Electronic data interchange tool

Abstract

Systems and methods are provided for processing data for facilitating a data exchange. The method may receive a source data of a source data model from a remote computer system. The method may generate a layout summary of the source data model by analyzing a sample of the source data using one or more large language model tools. The method may map, based on the layout summary of the source data model, fields of the source data model to fields of a destination data model using the one or more large language model tools. The method may perform the data exchange from the source data model to the destination data model based on the mapping.

Description

058440-03-5093-WGElectronic Data Interchange ToolCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 704,405, filed October 7, 2024, the entirety of which is incorporated herein by reference.TECHNICAL FIELD

[0002] The present disclosure generally relates to data processing and management, specifically focusing on data exchange.BACKGROUND

[0003] Seamless integration and efficient data exchange is an important part of business operation across industries. Electronic Data Interchange (EDI) tools allow for structured transmission of data between organizations and are commonly used for business-to-business (B2B) communications. Businesses rely on various EDI tools and solutions to simplify their data exchange processes.

[0004] EDI tools are powerful software applications designed to automate and facilitate the exchange of structured business data between systems, organizations, or trading partners. Such structured business data frequently includes purchase orders, invoices, shipping documents, healthcare claims, etc. These tools streamline the entire data exchange process by ensuring compatibility and consistency in data formats.

[0005] Traditional EDI tools usually require a pre-agreed upon single specific format between business entities for efficient data exchange. For decades, industries have felt the need for a tool that could handle multiple data formats for vendor onboarding without the limitations of the traditional EDI tools. The desire stems from the technical challenges in handling diverse data formats in data exchange and the costs associated with such an endeavor.

[0006] Traditional EDI tools typically require that the data be in a specific structured format. If there's a variance in input data format, companies have to restructure it to fit the EDI system. Traditional EDI systems also require an in-depth understanding of EDI standards and technology. Also, setting up and maintaining a traditional EDI system can be costly, especially for058440-03-5093-WG small and medium businesses. Moreover, vendors often do not get real-time status updates about their transactions, leading to a lack of transparency in the process. Error detection and correction can be arduous in traditional EDI system as issues may be realized only after the transaction has been processed.SUMMARY

[0007] The present disclosure aims to overcome the deficiencies of traditional EDI tools by facilitate data exchange with diverse data formats. Vendors may provide data in a variety of formats, including XML, Flat file, 278, CCF, JSON, FHIR, etc. The chief objective of the present disclosure is to provide a tool that can handle and process all these different formats. Through automation and standardization, the present disclosure seeks to enhance efficiency, accuracy, and accelerates delivery to production, thereby reducing the manual effort required in vendor onboarding.

[0008] In one aspect, a method is provided for facilitating a data exchange. The method may receive a source data of a source data model from a remote computer system. The method may generate a layout summary of the source data model by analyzing a sample of the source data using one or more large language model tools. The method may map, based on the layout summary of the source data model, fields of the source data model to fields of a destination data model using the one or more large language model tools. The method may perform the data exchange from the source data model to the destination data model based on the mapping.

[0009] In some embodiments, the method may further provide a user interface for validating the layout summary. In some embodiments, the providing of the user interface for validating the layout summary may include providing a user interface for collecting high-level layout specifications. In some embodiments, the layout summary of the source data model may include at least one of sections and field-level information. For each section, the layout summary of the source data model may include fields, field length, and an indication of whether the section includes multiple lines.

[0010] In some embodiments, the mapping may include categorizing the fields of the source data model using the one or more large language model tools. The mapping may alsoinclude generating a list of questions based on the fields of the destination data model. The mapping may further include preparing, using the one or more large language model tools, answers to at least a subset of the list of questions based on the categorizing of the fields of the source data model. The mapping may also include generating a specification for the data exchange based on the answers to the list of questions.

[0011] In some embodiments, the performing of the data exchange from the source data model to the destination data model based on the mapping may include performing the data exchange from the source data model to the destination data model based on the specification.

[0012] In some embodiments, the method may further receive a domain for which the data exchange needs to be established. The list of questions may be generated based on the domain.

[0013] In some embodiments, the mapping may further include providing a user interface for validating the categorizing of the fields of the source data model. In some embodiments, the mapping may further include providing a user interface for preparing answers to the list of questions. In some embodiments, the preparing of answers to the list of questions may include receiving validations of the answers to at least a subset of the list of questions prepared by the one or more large language model tools. The preparing of answers to the list of questions may further include receiving answers to one or more questions of the list of questions.

[0014] In some embodiments, the mapping may further include providing the answers to the list of questions to a subject matter module. The mapping may further include receiving feedback regarding the answers to the list of questions from the subject matter expert module.

[0015] In another aspect, a computer system for facilitating a data exchange is provided. The computer system may include one or more processors and a memory. The memory stores one or more programs configured for execution by the one or more processors. The one or more programs may include instructions for receiving a source data of a source data model from a remote computer system. The one or more programs may include instructions for generating a layout summary of the source data model by analyzing a sample of the source data using one or more large language model tools. The one or more programs may further include instructions for mapping, based on the layout summary of the source data model, fields of the source data modelto fields of a destination data model using the one or more large language model tools. The one or more programs may further include instructions for performing the data exchange from the source data model to the destination data model based on the mapping.

[0016] In some embodiments, the one or more programs may further include instructions for providing a user interface for validating the layout summary. In some embodiments, the instructions for providing the user interface for validating the layout summary may include instructions for providing a user interface for collecting high-level layout specifications. In some embodiments, the layout summary of the source data model may include at least one of sections and field-level information. For each section, the layout summary of the source data model may include fields, field length, and an indication of whether the section comprises multiple lines.

[0017] In some embodiments, the instructions for mapping may include instructions for categorizing the fields of the source data model using the one or more large language model tools. The instructions for mapping may further include instructions for generating a list of questions based on the fields of the destination data model. The instructions for mapping may further include instructions for preparing, using the one or more large language model tools, answers to at least a subset of the list of questions based on the categorizing of the fields of the source data model. The instructions for mapping may further include instructions for generating a specification for the data exchange based on the answers to the list of questions.

[0018] In some embodiments, the instructions for performing the data exchange from the source data model to the destination data model based on the mapping may include instructions for performing the data exchange from the source data model to the destination data model based on the specification.

[0019] In some embodiments, the one or more programs may further include instructions for receiving a domain for which the data exchange needs to be established. The list of questions may be generated based on the domain.

[0020] In some embodiments, the instructions for mapping may further include instructions for providing a user interface for validating the categorizing of the fields of the source data model. In some embodiments, the instructions for mapping may further include instructions058440-03-5093-WG for providing a user interface for preparing answers to the list of questions. In some embodiments, the instructions for preparing answers to the list of questions may include instructions for: receiving validations of the answers to at least a subset of the list of questions prepared by the one or more large language model tools; and receiving answers to one or more questions of the list of questions.

[0021] In some embodiments, the instructions for mapping may further include instructions for providing the answers to the list of questions to a subject matter expert module. The instructions for mapping may further include instructions for receiving feedback regarding the answers to the list of questions from the subject matter expert module.BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG. 1 A illustrates an example of a traditional EDI tool.

[0023] FIG. IB illustrates an example of an EDI tool according to some embodiments of the present disclosure.

[0024] FIG. 2 illustrates an example of a layout summary of a data model according to some embodiments of the present disclosure.

[0025] FIG. 3 illustrates an example of the fields of the layout summary to be categorized and the recommended categories according to some embodiments of the present disclosure.

[0026] FIG. 4 illustrates an example of the categorized fields of the layout summary according to some embodiments of the present disclosure.

[0027] FIG. 5 illustrates an example of the list of questions prepared by the EDI tool and the corresponding answers according to some embodiments of the present disclosure.

[0028] FIG. 6 illustrates an example of the questionnaire generated by the EDI tool according to some embodiments of the present disclosure.

[0029] FIG. 7 illustrates an example of the questionnaire with answers to some questions in the questionnaire prepopulated by the artificial intelligence (Al) content generation tool.

[0030] FIG. 8 shows a flowchart of a method for facilitating a data exchange according to some embodiments of the present disclosure.058440-03-5093-WG

[0031] FIG. 9 shows a flowchart of a method for mapping fields of the source data model to fields of the destination data model using Al tools according to some embodiments of the present disclosure.

[0032] FIG. 10 illustrates a block diagram of an EDI tool for data exchange according to some embodiments of the present disclosure.

[0033] FIG. 11 illustrates an example of distributed data warehouse system that may provide data management services to clients.

[0034] FIG. 12 illustrates an example of a computer system on which embodiments described herein may be executed.

[0035] Like reference numerals refer to corresponding parts throughout the drawings.DETAILED DESCRIPTION

[0036] Reference will now be made to various implementations, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention and the described implementations. However, the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the implementations.

[0037] The development of the solution in the present disclosure is a response to the deficiencies of traditional EDI tools. For example, traditional EDI tools require a pre-agreed upon single specific format between business entities for efficient data exchange. This limitation can lead to technical challenges in handling diverse data formats in data exchange and the costs associated with such an endeavor.

[0038] The present disclosure aims to provide a dynamic and efficient approach to facilitate data exchange with diverse data formats. The present disclosure provides the ability to accept data in any format from the vendor or source application, eliminates the need for manual data transformation or adhering to strict format requirements. This flexibility can streamline data integration processes and reduce the effort required for data preparation.058440-03-5093-WG

[0039] By automatically analyzing and mapping the input format to the desired target format, the present disclosure can save significant time and effort compared to manual mapping or coding / testing custom data transformation scripts for each input format. As the number of data sources and formats increases, the effort required for manual mapping and transformation can become overwhelming. The present disclosure can handle a wide range of formats in an automated manner, scale more effectively, and adapt to changing data landscape.

[0040] Manual data transformation processes are prone to human errors, which can lead to data quality issues and downstream problems. An automated mapping approach can minimize such errors and improve data integrity. The present disclosure can facilitate efficient integration between various systems or applications that use different data formats, acting as a universal translator and reducing the need for custom integration efforts for each format combination.

[0041] FIG. 1A illustrates an example of a traditional EDI tool. As illustrated in FIG. 1A, a computer system 102 sends data of a single specific format 105 to a computer system 120. The computer system 120 could be a cloud-based database system (e.g., the distributed data warehouse system 1100 described below with regard to FIG. 11). The computer system 120 may belong to a large organization. The computer system 102 may belong to an entity that has a business relationship (e.g., vendor) with the organization of computer system 120. In order for the computer system 120 to integrate the data of a single specific format 105, an EDI tool 160 converts the data of a single specific format 105 to data of destination data model 115. The destination data model is the data model used by the computer system 120. As a result, the data from the computer system 102 can be successfully integrated into the computer system 120. However, the traditional EDI tool 110 cannot handle diverse data formats.

[0042] FIG. IB illustrates an example of an EDI tool according to some embodiments of the present disclosure. As illustrated in FIG. IB, the computer system 102 sends data of bring- your-format 155 to the computer system 120. The bring-your-format can be formats like XML, 278, JSON, Flat fde, FHIR, etc. In order for the computer system 120 to integrate the data of bring- your-format 155, an EDI tool 160 convert the data of bring-your-format 155 to data of destination data model 165. As a result, the data of diverse formats from the computer system 102 can be successfully integrated into the computer system 120.058440-03-5093-WG

[0043] The EDI tool 160 may have the ability to accept data in any format from the vendor or source application, eliminating the need for manual data transformation or adhering to strict format requirements. This flexibility can streamline data integration processes and reduce the effort required for data preparation. By automatically analyzing and mapping the input format to the desired target format, the EDI tool 160 may save significant time and effort compared to the traditional EDI tool 110. With the traditional EDI tool 110, ss the number of data sources and formats increases, the effort required for manual mapping and transformation can become overwhelming. An EDI tool like the EDI tool 160 that can handle a wide range of formats in an automated manner can scale more effectively and adapt to changing data landscape. Furthermore, manual data transformation processes are prone to human errors, which can lead to data quality issues and downstream problems. An automated mapping approach adopted by the EDI tool 160 can minimize such errors and improve data integrity. The EDI tool 160 may facilitate efficient integration between various systems or applications that use different data formats, acting as a universal translator and reducing the need for custom integration efforts for each format combination.

[0044] By using the EDI tool 160, business partners and vendors of the organization of the computer system 120 may not need to invest any money or resources in developing or maintaining a specific process for sending data to the organization of the computer system 120. Unlike the traditional EDI tool 110 that require a specific data format, the EDI tool 160 may handle multiple formats (like XML, CCF, JSON, FHIR, etc.), thereby enhancing flexibility and reducing data conversion efforts. By automating the mapping between the source data model and the destination data model, the EDI tool 160 may reduce manual intervention, thereby increasing the speed of vendor onboarding process.

[0045] In some embodiments, the EDI tool 160 may provide real-time status updates of the onboarding process, improving transparency and vendor experience. Compared to the traditional EDI tool 110 where error detection and correction can be quite laborious, the EDI tool 160 may be designed with robust error handling capabilities. The automation and flexible data handling capabilities of the EDI tool 160 may potentially reduce the costs involved in data conversion and manual data model mapping.058440-03-5093-WG

[0046] In some embodiments, a user may first log into the EDI tool 160, e.g., via a web application. In some embodiments, the user may select the specific domain for which the data exchange needs to be established. In some embodiments, the specific domain may be a high-level concept such as healthcare. In some embodiments, the specific domain may be more narrowly defined, such as urgent care visit, primary care physician visit, hospital stay, and so on.

[0047] The user may upload a sample of the data of bring-your-format 155. The EDI tool 160 may create a prompt to ask a large language model tool to analyze the sample and provide a layout summary of the data model of the data of bring-your-format 155.

[0048] FIG. 2 illustrates an example of a layout summary 200 of a data model according to some embodiments of the present disclosure. As illustrated in FIG. 2, the layout summary 200 may include the identified sections (e.g., Section 1 through Section M) of the data model of the data of bring-your-format 155. For each section, the layout summary 200 may include the identified fields (e.g., Field 1 through Field N) of the section. For each field, the layout summary 200 may include the identified attributes of the field, which may include field length and whether the field is a multi-line field.

[0049] In some embodiments, the EDI tool 160 may present the user with the layout summary to validate the analysis. In some embodiments, if the user does not accept the layout summary, the EDI tool 160 may present the user with a view to collect high-level layout specifications. In some embodiments, the EDI tool 160 may provide the user with an interface to modify the layout summary. In some embodiments, the process may loop back to the user uploading another sample of the data of bring-your-format 155.

[0050] In some embodiments, the EDI tool 160 may create prompts to ask a large language model tool to categorize the sections or fields of the layout summary. FIG. 3 illustrates an example of the fields of the layout summary to be categorized and the recommended categories according to some embodiments of the present disclosure. As illustrated in FIG. 3, the EDI tool 160 has identified several fields 302 of the data model of the data of bring-your-format 155. The potential categories 304 are recommended. The EDI tool 160 may use the large language model tool to categorize the fields 302 into categories 304.058440-03-5093-WG

[0051] FIG. 4 illustrates an example of the categorized fields of the layout summary according to some embodiments of the present disclosure. As illustrated in FIG. 4, the identified fields 302 of the data model of the data of bring-your-format 155 has been categorized into category 402 by the EDI tool 160. In some embodiments, the EDI tool 160 may also allow the user to validate, correct, or update the field categorization as needed, e g., through user interface 400.

[0052] In some embodiments, the EDI tool 160 may prepare a list of questions based on fields required by the destination data model. In some embodiments, the EDI tool 160 may prepare the list of questions based on the selected domain. FIG. 5 illustrates an example of the list of questions prepared by the EDI tool and the corresponding answers according to some embodiments of the present disclosure. As illustrated in FIG. 5, the EDI tool 160 prepared questions 502 based on the fields of the target data model and the selected domain.

[0053] In some embodiments, the EDI tool 160 may use an artificial intelligence (Al) content generation tool to fill in answers to the questions based on the information collected. In some embodiments, the Al content generation tool may be a large language model tool. As illustrated in FIG. 5, the EDI tool 160 provides answers 505 to the questions 502. In some embodiments, the answers 505 are generated by the Al content generation tool.

[0054] In some embodiments, the EDI tool 160 may present to the user a questionnaire with answers prepopulated by the Al content generation tool. The user may validate or correct the answers, e.g., through user interface 500. The user may also fill in answers that are not provided by the Al content generation tool.

[0055] FIG. 6 illustrates an example of the questionnaire generated by the EDI tool according to some embodiments of the present disclosure. FIG. 7 illustrates an example of the questionnaire with answers to some questions in the questionnaire prepopulated by the Al content generation tool.

[0056] In some embodiments, the questionnaire may be submitted for subject matter expert (SME) review and approval. In some embodiments, the EDI tool 160 may generate a detailed layout specification. In some embodiments, the generation of the detailed layout specification may be based on user and SME inputs. In some embodiments, the detailed layout specification may058440-03-5093-WG represent a mapping of components (e g., sections and / or fields) from a source data model to a destination data model. In some embodiments, the EDI tool 160 may send the completed layout specification to the database administrator or SME for final validation. Once the layout specification is approved, the EDI tool 160 may use the layout specification to convert the data of bring-your-format 155 to the data of destination data model 165, thus enabling the integration of the data from the computer system 102 into the computer system 120.

[0057] FIG. 8 shows a flowchart of a method 800 for facilitating a data exchange according to some embodiments of the present disclosure. The method 800 may be performed by one or more computing devices (e.g., computer system 1200 described below with regard to FIG. 12). In some embodiments, the method 800 may correspond to the operations described above with reference to FIG. IB through FIG. 7.

[0058] In some embodiments, the method 800 may start at 802. At 804, the method 800 may receive source data of the source data model from a remote computer system for data exchange. In some embodiments, the remote computer system may be the computer system 102 described above with regard to FIG. IB, and the source data of the source data model may be the data of bring-your-format 155 described above with regard to FIG. IB. In such embodiments, the method 800 may be performed by the EDI tool 160 described above with regard to FIG. IB.

[0059] At 806, the method 800 may receive a domain for which the data exchange needs to be established. In some embodiments, the received domain may be a high-level concept such as healthcare. In some embodiments, the specific domain can be more narrowly defined, such as urgent care visit, primary care physician visit, hospital stay, and so on. The received domain may be used to determine what questions need to be generated in order to map the source data model to the destination data model, as will be further discussed below with regard to FIG. 9.

[0060] At 808, the method 800 may generate a layout summary of the source data model by analyzing a sample of the source data using one or more large language model tools. FIG. 2 described above illustrates an example of such a layout summary. In some embodiments, the EDI tool 160 may create a prompt to ask a large language model tool to analyze a sample of the data of bring-your-format 155 and provide a layout summary, as described above with regard to FIG. IB. In some embodiments, generating the layout summary may include providing the sample of058440-03-5093-WG the source data to a large language model, and / or instructing the large language model to identify sections, fields, field types, and field lengths. In some embodiments, the large language model may be further instructed to identify potential sensitive or protected information fields. In some embodiments, the large language model may be further instructed to provide a high-level overview of the layout structure and identify any patterns or standardizations in the data.

[0061] In some embodiments, the large language model is provided with specific prompts to guide the layout summary generation. For example, the system may instruct the LLM: "Analyze the following data sample and provide a detailed layout summary. Identify all sections, fields, their types, and lengths. Also, highlight any potential sensitive or protected information fields." The LLM's output is then processed by comparing it against predefined schemas and validating the identified field types against sample data values. The system may, for example, employ transformer-based LLM architectures, such as GPT or BERT variants, fine-tuned on a diverse set of data schemas and layouts relevant to the target domain (e.g., healthcare data structures). This fine-tuning process may involve exposing the model to thousands of example data layouts, allowing it to learn common patterns and structures in domain-specific data.

[0062] At 810, the method 800 may provide a user interface for validating the layout summary. As described above with reference to FIG. IB, the EDI tool 160 may present the user with the layout summary to validate the analysis. In some embodiments, if the user does not accept the layout summary, the EDI tool 160 may present the user with a view to collect high-level layout specifications. In some embodiments, the EDI tool 160 may provide the user with an interface to modify the layout summary. In some embodiments, the user may upload another sample of the data of bring-your-format 155.

[0063] At 812, the method 800 may map, based on the layout summary of the source data model, fields of the source data model to fields of the destination data model using one or more large language model tools. Further details of this operation will be discussed below with reference to FIG. 9.

[0064] At 814, the method 800 may perform the data exchange from the source data model to the destination data model based on the mapping. As described above with reference to FIG. IB, in some embodiments, the EDI tool 160 may generate a detailed layout specification as a result058440-03-5093-WG of the mapping performed at 812. Tn some embodiments, the detailed layout specification may represent a mapping of components (e.g., sections and / or fields) from a source data model to a destination data model. The EDI tool 160 may use the layout specification to convert the data of bring-your-format 155 to the data of destination data model 165, thus enabling the integration of the data from the computer system 102 into the computer system 120. The method 800 then ends at 816.

[0065] FIG. 9 shows a flowchart of a method 900 for mapping fields of the source data model to fields of the destination data model using Al tools according to some embodiments of the present disclosure. The method 900 may be performed by one or more computing devices (e.g., computer system 1200 described below with regard to FIG. 12). In some embodiments, the method 900 may correspond to the operation described above with reference to 812 in FIG. 8.

[0066] In some embodiments, the method 900 may start at 902. At 904, the method 900 may categorize the fields of the source data model using one or more large language model tools. For example, as discussed above with reference to FIGs. IB, 3, and 4, the EDI tool 160 may use the large language model tool to categorize fields 302 into category 402. In some embodiments, the EDI tool 160 may create prompts to ask a large language model tool to categorize the sections or fields of the layout summary. In some embodiments, the fields of the source data model may be obtained by analyzing a sample of the source data using one or more large language model tools, as described above with reference to FIGs. IB and 8.

[0067] In some embodiments, categorizing the fields of the source data model may include instructing a large language model to assign categories to each field and to provide a confidence level for each categorization. In some embodiments, the large language model may be further instructed to identifying fields that could belong to multiple categories. In some embodiments, the large language model may be further instructed to provide a brief explanation for each field categorization. The field categories used may include, for example, “Personal Identifiable Information,” “Medical History,” “Billing Information,” “Diagnostic Codes,” and “Treatment Plans.” For example, a field named “patient ssn” might be categorized as “Personal Identifiable Information” with a high confidence level. The system may interpret confidence levels on a scale of 0 to 1, with levels above 0.8 considered high confidence, for example. For fields with multiple058440-03-5093-WG potential categories, the system may retain up to three top categories if their confidence levels are within 0.1 of each other. For instance, a field named “medication_cost” may be categorized as both "Billing Information" (e.g., 0.85 confidence) and “Treatment Plans” (e.g., 0.79 confidence).

[0068] At 906, the method 900 may provide a user interface for validating the categorizing of the fields of the source data model. For example, as discussed above with reference to FIGs. IB and 4, the EDI tool 160 may allow the user to validate, correct, or update the field categorization as needed, e.g., through user interface 400.

[0069] At 908, the method 900 may generate a list of questions based on the fields of the destination data model and the domain. For example, as discussed above with reference to FIGs. IB and 5, the EDI tool 160 may prepare questions 502 based on the fields of the target data model and the selected domain. In some embodiments, generating the list of questions may include providing the large language model with the domain (e.g., a target healthcare domain) and field categorization results, and / or instructing the large language model to generate questions that validate field categorizations, gather missing information, and clarify ambiguities. In some embodiments, the large language model may be further instructed to specify the type of expected answer for each generated question. In some embodiments, the large language model may be further instructed to ensure the generated questions are clear, concise, and relevant to establishing an effective data exchange system in the specified healthcare domain.

[0070] Examples of generated questions include “How should the ‘patient id’ field from the source model be mapped to the destination model?” or “Does the ‘diagnosis’ field in the source model correspond to ICD-10 codes in the destination model?" and so on. The system may help ensure relevance by providing the LLM with context about the specific healthcare sub-domain (e.g., “This is for an emergency room data transfer system”), and / or by instructing the LLM to generate questions that address potential discrepancies and / or ambiguities between source and destination models. Generated questions may undergo a filtering process where they are compared against a predefined list of critical mapping aspects for the given domain, for example. Questions that do not address these critical aspects may be either refined through a follow-up LLM interaction or discarded. This helps ensure, for example, that the final set of questions covers different aspects of the data mapping process.058440-03-5093-WG

[0071] At 910, the method 900 may prepare, using one or more large language model tools, answers to at least a subset of the list of questions. For example, as discussed above with reference to FIGs. IB and 5, the EDI tool 160 may provide answers 505 to questions 502 using an Al content generation tool. In some embodiments, the Al content generation tool may be a large language model tool. In some embodiments, preparing answers to the list of questions may include providing the large language model with the domain (e.g., healthcare domain), data layout summary, and field categorizations, and instructing the large language model to generate content that reflects previously collected information and addresses domain-specific challenges. In some embodiments, the large language model may be further requested to provide explanations for any assumptions made in content generation. In some embodiments, the large language model may be further instructed to use domain-specific terminology correctly and maintain consistency with standard practices in healthcare data management.

[0072] An example of a prepared answer may be “The ‘patient id’ field from the source model should be mapped to the ‘member identifier’ field in the destination model. Both are unique alphanumeric identifiers, but the destination model uses a different naming convention.” The system may help ensure correct use of domain-specific terminology by providing the LLM with a comprehensive healthcare glossary and / or by instructing the LLM to prefer terms from this glossary in its responses. After the LLM generates answers, the answers may undergo a postprocessing step. This includes a terminology check against the domain glossary, a consistency check against previously established mapping rules, and / or a validation against sample data from both source and destination models. Any inconsistencies or potential errors flagged during this process may be sent for human expert review.

[0073] At 912, the method 900 may provide a user interface for preparing answers to the list of questions. For example, as discussed above with reference to FIGs. IB and 5, the EDI tool 160 may present to the user a questionnaire with answers prepopulated by the Al content generation tool. The user may validate or correct the answers, e.g., through user interface 500.

[0074] At 914, the method 900 may provide the answers to the list of questions to a subject matter expert module. The subject matter expert module may review the answers and provide058440-03-5093-WG feedback accordingly. At 916, the method 900 may receive feedback regarding the answers to the list of questions from the subject matter expert module.

[0075] At 918, the method 900 may generate a specification for the data exchange based on the answers to the list of questions. For example, as discussed above with reference to FIG. IB, the EDI tool 160 may generate a detailed layout specification. In some embodiments, the generation of the detailed layout specification may be based on user and SME inputs. In some embodiments, the detailed layout specification may represent a mapping of components (e.g., sections and / or fields) from a source data model to a destination data model.

[0076] The method 900 then ends at 920. For example, as discussed above with reference to FIG. IB, the EDI tool 160 may use the layout specification to convert the data of bring-your- format 155 to the data of destination data model 165, thus enabling the integration of the data from the computer system 102 into the computer system 120.

[0077] For error handling and as fallback mechanisms, in cases where an LLM output is deemed unsuitable (e.g., low confidence scores, inconsistencies with established rules), the system may employ a fallback mechanism. This may involve, for example, re-querying the LLM with more specific instructions, consulting a rule-based expert system for common mapping scenarios, and / or flagging the issue for human expert review. For maintaining consistency across multiple LLM interactions in the workflow, the system may maintain a context vector that is updated after each successful LLM interaction. This context vector may include, for example, key decisions made, established mappings, and / or important domain-specific details. The context vector may be provided to the LLM in subsequent interactions, allowing the model to maintain consistency with previous outputs and decisions. The LLMs used may undergo periodic fine-tuning to improve their performance in the specific domain of healthcare data mapping. This fine-tuning process may involve, for example, training on a curated dataset of successful mapping examples, expert- validated questions and answers, and domain-specific terminology. The fine-tuning may be performed using techniques, such as transfer learning and few-shot learning to maximize the model’s adaptation to the specific use case while minimizing the required amount of training data.

[0078] FIG. 10 illustrates a block diagram of an EDI tool 1000 for data exchange according to some embodiments of the present disclosure. In some embodiments, the functions of the EDI058440-03-5093-WG tool 1000 may be performed by one or more computing devices (e.g., computer system 1200 described below with regard to FIG. 12). As shown in FIG. 10, the EDI tool 1000 receives data of bring-your-format 1002 and converts it into data of destination data model 1035. In some embodiments, the EDI tool 1000 may be the EDI tool 160 described above with reference to FIG. IB. In some embodiments, the data of bring-your-format 1002 and the data of destination data model 1035 may be the data of bring-your-format 155 and the data of destination data model 165 described above with reference to FIG. IB, respectively.

[0079] In some embodiments, the EDI tool 1000 may include a data model summarizer 1010. The data model summarizer 1010 may generate a layout summary of the source data model by analyzing a sample of the data of bring-your-format 1002 using one or more large language model tools. FIG. 2 described above illustrates an example of such a layout summary. In some embodiments, the data model summarizer 1010 may create a prompt 1012 to ask a large language model tool 1030 to analyze a sample of the data of bring-your-format 1002 and provide a layout summary 1014. In some embodiments, the data model summarizer 1010 may perform the operation described above with regard to 808 in FIG. 8. The large language model tool 1030 may include one or more large language model tools.

[0080] In some embodiments, the EDI tool 1000 may include a data model mapper 1020. The data model mapper 1020 may map, based on the layout summary provided by the data model summarizer 1010, fields of the source data model to fields of the destination data model using the large language model tool 1030. In some embodiments, the data model mapper 1020 may perform the operation described above with regard to 812 in FIG. 8.

[0081] In some embodiments, the data model mapper 1020 may include a field classifier 1021. The field classifier 1021 may categorize the fields of the source data model using the large language model tool 1030. In some embodiments, the field classifier 1021 may perform the operation described above with regard to 904 in FIG. 9.

[0082] In some embodiments, the data model mapper 1020 may include a questionnaire generator 1022. The questionnaire generator 1022 may generate a list of questions based on the fields of the destination data model and the targeted domain. In some embodiments, the058440-03-5093-WG questionnaire generator 1022 may perform the operation described above with regard to 908 in FIG. 9.

[0083] In some embodiments, the data model mapper 1020 may include a questionnaire response module 1023. The questionnaire response module 1023 may prepare, using the large language model tool 1030, answers to at least a subset of the list of questions generated by the questionnaire generator 1022. In some embodiments, the questionnaire response module 1023 may perform the operation described above with regard to 910 in FIG. 9.

[0084] In some embodiments, the data model mapper 1020 may include a subject matter expert module 1024. The subject matter expert module 1024 may review the answers prepared by the questionnaire response module 1023 and provide feedback accordingly.

[0085] In some embodiments, the data model mapper 1020 may include a specification generator 1025. The specification generator 1025 may generate a specification for the data exchange based on the answers prepared by the questionnaire response module 1023. In some embodiments, the specification generator 1025 may perform the operation described above with regard to 918 in FIG. 9.

[0086] In some embodiments, various clients (or customers, organizations, entities, or users) may wish to store and manage data using a data management service. FIG. 11 illustrates an example of distributed data warehouse system 1100 that may provide data management services to clients. Specifically, data warehouse clusters may respond to store requests (e.g., to write data into storage) or queries for data (e.g., such as a Server Query Language request (SQL) for select data), along with many other data management or storage services.

[0087] Multiple users or clients may access a data warehouse cluster to obtain data warehouse services. In some embodiments, clients which may include users, client applications, and / or data warehouse service subscribers. In one example, each of the clients 1110a through 11 lOn is able to access data warehouse cluster 1130 and 1135 respectively in the distributed data warehouse service 1120. Distributed data warehouse cluster 1130 and 1135 may include two or more nodes on which data may be stored on behalf of the clients 1110a through 1 1 lOn who have access to those clusters.058440-03-5093-WG

[0088] A client, such as one of clients 1110a through 111 On, may communicate with a data warehouse cluster 1130 or 1135 via a desktop computer, laptop computer, tablet computer, personal digital assistant, mobile device, server, or any other computing system or other device, such as computer system 1200 described below with regard to FIG. 12, configured to send requests to the data warehouse clusters 1130 and 1135, and / or receive responses from the distributed data warehouse clusters 1130 and 1135. Such requests, for example, may be formatted as a message that includes parameters and / or data associated with a particular function or service offered by a data warehouse cluster. Such a message may be formatted according to a particular markup language such as Extensible Markup Language (XML), and / or may be encapsulated using a protocol such as Simple Object Access Protocol (SOAP). Application programmer interfaces (APIs) may be implemented to provide standardized message formats for clients, such as for when clients are communicating with distributed data warehouse service manager 1122.

[0089] Clients 1110a through l l lOn may communicate with distributed data warehouse clusters 1130 and 1135, hosted by distributed data warehouse service 1120 using a variety of different communication methods, such as over Wide Area Network (WAN) 1105 (e.g., the Internet). Private networks, intranets, and other forms of communication networks may also facilitate communication between clients and data warehouse clusters. A client may assemble a message including a request and convey the message to a network endpoint (e.g., a Uniform Resource Locator (URL)) corresponding to the data warehouse cluster). For example, a client 1110a may communicate via a desktop computer running a local software application, such as a web-client, which may be configured to send hypertext transfer protocol (HTTP) requests to data warehouse cluster 1130 over WAN 1105. Responses or other data sent to clients may be formatted in similar ways.

[0090] In at least some embodiments, a distributed data warehouse service 1120 may host distributed data warehouse clusters, such as clusters 1130 and 1135. The distributed data warehouse service 1120 may provide network endpoints to the storage clients 1110a to 11 lOn of the clusters which allow the clients 1110a through 111 On to send requests and other messages directly to a particular cluster. As noted above, network endpoints, for example may be a particular network address, such as a URL, which points to a particular cluster. For example, client 1110a058440-03-5093-WG may be given the network endpoint “http: / / mycluster.com” to send various request messages to. Multiple storage clients (or users of a particular storage client) may be given a network endpoint for a particular cluster. Various security features may be implemented to prevent unauthorized users from accessing the clusters. Conversely, a client may be given network endpoints for multiple clusters.

[0091] Distributed data warehouse clusters, such as data warehouse cluster 1130 and 1135, may be made up of one or more nodes. These clusters may include different numbers of nodes. A node may be a server, desktop computer, laptop, or, more generally any other computing device, such as those described below with regard to computer system 1200 in FIG. 12. In some embodiments, the number of nodes in a data warehouse cluster may be modified, such as by a cluster scaling request. Clusters may be configured to receive requests and other communications over WAN 1105 from storage clients, such as clients 1110a through l l lOn. A cluster may be configured to receive requests from multiple clients via the network endpoint of the cluster.

[0092] In some embodiments, distributed data warehouse service 1120 may be implemented as part of a web service that allows users to set up, operate, and scale a data warehouse in a cloud computing environment. The data warehouse clusters hosted by the web service may provide an enterprise-class database query and management system that allows users to scale the clusters, such as by sending a cluster scaling request to a cluster control interface implemented by the web-service. Scaling clusters may allow users of the web service to perform their data warehouse functions, such as fast querying capabilities over structured data, integration with various data loading and ETL (extract, transform, and load) tools, client connections with best-in-class business intelligence (BI) reporting, data mining, and analytics tools, and optimizations for very fast execution of complex analytic queries such as those including multitable joins, sub-queries, and aggregation, more efficiently.

[0093] In various embodiments, distributed data warehouse service 1120 may provide clients (e.g., subscribers to the data warehouse service provided by the distributed data warehouse system) with data storage and management resources that may be created, configured, managed, scaled, and terminated in response to requests from the storage client. For example, in some embodiments, distributed data warehouse service 1120 may provide clients of the system with datawarehouse clusters composed of virtual compute nodes. These virtual compute nodes may be nodes implemented by virtual machines, such as hardware virtual machines, or other forms of software implemented to simulate hardware configurations. Virtual nodes may be configured to perform the same tasks, functions, and / or services as nodes implemented on physical hardware.

[0094] Distributed data warehouse service 1120 may be implemented by a large collection of computing devices, such as customized or off-the-shelf computing systems, servers, or any other combination of computing systems or devices, such as the various types of devices described below with regard to FIG. 12. Different subsets of these computing devices may be controlled by distributed data warehouse service manager 1122. Distributed data warehouse service manager 1122, for example, may provide a cluster control interface to clients, such as clients 1 110a through l l lOn, or any other clients or users who wish to interact with the distributed data warehouse clusters managed by the distributed data warehouse manager 1122, which in this example illustration would be data warehouse clusters 1130 and 1135. For example, distributed data warehouse service manager 1122 may generate one or more graphical user interfaces (GUIs) for storage clients, which may then be utilized to select various control functions offered by the control interface for the data warehouse clusters hosted in the distributed data warehouse service 1120.

[0095] Embodiments described herein may be executed on one or more computer systems, which may interact with various other devices. One such computer system is illustrated by FIG. 12. In different embodiments, computer system 1200 may be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device.

[0096] In the illustrated embodiment, computer system 1200 includes one or more processors 1210 coupled to a system memory 1220 via an input / output (I / O) interface 1230. Computer system 1200 further includes a network interface 1240 coupled to I / O interface 1230, and one or more input / output devices 1250, such as cursor control device 1260, keyboard 1270, and display(s) 1280. Display(s) 1280 may include standard computer monitor(s) and / or other058440-03-5093-WG display systems, technologies or devices. In at least some implementations, the input / output devices 1250 may also include a touch- or multi-touch enabled device such as a pad or tablet via which a user enters input via a stylus-type device and / or one or more digits. In some embodiments, it is contemplated that embodiments may be implemented using a single instance of computer system 1200, while in other embodiments multiple such systems, or multiple nodes making up computer system 1200, may be configured to host different portions or instances of embodiments. For example, in one embodiment some elements may be implemented via one or more nodes of computer system 1200 that are distinct from those nodes implementing other elements.

[0097] In various embodiments, computer system 1200 may be a uniprocessor system including one processor 1210, or a multiprocessor system including several processors 1210 (e.g., two, four, eight, or another suitable number). Processors 1210 may be any suitable processor capable of executing instructions. For example, in various embodiments, processors 1210 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 1210 may commonly, but not necessarily, implement the same ISA.

[0098] In some embodiments, at least one processor 1210 may be a graphics processing unit. A graphics processing unit or GPU may be considered a dedicated graphics-rendering device for a personal computer, workstation, game console or other computing or electronic device. Modem GPUs may be very efficient at manipulating and displaying computer graphics, and their highly parallel structure may make them more effective than typical CPUs for a range of complex graphical algorithms. For example, a graphics processor may implement a number of graphics primitive operations in a way that makes executing them much faster than drawing directly to the screen with a host central processing unit (CPU). In various embodiments, graphics rendering may, at least in part, be implemented by program instructions configured for execution on one of, or parallel execution on two or more of, such GPUs. The GPU(s) may implement one or more application programmer interfaces (APIs) that permit programmers to invoke the functionality of the GPU(s).

[0099] System memory 1220 may be configured to store program instructions and / or data accessible by processor 1210. In various embodiments, system memory 1220 may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile / Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing desired functions, such as those described above for scaling computing clusters in distributed systems as described herein are shown stored within system memory 1220 as program instructions 1225 and data storage 1235, respectively. In other embodiments, program instructions and / or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory 1220 or computer system 1200. Generally speaking, a computer-accessible medium may include storage media or memory media such as magnetic or optical media, e.g., disk or CD / DVD-ROM coupled to computer system 1200 via I / O interface 1230. Program instructions and data stored via a computer-accessible medium may be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and / or a wireless link, such as may be implemented via network interface 1240.

[0100] In one embodiment, I / O interface 1230 may be configured to coordinate I / O traffic between processor 1210, system memory 1220, and any peripheral devices in the device, including network interface 1240 or other peripheral interfaces, such as input / output devices 1250. In some embodiments, I / O interface 1230 may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 1220) into a format suitable for use by another component (e.g., processor 1210). In some embodiments, I / O interface 1230 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I / O interface 1230 may be split into two or more separate components, such as a north bridge and a south bridge, for example. In addition, in some embodiments some or all of the functionality of I / O interface 1230, such as an interface to system memory 1220, may be incorporated directly into processor 1210.058440-03-5093-WG

[0101] Network interface 1240 may be configured to allow data to be exchanged between computer system 1200 and other devices attached to a network, such as other computer systems, or between nodes of computer system 1200. In various embodiments, network interface 1240 may support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via telecommunications / telephony networks such as analog voice networks or digital fiber communications networks; via storage area networks such as Fibre Channel SANs, or via any other suitable type of network and / or protocol.

[0102] Input / output devices 1250 may, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer system 1200. Multiple input / output devices 1250 may be present in computer system 1200 or may be distributed on various nodes of computer system 1200. In some embodiments, similar input / output devices may be separate from computer system 1200 and may interact with one or more nodes of computer system 1200 through a wired or wireless connection, such as over network interface 1240.

[0103] As shown in FIG. 12, memory 1220 may include program instructions 1225, configured to provide time-based item recommendations for a scheduled delivery orders as described herein, and data storage 1235, comprising various data accessible by program instructions 1225. In one embodiment, program instructions 1225 may include software elements of embodiments as described herein and as illustrated in the Figures. Data storage 1235 may include data that may be used in embodiments. In other embodiments, other or different software elements and data may be included.

[0104] Those skilled in the art will appreciate that computer system 1200 is merely illustrative and is not intended to limit the scope of the stereo drawing techniques as described herein. In particular, the computer system and devices may include any combination of hardware or software that can perform the indicated functions, including a computer, personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, network device, internet appliance, PDA, wireless phones, pagers, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device suchas a switch, modem, router, or in general any type of computing or electronic device. Computer system 1200 may also be connected to other devices that are not illustrated, or instead may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided and / or other additional functionality may be available.[00105J Those skilled in the art will also appreciate that, while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components may execute in memory on another device and communicate with the illustrated computer system via inter-computer communication. Some or all of the system components or data structures may also be stored (e.g., as instructions or structured data) on a computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some embodiments, instructions stored on a computer-accessible medium separate from computer system 1200 may be transmitted to computer system 1200 via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network and / or a wireless link. Various embodiments may further include receiving, sending or storing instructions and / or data implemented in accordance with the foregoing description upon a computer-accessible medium. Accordingly, the present invention may be practiced with other computer system configurations.

[0106] It is noted that any of the distributed system embodiments described herein, or any of their components, may be implemented as one or more web services. For example, leader nodes within a data warehouse system may present data storage services and / or database services to clients as web services. In some embodiments, a web service may be implemented by a software and / or hardware system designed to support interoperable machine-to-machine interaction over a network. A web service may have an interface described in a machine-processable format, such as the Web Services Description Language (WSDL). Other systems may interact with the web service in a manner prescribed by the description of the web service's interface. For example, the web058440-03-5093-WG service may define various operations that other systems may invoke, and may define a particular application programming interface (API) to which other systems may be expected to conform when requesting the various operations.

[0107] In various embodiments, a web service may be requested or invoked through the use of a message that includes parameters and / or data associated with the web services request. Such a message may be formatted according to a particular markup language such as Extensible Markup Language (XML), and / or may be encapsulated using a protocol such as Simple Object Access Protocol (SOAP). To perform a web services request, a web services client may assemble a message including the request and convey the message to an addressable endpoint (e.g., a Uniform Resource Locator (URL)) corresponding to the web service, using an Internet-based application layer transfer protocol such as Hypertext Transfer Protocol (HTTP).

[0108] In some embodiments, web services may be implemented using Representational State Transfer (“RESTful”) techniques rather than message-based techniques. For example, a web service implemented according to a RESTful technique may be invoked through parameters included within an HTTP method such as PUT, GET, or DELETE, rather than encapsulated within a SOAP message.

[0109] The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various implementations with various modifications as are suited to the particular use contemplated.

Claims

What is claimed is:

1. A method for facilitating a data exchange, comprising: receiving a source data of a source data model from a remote computer system; receiving a domain for which the data exchange needs to be established; generating a layout summary of the source data model by analyzing a sample of the source data using one or more large language model tools, wherein the layout summary of the source data model comprises at least one of sections and field-level information, wherein, for each section, the layout summary of the source data model comprises fields, field length, and an indication of whether the section comprises multiple lines, wherein generating the layout summary comprises: (i) providing the sample of the source data to a large language model; and (ii) instructing the large language model to identify sections, fields, field types, and field lengths; providing a first user interface for validating the layout summary, wherein the providing the first user interface for validating the layout summary comprises providing the first user interface for collecting high-level layout specifications; mapping, based on the layout summary of the source data model, fields of the source data model to fields of a destination data model using the one or more large language model tools, wherein the mapping comprises: categorizing the fields of the source data model using the one or more large language model tools, wherein categorizing the fields of the source data model comprises instructing a large language model to assign categories to each field and to provide a confidence level for each categorization; providing a second user interface for validating the categorizing of the fields of the source data model; generating a list of questions based on the fields of the destination data model and the domain, wherein generating the list of questions comprises: (i) providing the large language model with the domain and field categorization results, and (ii) instructing the large language model to generate questions that validate field categorizations, gather missing information, and clarify ambiguities;preparing, using the one or more large language model tools, answers to at least a subset of the list of questions based on the categorizing of the fields of the source data model, wherein preparing answers to the list of questions comprises: (i) providing the large language model with the domain, data layout summary, and field categorizations, and (ii) instructing the large language model to generate content that reflects previously collected information and addresses domain-specific challenges; providing a third user interface for preparing answers to the list of questions, wherein the preparing of answers to the list of questions comprises: receiving validations of the answers to at least a subset of the list of questions prepared by the one or more large language model tools; and receiving answers to one or more questions of the list of questions; providing the answers to the list of questions to a subject matter expert module; receiving feedback regarding the answers to the list of questions from the subject matter expert module; and generating a specification for the data exchange based on the answers to the list of questions; and performing the data exchange from the source data model to the destination data model based on the mapping, wherein the performing of the data exchange from the source data model to the destination data model based on the mapping comprises performing the data exchange from the source data model to the destination data model based on the specification.

2. A method for facilitating a data exchange, comprising: receiving a source data of a source data model from a remote computer system; generating a layout summary of the source data model by analyzing a sample of the source data using one or more large language model tools; mapping, based on the layout summary of the source data model, fields of the source data model to fields of a destination data model using the one or more large language model tools; and performing the data exchange from the source data model to the destination data model based on the mapping.

3. The method of claim 2, further comprising providing a user interface for validating the layout summary.

4. The method of claim 3, wherein the providing the user interface for validating the layout summary comprises providing a user interface for collecting high-level layout specifications.

5. The method of claim 2, wherein the layout summary of the source data model comprises at least one of sections and field-level information, wherein, for each section, the layout summary of the source data model comprises fields, field length, and an indication of whether the section comprises multiple lines.

6. The method of claim 5, wherein generating the layout summary comprises: (i) providing the sample of the source data to a large language model; and (ii) instructing the large language model to identify sections, fields, field types, and field lengths.

7. The method of claim 6, wherein the large language model is further instructed to identify potentially sensitive or protected information fields.

8. The method of claim 6, wherein the large language model is further instructed to provide a high-level overview of the layout summary and identify any patterns or standardizations in the data.

9. The method of claim 2, wherein the mapping comprises: categorizing the fields of the source data model using the one or more large language model tools; generating a list of questions based on the fields of the destination data model; preparing, using the one or more large language model tools, answers to at least a subset of the list of questions based on the categorizing of the fields of the source data model; and generating a specification for the data exchange based on the answers to the list of questions.

10. The method of claim 9, wherein categorizing the fields of the source data model comprises instructing a large language model to assign categories to each field and to provide a confidence level for each categorization.

11. The method of claim 10, wherein the large language model is further instructed to identifying fields that could belong to multiple categories.

12. The method of claim 10, wherein the large language model is further instructed to provide a brief explanation for each field categorization.

13. The method of claim 9, wherein generating the list of questions comprises providing a large language model with a domain and field categorization results, and instructing the large language model to generate questions that validate field categorizations, gather missing information, and clarify ambiguities.

14. The method of claim 13, wherein the large language model is further instructed to specify the type of expected answer for each generated question.

15. The method of claim 13, wherein the large language model is further instructed to ensure that the generated questions are clear, concise, and relevant to establishing an effective data exchange system in the domain.

16. The method of claim 9, wherein preparing answers to the list of questions comprises providing a large language model with a domain, data layout summary, and field categorizations, and instructing the large language model to generate content that reflects previously collected information and addresses domain-specific challenges.

17. The method of claim 16, wherein the large language model is further requested to provide explanations for any assumptions made in content generation.

18. The method of claim 16, wherein the large language model is further instructed to use domain-specific terminology correctly and maintain consistency with standard practices in healthcare data management.

19. The method of claim 9, wherein the performing of the data exchange from the source data model to the destination data model based on the mapping comprises performing the data exchange from the source data model to the destination data model based on the specification.

20. The method of claim 9, further comprising receiving a domain for which the data exchange needs to be established, wherein the list of questions is generated based on the domain.

21. The method of claim 9, wherein the mapping further comprises providing a user interface for validating the categorizing of the fields of the source data model.

22. The method of claim 9, wherein the mapping further comprises: providing a user interface for preparing answers to the list of questions, wherein the preparing of answers to the list of questions comprises: receiving validations of the answers to at least a subset of the list of questions prepared by the one or more large language model tools; and receiving answers to one or more questions of the list of questions.

23. The method of claim 9, wherein the mapping further comprises: providing the answers to the list of questions to a subject matter expert module; and receiving feedback regarding the answers to the list of questions from the subject matter expert module.

24. A computer system for facilitating a data exchange, comprising: one or more processors; and a memory, wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprising instructions for performing the method of any of claims 1-23.

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