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Local Health Department Experiences in Seeking Access to Surveillance Data

Local Health Department Experiences in Seeking Access to Surveillance Data. Joe Gibson, MPH, Ph.D. Director of Epidemiology Marion County Public Health Department, Indianapolis, IN IL Integrated PH & Medical Preparedness Summit, 2012-06-20. Challenges obtaining near-real-time health data.

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Local Health Department Experiences in Seeking Access to Surveillance Data

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  1. Local Health Department Experiences in Seeking Access to Surveillance Data Joe Gibson, MPH, Ph.D. Director of Epidemiology Marion County Public Health Department, Indianapolis, IN IL Integrated PH & Medical Preparedness Summit, 2012-06-20

  2. Challenges obtaining near-real-time health data • HIPAA: data sharing to public health is allowed (not required) • Reluctance to be the first sharer • Confidentiality laws omit public health use

  3. Challenges ObtainingSchool Absenteeism Data • Voluntary • Not part of schools’ core mission • Data content varied • Data format varied • Some very difficult to abstract • Most required custom code

  4. Challenges obtainingdata from state health dept. • Confidentiality laws vary across diseases • Local training in data protection varies • Local “home rule” but state is data steward • Intervention authority is local • Laws name state to receive data • State-to-local transfer not addressed

  5. What did not work • Broad requests • Relying on authority, power plays, legal debate • Not understanding restrictions faced by the sender • Altering sender’s work processes

  6. What works: Trust • Build relationship with data provider • Understand provider’s data protection rules • Find opportunities for interaction • Be incremental. Start with narrow requests. • Clear agreements on how data will & won’t be used • Conform to client’s intent in providing data • Protect unidentified but sensitive information • Show that you are using the data

  7. What works: Minimize burden, maximize value for sender • Minimize sender’s work (be a “data beggar”) • Accept many formats • Don’t change work processes • Minimize sender’s risk • No identified results (e.g., absenteeism) • Create value • Provide data interface (or at least reports) • Show data being used, recognize the sender

  8. What works: Legal mandate or top executive support • Legal mandate • “A health care provider … that collects (data related to symptoms and health syndromes) … shall report to the state department” • Even willing partners like a legal mandate • Superintendents’ OK to get absentee data Authority is often necessary,but usually not sufficient

  9. What works: Finding the right person • Whose job are you making easier? • With whose mission do you align? • Who is invested in the issue? • Who has the tools & skills to provide what you need?

  10. What works: Key points • Build trust through frequent contact • Keep data request scope narrow • Understand sender’s data protection rules • Make data useful to the providers • Demonstrate ongoing use and value

  11. Extra Slides for Extra Time

  12. What works: Cooperation • MCPHD & ISDH do daily, independent analyses of sydromic surveillance data • We call each other when we see something of interest • The Result: • higher quality surveillance • faster learning • strong, respectful relationships

  13. What works: Streamlining, Standardization, Automation • Challenges • Find a person who will send the data • Have them send it regularly • Prompt them to continue sending it • … in a good format • … via a good transport method • … with good data quality • Troubleshoot transmission & quality problems

  14. What works: Streamlining, Standardization, Automation • We want one surveillance tool, not many • We fail to monitor data sources that are not in easily monitored systems, or data we need infrequently

  15. Once you have the data … • Analysis & reporting takes time • Monitor the transfers • Deduplication is challenging • Data Quality & Interpretation • The more you know, the more you know that you don’t know. • High level skill: Knowing which errors matter • Interpretation: not a one-night stand

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