Hcc coding notifications

Patent metadata

Field	Value
Publication number	US-2021125720-A1
Application number	US-201816494324-A
Country	US
Kind code	A1
Filing date	Mar 7, 2018
Priority date	Mar 17, 2017
Publication date	Apr 29, 2021
Grant date	—

Abstract

In one embodiment, a computer-implemented method, comprising: receiving information from plural patients, the information from each of the plural patients comprising one or more items recorded over a predefined period of time; formatting the items for use in a machine learning algorithm, wherein the formatting comprises: associating each of the one or more items with a respective word; forming a sentence for each of the plural patients from the words; and transforming the sentences for the plural patients into a numerical matrix, wherein the matrix comprises one row per patient and one column per word, each word weighted by occurrence; training models according to the machine learning algorithm based on the formatted items of the plural patients; determining if a first patient should have a hierarchical condition category code by applying one or more formatted items of the first patient to the trained models; and providing a notification of any suspect hierarchical condition category codes based on the application of the one or more formatted items of the first patient to the trained models.

First claim

1 . A computer-implemented method, comprising:
receiving information from plural patients, the information from each of the plural patients comprising one or more items recorded over a predefined period of time; formatting the items for use in a machine learning algorithm, wherein the formatting comprises:
associating each of the one or more items with a respective word;
forming a sentence for each of the plural patients from the words; and
transforming the sentences for the plural patients into a numerical matrix, wherein the matrix comprises one row per patient and one column per word, each word weighted by occurrence;
training models according to the machine learning algorithm based on the formatted items of the plural patients; determining if a first patient should have a hierarchical condition category code by applying one or more formatted items of the first patient to the trained models; and providing a notification of any suspect hierarchical condition category codes based on the application of the one or more formatted items of the first patient to the trained models.

2 . The method of claim 1 , wherein the formatting comprises quantifying an importance of each of the one or more items of the plural patients.

3 . The method of claim 1 , wherein the training comprises building a plurality of independent decision trees from the matrix, wherein each decision tree is built based on ordering nodes of each of the decision trees according to the importance of each word to the suspect hierarchical category code.

4 . The method of claim 3 , wherein the formatted items of the first patient is achieved by:
associating each of the items of the first patient with a respective word;
forming a sentence for the first patient from the words; and
transforming the sentence into a numerical matrix.

5 . The method of claim 4 , wherein the applying comprises:
passing the words of the transformed sentences through all of the plurality of decision trees; and tallying outcomes from all of the plurality of decision trees, wherein the determining is based on the tallied outcomes for each of the words passed through all of the plurality of decision trees.

6 . The method of claim 3 , wherein building includes building hierarchical condition category code models according to a training phase, wherein each model corresponds uniquely to one of the hierarchical condition category code models.

7 . The method of claim 6 , wherein each outcome is based on applying the words of the patient to all of the hierarchical condition category code models.

8 . The method of claim 1 , wherein providing the notification comprises recommending to a user that a hierarchical condition category code recorded for the first patient is possibly over-coded.

9 . The method of claim 8 , wherein the notification includes a confidence measure.

10 . The method of claim 1 , wherein providing the notification comprises recommending to a user that a hierarchical condition category code for the patient is possibly missing.

11 . The method of claim 10 , wherein the notification includes a confidence measure.

12 . The method of claim 1 , wherein the one or more items includes one or any combination of cares, medications, labs, or vitals.

13 . The method of claim 1 , wherein transforming includes tokenizing the sentences with word separators, counting occurrences of each of the words in each of the sentences, and normalizing and weighting with diminishing importance the words that occur in a majority of the patients.

14 . A system, comprising:
one or more computing devices configured to:
receive information from plural patients, the information from each of the plural patients comprising one or more items recorded over a predefined period of time;
format the items for use in a machine learning algorithm, wherein the formatting comprises:
associating each of the one or more items with a respective word;
forming a sentence for each of the plural patients from the words; and
transforming the sentences for the plural patients into a numerical matrix, wherein the matrix comprises one row per patient and one column per word, each word weighted by occurrence;

train models according to the machine learning algorithm based on the formatted items of the plural patients;
determine if a first patient should have a hierarchical condition category code by applying one or more formatted items of the first patient to the trained models; and
provide a notification of any suspect hierarchical condition category codes based on the application of the one or more formatted items of the first patient to the trained models.

15 - 19 . (canceled)

20 . A non-transitory computer readable medium encoded with instructions executable by a processor or processors that causes the processor or processors to:
receive information from plural patients, the information from each of the plural patients comprising one or more items recorded over a predefined period of time; format the items for use in a machine learning algorithm, wherein the formatting comprises:
associating each of the one or more items with a respective word;
forming a sentence for each of the plural patients from the words; and
transforming the sentences for the plural patients into a numerical matrix,
wherein the matrix comprises one row per patient and one column per word, each word weighted by occurrence; train models according to the machine learning algorithm based on the formatted items of the plural patients; determine if a first patient should have a hierarchical condition category code by applying one or more formatted items of the first patient to the trained models; and provide a notification of any suspect hierarchical condition category codes based on the application of the one or more formatted items of the first patient to the trained models.

Assignees

• Koninklijke Philips Nv

Inventors

• Cheng Chih-Wen
• Menn Bryant
• Emanuele Vincent

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N20/00

  Machine learning · CPC title

• G16H50/20 Primary

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

• G16H10/60 Primary

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

• G16H15/00

  ICT specially adapted for medical reports, e.g. generation or transmission thereof · CPC title