Use Case 47: Influenza forecast hot spot visualization

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Requirements

Use case summary


Description

We need the ability to write daily tele-prompter texts and create associated animated videos for local TV weathermen nationwide focusing on the Control and Prevention of Influenza.

The animated television video reports should be based upon both real-time (daily) de-identified influenza-related claims data from CMS-HHS and other sources using a predefined collection of CPT and ICD-9/ICD-10 codes.


Value

  • Value to customer: This data would allow the customer to write teleprompter text for weathermen to provide daily current "flu status" by school district lines; this would make people aware if there is a local flu outbreak prompt parents to vaccinate their children if they haven't yet been.
  • Value to industry/public: ___

Customer


Current data and limitation

  • Traditional flu monitoring has long reporting delays and misses a lot of potential flu cases. It has historically depended on networks of physicians to report patient visits that show signs of influenza-like illness (ILI). Currently, CDC reports influenza on a weekly basis.
  • Data source: BioSense
    • How it's used:
      • BioSense is a syndromic surveillance system intended as an integrated national public health surveillance system to provide nationwide and regional situational awareness for all-hazard health-related threats and to support national, state, and local responses to those threats.
      • BioSense supports Stage 1, Measure 9 of the Meaningful Use requirements: Syndromic Surveillance Data Submission
      • EHR information from hospital emergency departments is transmitted to the local or state health department (in theory, within 48 hours of the first patient encounter to meet ONC Meaningful Use requirements), which then transmits the information to CDC.
      • For influenza-like illness, a syndrome or case definition is defined using related symptomology of what could potentially be flu in order to be as early as possible in identifying what might potentially be flu.
      • Approximately 45% of ER visits nationwide are captured by BioSense, but there is great variability by state. For example, only 3% of California visits are captured while nearly 100% of North Carolina visits are captured.
      • Data is also obtained from DoD ambulatory visits.
    • Limitations:
      • Data from BioSense can only be used by the CDC at the national aggregate or HHS regional level. Use at the state, county, or zip code level must be negotiated with the state or jurisdiction.
      • Hospitals vary greatly in adhering to the 48 hour MU deadline -- for example, Cerner requires the data to be processed centrally before transmitting to the state or local health department, introducing an additional step and delay into the system.
  • Data source: Medicare claims files
    • How it's used:
      • Regional Medicare carriers are likely the closest source of data to reduce lag time
    • Limitations:
      • Claims data hasn't traditionally been used for semi-real-time analytics

Specifications

Data specifications

  • Fields:
    • CPT codes
      • 87804 (Influenza A)
      • 87804-59 (Influenza B)
    • ICD-9 Codes
      • 079.99 Unspecified Viral Infection
      • 487.0 - 487.8 Influenza
      • 729.1 Myalgia and Myositis, Unspecified
      • 780.60 Fever, Unspecified
      • 780.79 Other Malaise and Fatigue
      • 784.0 Headache
      • 786.05 Shortness of Breath
      • 786.2 Cough
      • 462 Acute Pharyngitis
    • ICD-10 Codes
      • B34.9 Viral Infection, Unspecified
      • J10.00 - J11.89 Influenza due to other Influenza Virus
      • M79.1 Myalgia
      • R05 Cough
      • R05.9 Fever, Unspecified
      • R06.02 Shortness of Breath
      • R51 Headache
      • R53.81 Other Malaise
  • Update frequency: Daily
  • Joins between datasets: _______
  • Lag time: As real-time as possible
  • History: _______
  • Delivery mechanism: _______

Visualization example

Source: http://www.nature.com/news/when-google-got-flu-wrong-1.12413


Solution

Potential solution paths

  • Investigate input existing data feeds
    • CDC's BioSense surveillance program
      • Data sources:
        • Collect input from hospital ER visits, as part of Meaningful Use (MU) requirement
        • DOD (Department of Defense) ambulatory clinics
    • CMS Medicare FFS (fee-for-service) claims processing
    • John Brownstein, chief innovation officer at Boston Children’s
    • Google Flu Trends

Short term workaround

  • ___

Long term implementation

  • ___

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