Machine-readable files (MRFs) have become a cornerstone of healthcare data analysis, especially in the wake of price transparency initiatives. These files, typically provided in JSON format, offer detailed information on healthcare pricing, reimbursement rates, and provider contracts. While they’re structured for machine processing, analysts often need to make sense of this data manually, whether to create reports, identify trends, or support compliance efforts.
JSON files, while highly versatile for developers, can be intimidating for Excel users. Unlike traditional tabular data formats like CSV, JSON files are hierarchical, resembling a tree with nested elements. For example, a JSON-based MRF might include a structure like this:
{ "provider": "HealthCare Corp", "rates": [ { "service_code": "12345", "description": "MRI Scan", "negotiated_rate": 450 }, { "service_code": "67890", "description": "Blood Test", "negotiated_rate": 30 } ]}In this example, the rates field contains a list of services, each with details like service codes, descriptions, and negotiated rates. This hierarchical format is great for machines but not ready-to-use for spreadsheets. To unlock the value in JSON MRFs, you must first flatten this data into rows and columns, making it accessible for Excel-based analysis.
This guide focuses on importing JSON MRFs into Excel and preparing them for deeper exploration.
Import JSON Files into Excel with Power Query
Excel provides built-in tools to work with JSON files through its Power Query feature. This allows you to navigate and flatten JSON hierarchies into a tabular format:
Open Power Query:
Go to the Data tab in Excel and click Get Data > From File > From JSON.
Browse to select the JSON MRF file you’ve downloaded.
Inspect the Data Structure:
Once imported, Power Query displays the JSON structure. You’ll see fields and nested elements organized into expandable lists or records.
Click the Expand icon (a small arrow next to a column name) to open up nested fields. For example, expanding the rates field in the example above would reveal service codes, descriptions, and negotiated rates.
Select Relevant Fields:
Identify and expand only the fields you need. This minimizes complexity and improves performance, especially when working with large datasets.
Preview and Transform:
As you expand and flatten fields, Power Query will display a preview of how the data will look in a tabular format. Use filters, rename columns, and remove unnecessary fields at this stage.
Load the Data: Once you’re satisfied with the transformation, click Close & Load to import the data into an Excel worksheet.
Prepare for Large File Sizes
JSON MRFs can be massive, often containing millions of records, especially when detailing provider rates across regions and services. Excel has limits, so managing file size is critical:
Filter Data in Power Query: Before loading, apply filters to include only the data you need. For example, filter by a specific provider, region, or service type.
Split Large Files: If the file is too big, divide it into smaller chunks using tools like Python, Gigasheet or a text editor before importing into Excel.
Optimize Excel Settings: Turn off automatic calculations and use data tables to keep Excel responsive when working with large datasets.
Alternatives to Excel for Massive MRFs
If the dataset exceeds Excel’s capabilities, it may be time to explore more advanced tools and platforms designed to handle large-scale data processing and analysis. Power BI is an excellent option for creating dynamic visualizations and dashboards, allowing you to analyze massive datasets interactively without running into Excel’s row and memory limits. For those who need a spreadsheet-like interface but with support for billions of rows, Gigasheet provides a powerful alternative that’s both familiar and scalable. If your work requires advanced querying or integration with other systems, consider using database systems like SQL Server, MySQL, or Snowflake, which can efficiently manage and analyze large datasets. These platforms also support integrations with tools like Excel, allowing you to query specific data subsets and load them into spreadsheets for reporting. By leveraging these complementary solutions, you can maintain the flexibility of Excel while addressing the demands of big data analysis.
Power BI: For dynamic visualization and analysis of large MRF datasets.
Gigasheet for Price Transparency: Ideal for curated reports focused on markets/payers of interest; distilled insights from billions of rows of MRFs in a spreadsheet-like interface.
Database Systems: Load MRFs into SQL databases for advanced querying.
Analyze and Visualize the Data
Once your JSON MRF is in Excel, you can leverage Excel’s full range of analysis tools:
Pivot Tables: Summarize large datasets to find trends in negotiated rates, service volumes, or regional variations.
Formulas: Use Excel’s formula capabilities to calculate averages, identify outliers, or compare rates across providers.
Conditional Formatting: Highlight high-cost or low-cost services using color scales or data bars for quick visual insights.
Why Focus on JSON?
JSON is now the standard format for most machine-readable healthcare data because of its flexibility and ability to represent complex relationships. By learning how to work with JSON MRFs in Excel, you gain access to a wealth of data for decision-making, from analyzing provider costs to supporting policy compliance.
For Excel users, tools like Power Query make this transformation process intuitive, bridging the gap between machine-readable formats and actionable insights. With a few steps, you can unlock the power of healthcare MRFs in a familiar spreadsheet interface, enabling efficient analysis without requiring advanced coding skills.
Are you working with JSON-based MRFs in Excel? Share your experiences and tips!
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