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Proposed Process

warning

For reference only. This process will occur outside of I2C's scope. This is included here for reference for the AANP team.

Overview​

This document outlines the proposed matching process for data imports from the National Provider Identifier (NPI) and State Board of Nursing (SBON) datasets into the Research department's NP Demographics database. The objective is to streamline the matching process, improve data quality, and reduce the creation of duplicate records. Additionally, this document includes ideas for a user interface (UI) to facilitate the matching process.

The NP Demographics database serves as a central repository for NP workforce data, supporting research, strategic planning, and compliance initiatives. By enhancing the matching process and developing a user-friendly interface, we aim to optimize the data import process and ensure the accuracy and integrity of the data.

Initially, the database will contain names, addresses, license numbers, and NPI numbers. Additional fields may be added as needed to support research and analysis. Note that previously imported files will not have those fields populated.

Salesforce will be used to store only the essential fields needed to identify the person as part of the NP workforce. This will be a separate object from the main Salesforce Person object.

Proposed Matching Process​

Step 1: Data Preparation and Cleaning​

  1. Data Ordering:

    • Collect NPI and SBON data files.
    • Organize the data into optimized batches for processing.

  2. Data Cleaning:

    • Remove any syntax errors or inconsistencies in the raw data.
    • Ensure that the data is in a standardized format for easier matching.

Step 2: Initial Matching Algorithms​

Matching would be completed against the NP Demographics database.

  1. Automated Matching:

    • Run initial matching algorithms to identify perfect and possible matches based on key data points (combinations of NPI number, license number, name, address).

  2. Scoring Matches:

    • Assign a score to each possible match based on the confidence level of the match.
    • Categorize matches into high-score (perfect), medium-score (possible), and low-score (unlikely) matches.

Step 3: Review and Validation​

  1. High-Score Matches:

    • Automatically approve high-score matches and link them to existing records in the NP Demographics database.

  2. Medium and Low Score Matches:

    • Queue medium and low score matches for manual review by internal users.
    • Provide tools for internal users to validate and resolve these matches in an efficient manner.

Step 4: Data Import to NP Demographics Database​

  1. Import Matched Records:
    • Import updates to existing records, tagging name and address changes where applicable.
    • Import unmatched records as new records in the NP Demographics database.

Step 5: Salesforce Record Matching​

  1. Automated Matching:

    • Only runs on new records added to the NP Demographics Datbase in previous step.
    • Run initial matching algorithms to identify perfect matches based on key data points (combinations of NPI number, license number, name, address).

  2. Scoring Matches:

    • Do not score matches, only import perfect matches.

Step 6: Data Import to Salesforce​

  1. Import Matched Records:

    • Import only the matched records into Salesforce, including only essential fields used to identify the person is in the NP workforce.
    • Ensure that no new records are created for unmatched items.

  2. Validation and Verification:

    • Validate and verify the imported data to ensure accuracy.

Proposed User Interface (UI) for Matching Process​

Dashboard​

A dashboard of the NP Demographics database will be created to provide an overview of the data import process and key metrics.

  • Overview: Provide a dashboard with an overview of the data import process, including the number of records processed, matched, records flagged for review, and how many records have been manually processed by a staff member.
  • Metrics: Display key metrics such as processing times, match rates, and outstanding items.

Matching Review Screen​

  1. Match Details:

    • Display details of the potential match, including NPI number, license number, name, address, and other relevant data points.
    • Show the confidence score for each match.

  2. Review Tools:

    • Provide tools for internal users to validate and resolve medium and low score matches.
    • Allow users to accept, reject or flag matches for further investigation.

  3. Batch Processing:

    • Enable batch processing for medium to low score matches to streamline the review process.
    • Allow users to skip and return to matches as needed.
------------------------------------------------------------
| Matching Review Dashboard                                 |
------------------------------------------------------------
| Record Details        | Possible Match 1    | Possible Match 2    |
------------------------------------------------------------
| Name:                 | Name:               | Name:               |
| Address:              | Address:            | Address:            |
| License Number:       | License Number:     | License Number:     |
| NPI Number:           | NPI Number:         | NPI Number:         |
| Score: 85%            | Score: 85%          | Score: 75%          |
------------------------------------------------------------
| Actions:              | Actions:            | Actions:            |
| [Accept] [Reject]     | [Accept] [Reject]   | [Accept] [Reject]   |
| [Flag]                | [Flag]              | [Flag]              |
------------------------------------------------------------
| Navigation:           |                     |
| [< Previous] [Next >] |                     |
------------------------------------------------------------

Data Import Screen​

  1. Import Summary:

    • Display a summary of the matched records ready for import into the CRM system.
    • Show the fields that will be updated or added for each record.

  2. Import Actions:

    • Provide options to proceed with the import or make adjustments as needed.
    • Allow users to validate and verify the data before finalizing the import.
------------------------------------------------------------
| Data Import Summary                                      |
------------------------------------------------------------
| Matched Records: 150                                     |
------------------------------------------------------------
| Record Details        | Fields to be Updated             |
------------------------------------------------------------
| Name: John Doe        | Board State: CA                  |
| NPI Number: 123456789 | License Number: 987654321        |
| Address: 123 Main St  | Year: 2024                       |
------------------------------------------------------------
| Actions:                                                 |
| [Proceed with Import] [Adjust Fields] [Validate Data]    |
------------------------------------------------------------

Benefits of the Proposed Process​

  1. Improved Data Quality:

    • Ensures that only accurate and verified records are imported into the NP Demographics database and Salesforce.
    • Reduces the creation of duplicate records.

  2. Operational Efficiency:

    • Streamlines the data import process, making it more efficient and manageable.
    • Frees up staff resources by automating the matching and validation process.

  3. Compliance Assurance:

    • Simplifies compliance with state-specific regulations by ensuring that only validated records are imported and used.

  4. Enhanced Strategic Insights:

    • Provides accurate data for analyzing trends in the NP population, supporting strategic planning and research initiatives.

Additional Considerations​

  1. Merge and Deduplication:

    • Implement a merge and deduplication process to handle any potential duplicates within the NP Demographics database and Salesforce.

  2. Data Privacy and Security:

    • Ensure that all data handling processes comply with data privacy regulations and maintain the security of sensitive information.

Conclusion​

The proposed matching process for data imports from NPI and SBON datasets aims to improve data quality, ensure compliance, and optimize operational efficiency. By implementing these enhancements and developing a user-friendly interface, we can streamline the matching process, reduce duplicate records, and provide valuable insights for strategic planning and research.

Next Steps​

  1. Develop UI Mockups: Create detailed UI mockups based on the proposed design.
  2. Implement Matching Algorithms: Develop and test the initial matching algorithms.
  3. Pilot Testing: Conduct pilot testing with a sample dataset to validate the process and make necessary adjustments.
  4. Training and Rollout: Train internal users on the new process and roll out the system across the organization.

Appendices​

Appendix A: UI Mockups​

Dashboard Mockup:

------------------------------------------------------------
| Data Import Dashboard                                    |
------------------------------------------------------------
| Total Records Processed: 10,000                          |
| Total Matches Found: 8,500                               |
| Total Flagged for Review: 1,500                          |
------------------------------------------------------------
| Metrics:                                                 |
| - Processing Time: 5 hours                               |
| - Match Rate: 85%                                        |
| - Outstanding Items: 1,500                               |
------------------------------------------------------------

Matching Review Screen Mockup:

------------------------------------------------------------
| Matching Review Dashboard                                 |
------------------------------------------------------------
| Record Details        | Possible Match 1    | Possible Match 2    |
------------------------------------------------------------
| Name:                 | Name:               | Name:               |
| Address:              | Address:            | Address:            |
| License Number:       | License Number:     | License Number:     |
| NPI Number:           | NPI Number:         | NPI Number:         |
| Score: 85%            | Score: 85%          | Score: 75%          |
------------------------------------------------------------
| Actions:              | Actions:            | Actions:            |
| [Accept] [Reject]     | [Accept] [Reject]   | [Accept] [Reject]   |
| [Flag]                | [Flag]              | [Flag]              |
------------------------------------------------------------
| Navigation:           |                     |
| [< Previous] [Next >] |                     |
------------------------------------------------------------

Data Import Screen Mockup:

------------------------------------------------------------
| Data Import Summary                                      |
------------------------------------------------------------
| Matched Records: 150                                     |
------------------------------------------------------------
| Record Details        | Fields to be Updated             |
------------------------------------------------------------
| Name: John Doe        | Board State: CA                  |
| NPI Number: 123456789 | License Number: 987654321        |
| Address: 123 Main St  | Year: 2024                       |
------------------------------------------------------------
| Actions:                                                 |
| [Proceed with Import] [Adjust Fields] [Validate Data]    |
------------------------------------------------------------

References​

This document provides a clear and professional outline of the proposed matching process and user interface for data imports, emphasizing the benefits and addressing the key challenges associated with the current process.

Approvals / Signoffs​

RoleNameSignatureDate
Technical LeadJeff Sikes
StakeholderKate Bradley