Fault tolerant method for processing data with human intervention
The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file even in the face of known errors that would otherwise prevent the method from operating. When a fault condition is detected, a fault handler may attempt to fix the faulty data, remove the faulty data from the input data file being processed, or provide the faulty data at a user interface so a human user can intervene. The method and storage devices may continue processing the data regardless of whether human input has been received because the system can bypass or remove the data, thereby keeping the method fault tolerant.
1. A fault-tolerant computer-implemented method of identifying demographic information in a data file, comprising:
(a) receiving the data file containing a plurality of fields of demographic information from a third-party, the data file having unknown or unexpected nomenclatures for one or more fields of the plurality of fields;
(b) analyzing the data file to distinguish between each of the plurality of fields of demographic information;
(c) detecting a data type for one of the plurality of fields of demographic information;
(d) assigning a naive label describing the detected data type of (c);
when the data type for the one or more of the plurality of fields of demographic information is unknown or unexpected, (e) determining whether an exception condition exists based on the unknown or unexpected data type;
when the exception condition is determined, (f) generating a request for human intervention, the generating the request for human intervention comprising:
(I) providing a notification to a user that the request for human intervention has been generated,
(II) causing the human intervention notification to be displayed at a user-interface,
(III) receiving, at the user-interface, a user input in response to the human intervention notification,
(IV) assigning an active label to the unknown or unexpected data type based on the received user input at (III), and
(V) storing one or more of the received user input and the assigned active label in a memory;
(g) normalizing a format of the naive labeled data of (c) and the active labeled data of (IV); and
(h) outputting the normalized data using a pre-specified format.
2. The method of claim 1 , wherein the third-party is a healthcare provider and the data file containing the plurality of fields of demographic information is a medical roster.
3. The method of claim 1 , wherein the data type is detected based on semantic content, a data shape, style, name, or phrase.
4. The method of claim 3 , wherein the data type is determined based on a column type, a column title, a neighboring data type, an active label stored in the memory, or a combination thereof.
5. The method of claim 4 , wherein the determined data type is at least one of an address or phone number.
6. The method of claim 1 , wherein causing the notification to be displayed further comprises suggesting a possible data type for human confirmation.
7. The method of claim 6 , wherein the suggested data type is determined based on a probability that the data type was identified correctly.
8. The method of claim 1 , wherein the assignment of (IV) is used as training data for at least one machine learning model.
9. The method of claim 1 , wherein data stored in (V) is used to determine an additional data type without being used as training data for a machine learning model.
10. The method of claim 1 , wherein the pre-specified format is pre-specified by a health insurance provider.
11. The method of claim 1 , further comprising (i) validating the normalized data.
12. A non-transitory program storage device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform a method, the method comprising:
(a) receiving the data file containing a plurality of fields of demographic information from a third-party, the data file having unknown or unexpected nomenclatures for one or more fields of the plurality of fields;
(b) analyzing the data file to distinguish between each of the plurality of fields of demographic information;
(c) detecting a data type for one of the plurality of fields of demographic information;
(d) assigning a naive label describing the detected data type of (c);
when the data type for the one or more of the plurality of fields of demographic information is unknown or unexpected, (e) determining whether an exception condition exists based on the unknown or unexpected data type;
when the exception condition is determined, (f) generating a request for human intervention, the generating the request for human intervention comprising:
(I) providing a notification to a user that the request for human intervention has been generated,
(II) causing the human intervention notification to be displayed at a user-interface,
(III) receiving, at the user-interface, a user input in response to the human intervention notification,
(IV) assigning an active label to the unknown or unexpected data type based on the received user input at (III), and
(V) storing one or more of the received user input and the assigned active label in a memory;
(g) normalizing a format of the naive labeled data of (c) and the active labeled data of (IV); and
(h) outputting the normalized data using a pre-specified format.
13. The non-transitory program storage device of claim 12 , wherein the data type is detected based on semantic content, a data shape, style, name, or phrase.
14. The non-transitory program storage device of claim 12 , wherein the data type is determined based on a column type, a column title, a neighboring data type, an active label stored in the memory, or a combination thereof.
15. The non-transitory program storage device of claim 12 , wherein causing the notification to be displayed further comprises suggesting a possible data type for human confirmation.
16. The non-transitory program storage device of claim 15 , wherein the suggested data type is determined based on a probability that the data type was identified correctly.
17. The non-transitory program storage device of claim 12 , wherein the assignment of (IV) is used as training data for at least one machine learning model.
18. The non-transitory program storage device of claim 12 , wherein data stored in (V) is used to determine an additional data type without being used as training data for a machine learning model.
19. The non-transitory program storage device of claim 12 , wherein the pre-specified format is pre-specified by a health insurance provider.
20. The non-transitory program storage device of claim 12 , wherein the method further comprises (i) validating the normalized data.