# US 2024/0202170 A1 · Data discrepancy analysis using machine learning models

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- Number: US 2024/0202170 A1
- Document ID: US20240202170A1
- Status: Published application
- Kind: A1
- Application number: 18/068,870
- Filed: 2022-12-20
- Published: 2024-06-20
- Earliest claimed: 2022-12-20
- Assignees: Radiology Partners, Inc.
- Applicants: Radiology Partners, Inc.
- Inventors: Nina KOTTLER; Bartosz BREWINSKI; Kelly DENNEY; Telford BERKEY; Tuguldur SUKHBOLD; Josiah WALTON
- Web page: https://patentgrep.com/patent/US20240202170A1

## CPC

- G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
- G06F 16/2365: {Ensuring data consistency and integrity}
- G06F 16/258: {Data format conversion from or to a database}
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Provided are a computer program product, a system, and a computer-implemented method for performing data discrepancy analysis. Data is received from a plurality of source data systems. The data is analyzed to identify a data discrepancy of a data field. A recommendation is generated that indicates whether the data discrepancy is fixable. In response to the recommendation indicating that the data discrepancy is fixable, the data discrepancy is fixed by modifying a value of the data field, and the data is routed to a first downstream system service that does not rely on the data field and to a second downstream system service that does rely on the data field. In response to the recommendation indicating that the data discrepancy is not fixable, the data is routed to the first downstream system service, while the second downstream system service is disabled.

## Claims

### Claim 1

A computer program product, the computer program product comprising a computer readable storage medium having computer readable program code embodied therein that is executable to perform operations, the operations comprising:
receiving data from a plurality of source data systems;
analyzing the data to identify a data discrepancy of a data field;
generating a recommendation that indicates whether the data discrepancy is fixable;
in response to the recommendation indicating that the data discrepancy is fixable,
fixing the data discrepancy by modifying a value of the data field; and
routing the data to a first downstream system service that does not rely on the data field and to a second downstream system service that does rely on the data field; and
in response to the recommendation indicating that the data discrepancy is not fixable,
routing the data to the first downstream system service that does not rely on the data field; and
disabling the second downstream system service that does rely on the data field.

### Claim 2 (depends on 1)

The computer program product of claim 1, wherein the data from the plurality of source data systems is analyzed to identify common data configurations.

### Claim 3 (depends on 1)

The computer program product of claim 1, wherein the data from the plurality of source data systems is aggregated and standardized.

### Claim 4 (depends on 1)

The computer program product of claim 1, wherein the computer readable program code is executable to perform further operations comprising:
providing a discrepancy dashboard, wherein the discrepancy dashboard displays common data set configurations and indicates which of the plurality of source data systems are deviating from the common data set configurations.

### Claim 5 (depends on 1)

The computer program product of claim 1, wherein the data discrepancy comprises one of a missing value and an incorrect value.

### Claim 6 (depends on 1)

The computer program product of claim 1, wherein one or more machine learning models are used to identify the data discrepancy, generate the recommendation, and determine that the second downstream system service is to be disabled.

### Claim 7 (depends on 1)

The computer program product of claim 1, a first source data system of the plurality of source data systems comprises first data in accordance with Health Level Seven (HL7) international standards, and wherein a second source data system of the plurality of source data systems comprises second data in accordance with Digital Imaging and Communications in Medicine (DICOM) standards.

### Claim 8

A system, comprising:
a processor; and
a computer readable storage medium having computer readable program code that when executed by the processor performs operations, the operations comprising:
receiving data from a plurality of source data systems;
analyzing the data to identify a data discrepancy of a data field;
generating a recommendation that indicates whether the data discrepancy is fixable;
in response to the recommendation indicating that the data discrepancy is fixable, fixing the data discrepancy by modifying a value of the data field; and
routing the data to a first downstream system service that does not rely on the data field and to a second downstream system service that does rely on the data field; and
in response to the recommendation indicating that the data discrepancy is not fixable,
routing the data to the first downstream system service that does not rely on the data field; and
disabling the second downstream system service that does rely on the data field.

### Claim 9 (depends on 8)

The system of claim 8, wherein the data from the plurality of source data systems is analyzed to identify common data configurations.

### Claim 10 (depends on 8)

The system of claim 8, wherein the data from the plurality of source data systems is aggregated and standardized.

### Claim 11 (depends on 8)

The system of claim 8, wherein the operations further comprise:
providing a discrepancy dashboard, wherein the discrepancy dashboard displays common data set configurations and indicates which of the plurality of source data systems are deviating from the common data set configurations.

### Claim 12 (depends on 8)

The system of claim 8, wherein the data discrepancy comprises one of a missing value and an incorrect value.

### Claim 13 (depends on 8)

The system of claim 8, wherein one or more machine learning models are used to identify the data discrepancy, generate the recommendation, and determine that the second downstream system service is to be disabled.

### Claim 14 (depends on 8)

The system of claim 8, a first source data system of the plurality of source data systems comprises first data in accordance with Health Level Seven (HL7) international standards, and wherein a second source data system of the plurality of source data systems comprises second data in accordance with Digital Imaging and Communications in Medicine (DICOM) standards.

### Claim 15

A computer-implemented method comprising operations for:
receiving data from a plurality of source data systems;
analyzing the data to identify a data discrepancy of a data field;
generating a recommendation that indicates whether the data discrepancy is fixable;
in response to the recommendation indicating that the data discrepancy is fixable,
fixing the data discrepancy by modifying a value of the data field; and
routing the data to a first downstream system service that does not rely on the data field and to a second downstream system service that does rely on the data field; and
in response to the recommendation indicating that the data discrepancy is not fixable,
routing the data to the first downstream system service that does not rely on the data field; and
disabling the second downstream system service that does rely on the data field.

### Claim 16 (depends on 15)

The computer-implemented method of claim 15, wherein the data from the plurality of source data systems is analyzed to identify common data configurations.

### Claim 17 (depends on 15)

The computer-implemented method of claim 15, wherein the data from the plurality of source data systems is aggregated and standardized.

### Claim 18 (depends on 15)

The computer-implemented method of claim 15, wherein the operations further comprise:
providing a discrepancy dashboard, wherein the discrepancy dashboard displays common data set configurations and indicates which of the plurality of source data systems are deviating from the common data set configurations.

### Claim 19 (depends on 15)

The computer-implemented method of claim 15, wherein the data discrepancy comprises one of a missing value and an incorrect value.

### Claim 20 (depends on 15)

The computer-implemented method of claim 15, wherein one or more machine learning models are used to identify the data discrepancy, generate the recommendation, and determine that the second downstream system service is to be disabled.