prediction comprises:
comparing, by the device, the segmented data to patterns of a plurality of predictive models; and combining, by the device, results of the comparing with majority or weighted voting or statistical combination.

15 . The method of claim 9 , further comprising:
after receiving the initial data, receiving, by the device, temporal data comprising variables relating to the patient; transforming, by the device, the temporal data into transformed data; and storing, by the device, the transformed data in one or more of a plurality of pattern categories.

16 . The method of claim 15 , wherein the transforming the temporal data into the trans

Assignees

• Univ Rush Medical Center

Inventors

• Rumoro Dino Peter

Classifications

• G06F16/2365

  Ensuring data consistency and integrity · CPC title

• G16H10/60 Primary

  for patient-specific data, e.g. for electronic patient records · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title