Systems and methods for data cleansing such as for optimizing clinical scheduling

• US 10,090,069 B2
• Filed: 05/10/2016
• Issued: 10/02/2018
• Est. Priority Date: 12/17/2015
• Status: Active Grant

• Current Assignee: Kairoi Healthcare Strategies, Inc.
• Original Assignee: Kairoi Healthcare Strategies, Inc.
• Inventors: Lee, Russell D., Wu, Jeffrey
• Primary Examiner(s): Saeed, Usmaan
• Assistant Examiner(s): Zhao, Yu
• Application Number:
  US15/151,298
• Publication Number:
  US 20170177804A1
• Time in Patent Office: 875 Days
• Field of Search: None
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A scheduling system and method for data cleansing may be used to optimize clinical scheduling. The present disclosure describes receiving clinical record data, in an agnostic manner, from a system including a source scheduling database containing the clinical record data; mapping the clinical record data to a desired format; conforming the clinical record data to standardized scheduling elements of the scheduling system; cleansing, in a manner configurable by a user, the clinical record data to purge portions of the clinical record data; providing the clinical record data to an optimization engine for optimization of the clinical record data; optimizing the clinical record data by applying configurable logic to the clinical record data; and uploading one or more newly defined optimized scheduling templates via an outbound connection back to the scheduling system.

Claims

1. A scheduling system configured for data cleansing to optimize clinical scheduling, the system comprising:

one or more hardware processors of a clinical scheduling system configured by machine-readable instructions to;

receive clinical record data, in an agnostic manner, from a system including a source scheduling database containing the clinical record data, wherein;

received clinical record data is received in one of a plurality of different formats the clinical scheduling system is configured to operatively accept for purposes of optimizing scheduling templates,

received clinical record data is received from at least one table in the source scheduling database, and

at least some of the appointment records include data from the at least one table in the source scheduling database;

map the clinical record data to a desired format, the desired format including a plurality of fields of standardized scheduling elements of the clinical scheduling system, wherein mapping comprises mapping respective appointment records to corresponding fields of the standardized scheduling elements;

based on the mapping, conform the clinical record data to standardized scheduling elements of the scheduling system by parsing the clinical record data and reformatting the clinical record data by assigning portions of the data to appropriate fields to form the standardized scheduling elements from the appointment records, wherein a given standardized scheduling element has fields stored in a single table of the clinical scheduling system from each of at least two tables in the source scheduling database;

cleanse, in a manner configurable by a user, the clinical record data to filter out portions of the clinical record data, wherein cleansing comprises;

determining whether to exclude data of at least some appointment records based on whether the respective appointment records contain an error related to one or more of data errors, data artifacts, or business logic;

after cleansing, provide the standardized scheduling elements to an optimization engine for optimization of at least one of provider or facility scheduling templates based on the clinical record data;

optimize, with the optimization engine, the at least one of provider and facility scheduling templates by applying configurable logic to the standardized scheduling elements in order to provide one or more at least partially newly defined optimized scheduling templates that configure at least one of providers and rooms for improved usage of time relative to un-optimized scheduling templates, wherein optimizing comprises;

matching provider availability with customized variables, the customized variables being based on visit complexity, visit length, number of exam rooms, provider preference, or non-physician resources;

optimizing at least some of the scheduling templates for providers matched with the customized variables based on user-defined rules to optimize schedules; and

detecting and resolving a conflict in the user-defined rules to optimize schedules; and

generate one or more communications configured to upload one or more at least partially newly defined optimized scheduling templates via an outbound connection back to the scheduling system, wherein;

the one or more hardware processors are further configured by machine-readable instructions to populate a primary fact table of data of the clinical scheduling system surrounded by dimensional tables of the clinical scheduling system, the primary fact table of data including appointment information, subject and provider keys, appointment scheduling start times, and appointment scheduling end times, the dimensional tables including subject dimensional tables and provider dimensional tables;

the one or more hardware processors are further configured by machine-readable instructions to utilize a star schema extensible framework, the star schema extensible framework including one or more fact tables referencing a plurality of dimension tables, the fact tables including one or both of numerical values or information regarding where descriptive information is kept, and the dimension tables including records with attributes to describe the fact data; and

the one or more hardware processors are further configured by machine-readable instructions to run one or more cleansing functions to ignore one or more of data that is incomplete, appointment information that is missing a start time, or appointment information that is missing an end time.

2. The system of claim 1, wherein conforming the clinical record data allows the scheduling system to work with any electronic medical record.

3. The system of claim 1, wherein the dimensional tables include standardized dimensional tables that comprise detail information that includes one or more of appointment outcome information having appointment statuses, actual appointment start times, or actual appointment end times, and wherein subject and provider dimensional tables include detail information that includes at least one of subject demographics or provider specialty information.

4. The system of claim 1, wherein the scheduling system includes subject and provider dimensional tables with details that include one or both of subject demographics or provider specialty information.

5. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to extract data from the source scheduling database or other source scheduling databases via an OLEDB connection, an ODBC connection, or another application program interface (API).

6. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to cleanse data errors including rows of data missing information.

7. The system of claim 1, wherein data that is excluded from the optimization engine is stored for analytics and reporting, and wherein the clinical scheduling system is configured to generate one or more reports on types and amounts of data that are being excluded from analysis, the types and amounts of data that are being excluded from analysis including one or more of data with missing information, data that is not relevant, data that is incomplete, or data that is erroneous.

8. A scheduling method configured for data cleansing to optimize clinical scheduling, the method being performed by one or more hardware processors configured by machine-readable instructions, the method comprising:

receiving clinical record data, with one or more processors of a clinical scheduling system, in an agnostic manner, from a system including a source scheduling database containing the clinical record data, wherein;

received clinical record data is received in one of a plurality of different formats the clinical scheduling system is configured to operatively accept for purposes of optimizing scheduling templates,

received clinical record data is received from at least one table in the source scheduling database, and

at least some of the appointment records include data from at least one table in the source scheduling database,

mapping, with one or more processors of the clinical scheduling system, the clinical record data to a desired format, the desired format including a plurality of fields of standardized scheduling elements of the clinical scheduling system, wherein mapping comprises mapping respective appointment record to corresponding fields of the standardized scheduling elements;

based on the mapping, conforming, with one or more processors of the clinical scheduling system, the clinical record data to standardized scheduling elements of the scheduling system by parsing the clinical record data and reformatting the clinical record data by assigning portions of the data to appropriate fields to form the standardized scheduling elements from the appointment records;

cleansing, with one or more processors of the clinical scheduling system, in a manner configurable by a user, the clinical record data to filter out portions of the clinical record data, wherein cleansing comprises;

determining whether to exclude data of at least some appointment records based on whether the respective appointment records contain an error related to one or more of data errors, data artifacts, or business logic;

after cleansing, providing, with one or more processors of the clinical scheduling system, the standardized scheduling elements to an optimization engine configured to optimize at least one of provider or facility scheduling templates based on the clinical record data;

optimizing, with one or more processors of the clinical scheduling system, the at least one of provider or facility scheduling templates by applying configurable logic to the standardized scheduling elements in order to provide one or more at least partially newly defined optimized scheduling templates that configure at least one of providers or rooms for improved usage of time relative to un-optimized scheduling templates, wherein optimizing comprises;

matching provider availability with customized variables, the customized variables being based on visit complexity, visit length, number of exam rooms, provider preference, or non-physician resources;

optimizing at least some of the scheduling templates for providers matched with the customized variables based on user-defined rules to optimize schedules; and

detecting and resolving a conflict in the user-defined rules to optimize schedules; and

generating, with one or more processors of the clinical scheduling system, one or more communications configured to upload one or more newly defined optimized scheduling templates via an outbound connection back to the scheduling system, wherein;

the one or more hardware processors are further configured by machine-readable instructions to populate a primary fact table of data of the clinical scheduling system surrounded by dimensional tables of the clinical scheduling system, the primary fact table of data including appointment information, subject and provider keys, appointment scheduling start times, and appointment scheduling end times, the dimensional tables including subject dimensional tables and provider dimensional tables;

the one or more hardware processors are further configured by machine-readable instructions to utilize a star schema extensible framework, the star schema extensible framework including one or more fact tables referencing a plurality of dimension tables, the fact tables including one or both of numerical values or information regarding where descriptive information is kept, and the dimension tables including records with attributes to describe the fact data; and

the one or more hardware processors are further configured by machine-readable instructions to run one or more cleansing functions to ignore one or more of data that is incomplete, appointment information that is missing a start time, or appointment information that is missing an end time.

9. The method of claim 8, wherein conforming the clinical record data allows the scheduling system to work with any electronic medical record.

10. The method of claim 8, wherein the dimensional tables include standardized dimensional tables that comprise detail information that includes appointment outcome information having appointment statuses, actual appointment start times, or actual appointment end times, and wherein subject and provider dimensional tables include detail information that includes at least one of subject demographics or provider specialty information.

11. The method of claim 8, wherein the scheduling system includes subject and provider dimensional tables with details that include one or both of subject demographics or provider specialty information.

12. The method of claim 8, wherein the one or more hardware processors are further configured by machine-readable instructions to extract data from the source scheduling database or other source scheduling databases via an OLEDB connection, an ODBC connection, or another application program interface (API).

13. The method of claim 8, wherein the one or more hardware processors are further configured by machine-readable instructions to cleanse data errors including rows of data missing information.

14. The method of claim 8, wherein data that is excluded from the optimization engine is stored for analytics and reporting, and wherein the clinical scheduling system is configured to generate one or more reports on types and amounts of data that are being excluded from analysis, the types and amounts of data that are being excluded from analysis including one or more of data with missing information, data that is not relevant, data that is incomplete, or data that is erroneous.