1. Deepthi Rajeev, MS, MSc Department of Biomedical Informatics University of Utah Evaluating the Impact of Electronic Disease Surveillance Systems On Local Health Department Work Processes 10/28/2009

2. 2 Steps in the Reporting Process Laboratories, hospitals, doctors: Identify condition Recognize that it is reportable Collect data and transmit clinical and laboratory information to public health Health Departments: Receive clinical and laboratory reports Trigger an investigation if indicated Implement control measures to prevent further exposure and transmission

3. 3 Problems for local health departments Manual reporting process (fax) Insufficient data in initial case report Manual triage of initial case report  Time consuming, but not quantified Reports belong to other jurisdictions Duplicate reporting Lack of shared information

4. NETSS Manual entry Local Health Dept Electronic health record Lab information system NETSS Manual entry State Health Dept Fax * No interface to receive electronic information * No shared public health records Former Reporting Process in Utah Reporting entities Fax Phone Physician Infection Preventionist Others

5. NEDSS Manual entry Local Health Dept Electronic health record Lab system Manual entry State Health Dept Fax RTCEND= electronic transmission of case reports NEDSS = shared public health records New Reporting Process in Utah Other reporting entities Fax Phone Physician Infection Preventionist HL7 (RT-CEND) Electronic health record Intermountain Healthcare Others

6. 6 Issues to consider Will the new electronic systems impact workflow? Who will be affected? Will the impact be positive or negative?

7. 7 Research Objectives Identify metrics to monitor impact on workflow as new systems are developed and implemented Collect baseline data

8. Salt Lake Valley Health Dept 8000+ reports per year Reports from Laboratories, Hospitals, Clinics, State Health Department, other local Health Departments, Community, Jail… Formats: Fax, Phone call, Email, Mail Study Location

9. 9 Methods to select metrics Observation Study - observed tasks performed by various personnel at SLVHD Interviewed SLVHD personnel - Triage nurse, data entry, nurse, nurse manager Documented workflow associated with processing a case report and validated workflow Identified tasks that were frequent, important, and measurable Identified metrics to measure the selected tasks

10. 10 Timestamps for timeliness evaluation Case detected (date of lab results or diagnosis) Reported to public health Entry in surveillance database Investigation ends Investigation starts Time to diagnose case Reporting Time Time until case is triaged Time to review (establish jurisdiction and reportable condition status) + time for initial data entry Time until case is investigated Time until case investigation is completed Goal: shorten this time interval Start triage process Onset of disease

11. 11 SLVHD workflow Triage Report Initial Data Entry Assignment Investigation and implementation of control measures Review and assign case classification Archive Case Information Forward to state health department Stop Start Does the report have all the information required to identify: if the condition is reportable? if SLVHD is the responsible health department? Identify if the report belongs to a new case or is an update to an existing case

12. 12 Metrics for Triage Process Relevance of the reports received:  # (%) of reports with new information including: o new unique (non-duplicate) cases o updated information  # (%) of duplicate reports  # of out-of-county cases Follow-up:  # of phone calls to gather additional information  Type of additional information required  # of times data required was obtained  # of times forwarding of reports to data entry was delayed Time to review a report and determine that condition is reportable and relevant for Salt Lake County

13. 13 Metrics for Data Entry Process Time required to identify whether information on a newly arrived report has previously been reported (i.e., new or existing case) Time required to enter data into the computer Number of reports entered each day and week

14. Baseline data collection

15. 15 Methods Direct observations at Salt Lake Valley Health Department July 6 - 13, 2009 Data collection form Extracted timestamps from NEDSS that were collected as part of routine work processes

16. 16 Date Collection Form

17. 17 Distribution of Reports Received 380 reports received for 33 different diseases New unique reports for Salt Lake County (n=172) Updated information (n=50) Duplicate reports (n=72) Out-of-County reports (n=86) 76% reports from Utah Department of Health

18. 18 Number of reports triaged by day

19. 19 Incomplete Reports Of 380 reports, 105 reports (32%) required additional information  99 phone calls made  63 reports (60%) were held for additional information and not forwarded to data entry immediately

20. 20 Details on Missing Data

21. 21 Time to Triage Reports Average 3 mins 31 sec / report –3 mins 30 sec for SLVHD cases –3 min 38 secs for Out-of-county cases Total time to triage cases (before forwarding to data entry) : 12:20:40 (hh:mm:ss) –~ 26% FTE

22. 22 Interval between Report and Triage Date

23. 23 Time for Initial Data Entry Observed 29 th - 30 th June 2009 62 reports entered Time to identify if report already exists  in NETSS: 12 seconds/ report  in NEDSS: 35 seconds/report Time to enter data  in NETSS: 49 seconds/ report *  in NEDSS: 3 min 9 seconds/ report * During study, only part of the data was entered in NETSS (NEDSS was the main system in use)

24. 24 SLVHD Timeliness Case detected (date of lab results or diagnosis Reported to public health Entry in surveillance database Investigation ends Investigation starts Time to diagnose case Reporting Time Time until case is triaged Time to triage Time until case is investigated Time until case investigation is completed Goal: shorten this time interval Start triage process Onset of disease 7 days 6 days 1 day 0 days 7 days

25. 25 Next Steps Develop an ongoing monitoring system to evaluate impact of surveillance systems on workflow Issues:  Is this feasible with the existing infrastructure?

26. 26 Acknowledgements CDC- Utah Public Health Informatics Center of Excellence (Grant # 8P01HK000030) Rui Zeller Andrea Price Jon Reid Catherine Staes Ilene Risk Richard Kurzban Mary Hill Kris