Use Case 30: Identify dual eligibles in CMS DE-SynPUF

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Use Case

Use case summary

Description

This use case requests to the creation of a better public data source for analysis of dual eligibles. The aim is to address a high impact topic in health policy: Identifying high-risk, high-cost individuals with the aim of providing them appropriate social services. Medicaid can pay for such services, but not Medicare. CMS clearly knows who is dual eligible because they publicly report percentage of dual eligibles by county in their Geographic Variation PUFs. The ACA set up a special office in CMS for dual eligible matters as they are well known to be both sicker and more expensive than the general population by a lot.


Value

  • Value to customer: Help identify dual eligibles for specific analytics.
  • Value to industry/public: Find ways to deal with high-risk, high-utilization populations by providing appropriate social services.

Customer

  • Optum


Current data and limitation

  • Data source: Data Entrepreneurs’ Synthetic Public Use File [1]
  • How it's used:
    • CMS put in a LOT of effort to create something called the DE-SynPUF (data entrepreneurs synthetic public use file) which is a realistic and professionally deidentified (CMS hired the NORC!) sample data set to give would-be data entrepreneurs something realistic to try out new applications.
  • Limitations: Missing fields:
    • Flag indicating if beneficiary is dual eligible
    • Reason why they're dual eligible (For example: over 65, on SSDI for 2+ years, or have ESRD or ALS))


Specifications

  • Fields:
    • DESYNPUF_ID (DESYNPUF: Beneficiary Code)
    • Flag indicating if beneficiary is dual eligible
    • Reason why they're dual eligible (For example: over 65, on SSDI for 2+ years, or have ESRD or ALS))
  • Update frequency: Same as existing PUF
  • Joins between datasets: Link on DESYNPUF_ID for DE-SynPUF files:
    • Beneficiary Summary
    • Inpatient Claims
    • Outpatient Claims
    • Carrier Claims
    • Prescription Drug Events (PDE)
  • Lag time: Same as existing PUF
  • History: Same as existing PUF
  • Delivery mechanism: Download CSV (API is less important if data set isn't refreshed often)


Solution

Short term workaround

  • ___

Long term solution

  • ___