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Transfer of Data from a Hospital System to a Local Health Department Evelyn O. Talbott, Dr.P.H., M.P.H LuAnn L. Brink, Ph.D. February 24-26, 2009 TRACKS2009 Washington, DC Thanks to… Ron Voorhees, ACHD Rao Guduru, ACHD Linda Gmitter, UPMC Adam Gronsky, UPMC

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transfer of data from a hospital system to a local health department

Transfer of Data from a Hospital System to a Local Health Department

Evelyn O. Talbott, Dr.P.H., M.P.H

LuAnn L. Brink, Ph.D.

February 24-26, 2009

TRACKS2009 Washington, DC

thanks to
Thanks to…
  • Ron Voorhees, ACHD
  • Rao Guduru, ACHD
  • Linda Gmitter, UPMC
  • Adam Gronsky, UPMC
  • Tony Fabio, University of Pittsburgh
  • Tanya Kenkre, University of Pittsburgh
cooperation
Cooperation
  • Data provided to Allegheny County Health Department as part of Public Health Surveillance
  • Data provided by the University of Pittsburgh Medical Center to improve Public Health
  • Only aggregate data will be provided to investigators
slide5

Via site-to-site VPN,

  • UPMC will send:
  • patient name,
  • SSN,
  • race
  • date of visit,
  • time of visit,
  • address,
  • date of birth,
  • gender,
  • type of insurance,
  • Chief complaint
  • ICD9 diagnosis code
  • disposition
  • For all hospital visits with a final diagnosis code of 460-520 from Allegheny County hospitals

Matched respiratory data

UPMC will purge all ED data that do not match a corresponding discharge code of 460-520

Irrelevant data

data transfer
Data Transfer
  • Site-to-site VPN has been dedicated for data transfer
    • Firewalls to secure data are in place at ACHD
    • Firewall exceptions have been added to allow data from hospital to pass continuously to ACHD
data format
Data format
  • Medical record data arrives at ACHD in a flat file in real time when the final diagnosis is applied to an asthma Emergency Department visit (usually less than 24 hours)
  • Data is then stored on a secure Oracle server that was provided by Pitt APEX
  • Data is accessible to ACHD epidemiologists through Oracle Discoverer
slide9

VPN Connection shared with UPMC

If no acknowledgement, the connection is broken

If Medipac data is present for a visit with acute reactive airway disease is there, it is transferred

ACHD acknowledges the data receipt

yes

ACHD dumps data to an Oracle database

The data can then be analyzed using the password-protected web-based interface of Oracle Discoverer at ACHD

Data is de-identified and made available to APEX researchers

de identification of upmc data file
De-identification of UPMC Data File
  • Creation of unique Identifier and removal of UPMC and patient identifiers
    • Create Cpi-T variable [integer; 14 digits]
    • Create VN-T variable [integer; 10 digits]
    • Let Cpi-T = formula(Cpi)
    • Let VN-T = formula(Visit number)
de identification of upmc data file12
De-identification of UPMC Data File
  • Calculation of Age variable and removal of Date of Birth
    • Create “Admit Day” variable [CCYYMMDD]
    • Create “Age” variable [numeric, 3 digits]
    • Create Age Groups
    • Let “Admit Day” = truncated “Visit Admit Date”; keep first 8 digits only
    • Let “Admit Day” = truncated “Visit Admit Date”; keep first 8 digits only
de identification of upmc data file13
De-identification of UPMC Data File
  • Assigning of Census Tract and removal of patient address information
    • Create “Census Tract” variable
    • Assign census tract using program (80% hit rate)
    • Manually assign remaining Census Tracts
de identification of upmc data file14
De-identification of UPMC Data File
  • Delete Identifying Information
    • Delete Account Number
    • Delete Cpi
    • Delete Visit number
    • Delete Patient Name Last
    • Delete Patient Name First
    • Delete Patient Name Middle
    • Delete “Date of Birth” field
    • Delete “Age” field
    • Delete “Patient Address- Line 1” field
    • Delete “Patient Address- Line 2” field
preliminary data 2009 provided by achd
Preliminary data 2009 – provided by ACHD
  • 7921 messages transmitted Feb 1-12, 2009
    • 548 had diagnosis of 493.x (6.9%)
  • 5228 of these were final (abstracted) diagnoses
    • 389 had diagnosis of 493.x (7.4%)
  • These represent 2623 individuals
    • Age range is 0-99
    • Mean age is 46.93
expected achievements
Expected achievements
  • Innovative, cost-effective surveillance strategy
  • Near real-time surveillance
  • Manageable amount of information (~50 asthma cases/day)
  • Based upon a diagnosis, not free-text
  • Mechanism to collect other information of public health importance
future directions
Future Directions
  • This secure connection can be utilized to speed up reporting of notifiable diseases to benefit the health department
  • It can be utilized to transmit information on lab tests for metals and other biological markers for biomonitoring to benefit environmental public health tracking
slide19

Im W. Schneider D. Effect of Weed Pollen on Children’s Hospital Admissions for Asthma During the Fall Season. Archives of Environmental and Occupational Health. 60(5), September-October 2005, 257-65.

slide20

Im W. Schneider D. Effect of Weed Pollen on Children’s Hospital Admissions for Asthma During the Fall Season. Archives of Environmental and Occupational Health. 60(5), September-October 2005, 257-65.

slide22

Case-Crossover Analysis of Air Pollution and Cardiorespiratory Hospitalizations: Using Routinely Collected Health and Environmental Data for Tracking

  • Significant associations were observed between the fourth quartile in PM10 and cardiorespiratory hospitalizations (odds ratio [OR]: 1.12; 95% CI: 1.02–1.23) and cardiovascular hospitalizations only (ICD-9: 390–459) (OR: 1.13; 95% CI: 1.01–1.26) before the plant closure.
  • After closure of the plant, PM10 was not significantly associated with cardiorespiratory or cardiovascular disease hospitalizations. Moreover, the referent sampling approaches did not greatly alter the estimations in the case-crossover analysis.
  • The findings suggest that closure of the steel coke plant was associated with a reduction risk of the cardiovascular hospitalizations.
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