Biosurveillance/BioSense Evaluation Project

1. Biosurveillance/BioSense Evaluation Project Overview of State AssessmentsAugust 28, 2008 Peter L. Elkin, MD, FACP, FACMI Anna Orlova, PhD Walter G. Suarez, MD, MPH Brett Trusko, PhD Katie Skeen-Morris, MPH Lorna Will, RN, MPH Prya Rajamani, MPH Jennifer Ellsworth, MPH David Froehling, MD Dietlind Wahner-Roedler, MD Zelalem Temesgen, MD Martin LaVenture, PhD, MPH Larry Hanrahan, PhD Roland Gramache, MD

3. Background • Current public health surveillance and investigation often involves manual reporting of cases to public health agencies and phone calls to healthcare providers for more detailed information • The timeliness, completeness, and breadth of coverage of these manual processes can be problematic, especially during a public health emergency • With increasing amounts of healthcare and health-related data in electronic form, the progressive adoption and use of electronic health records and the move towards interoperable health information exchanges (HIEs) at the local, regional, state and national level, there are now significant opportunities to leverage health IT and HIE to better support public health functions including preparedness and disease surveillance.

4. The BioSense Mission • The purpose of the BioSense program is to improve the nation's capabilities for real-time biosurveillance and situational awareness • Provide immediate, constant, and comparable information needed to inform local, state, and national public health, to support preparedness efforts; and • Assess health data about current patients as needed from hospitals nationwide to identify disease clusters (i.e. outbreak) or unusual health indicators (i.e. bioterrorism attack). 4

5. The BioSense Vision • Provide local, state, and nationwide health situational awareness • For suspect illness • Cases of disease • Before, during, and after a health event • Help confirm or refute the existence of an event • Monitor disease outbreak • Size • Location • Rate of spread 5

6. The BioSense Approach • Real-time delivery of healthcare data to BioSense from: • Hospitals • Laboratories • Ambulatory care settings • Other health data sources • Electronic “views,” analytics, and reports for: • National public health, CDC • State and local public health • Contributing healthcare organizations 6

7. Defining the Intent • Early Event Detection: • Prediction of an outbreak or other public health emergency at as early a stage as possible through review of supporting clinical data. (Does NOT imply only initial event detection). May assist in detecting the very first upswing of the epidemic curve. • Health Situational Awareness • Literally, the ability to know what’s going on; accomplished by monitoring extent of disease or disease indicators over time and geographically, especially in an emergency context. Emphasis is placed on monitoring after the initial upswing of the epidemic curve. 7

8. Clinical Care Data of Interest:ED, Outpatient, Inpatient Care Settings • Foundational: demographics (minus obvious identifiers), chief complaint, discharge diagnoses, disposition, hospital utilization • Clinical (ED, Ambulatory, Inpatient): vitals, triage notes, working diagnosis, clinical records, discharge summary • Laboratory: orders, microbiology results • Pharmacy: medication orders • Radiology: orders, interpretation results 8

9. : Implementation Targets and Status: National Healthcare Data Sources 9

10. BioSense Hospitals and Target Cities • Houston • Indianapolis • Jacksonville • Kansas City • Las Vegas • Los Angeles • Louisville • Memphis • Miami • Milwaukee • Minneapolis • New Orleans • New York • Newark • Norfolk • Atlanta • Austin • Baltimore • Boston • Buffalo / Rochester • Charlotte • Chicago • Cincinnati • Cleveland • Columbus • Dallas – Ft. Worth • Denver • Detroit • El Paso • Orlando • Philadelphia • Phoenix Pittsburgh • Portland • Salt Lake City • San Antonio • San Diego • San Francisco • San Jose • Seattle • St. Louis • Tampa • Washington D.C. Last updated 1Oct06 10

11. Biosense • National Public Health System that Receives, Analyzes and Visualizes Health Record data • As of June 2008 => • 558 acute-care hospitals • 826 Veterans Affairs • 354 Department of Defense facilities • Transmitting chief complaint or diagnosis • 42 that send text reports for chest and skeletal radiographs

12. Long-term Performance Measure For CDC Biosurveillance Capacity2006 OMB PART Measure By 2010, CDC’s biosurveillance activities (including BioSense) will reduce the time needed from a triggering surveillance event to initiate event-specific standard operating procedures for all infectious, occupational or environmental (whether man-made or naturally occurring) threats of national importance. 12

13. Early warning system, primary terrorism detection VS System to enable PH access to real-time existing healthcare information, for any situation that arises Developing a single national system, one architecture, cross-jurisdictional info VS Local responsibility, under jurisdiction of States, necessary for local relationship-building Primary Tensions 13

14. Suggested Improvements to Biosense • Suggestions for improved system functionality • Expand and enhance geographic mapping functions • Track visits over time (for same individual) • Enable more ways to share, print, export, download data • Enable views of aggregated national data • Use BioSense data as overlay with existing data, such as air quality index, sewer maps • Whole Record Surveillance • Use of Standardized Ontologies to code and surveill clinical data 14

15. Biosense Evaluation • Evaluation is critical • Systematic evaluation of use for emergency detection and response critical • Develop systematic approach to evaluate inclusion of new data streams (value, data use, etc) • Pilot new data types before broad use • Need specific plan to ensure and enhance data quality 15

16. Cooperative Agreement for BioSense Program Evaluation CDC awarded cooperative agreement for key evaluation aspects including: • Baseline data – to determine PH impact • Data accuracy and validity • System usability and utility • Cost effectiveness 16

17. CDC’s Health Protection Goals Healthy People in Every Stage of Life: All people, and especially those at greater risk of health disparities, will achieve their optimal lifespan with the best possible quality of health in every stage of life. Healthy People in Healthy Places: The places where people live, work, learn, and play will protect and promote their health and safety, especially those at greater risk of health disparities. People Prepared for Emerging Health Threats: People in all communities will be protected from infectious, occupational environmental, and terrorist threats. Healthy People in a Healthy World: People around the world will live safer, healthier, and longer lives through health promotion, health protection, and health diplomacy. 17

18. BioSense Aligned with Four of CDC’s Nine Preparedness Goals 18

19. BioSense Evaluation Areas Awardees should evaluate BioSense with respect to one or more of the following areas: System Utility Data Accuracy Data Use Overall Approach • Appropriateness of methods and protocols as well as whether the methods are accurately applied • Specific data uses to be evaluated: data analysis, data display, monitoring, reporting, & actions taken • Description of BioSense system & stakeholders • Appropriateness of project goals • Appropriateness of data sources & data elements • Rationale (proof-of-concept) for system utility in emergency situations. Assess needs of emergency responders, inventories of other data sources, and mathematical modeling • At the data source, the accuracy of electronic records when compared with an independent source & the accuracy of standard codes • Similarity of received electronic data to data that is sent from the data source & the accuracy of data preprocessing • Aggregate data from source & CDC • Using sample and/or simulated data, confirm data accepted & stored by system is the same as that being transmitted by the source • For day-to-day use, the costs & benefits to identify communicable or reportable diseases • For early event detection, the sensitivity & predictive values for finding disease clusters (including ILIs), cost to users of evaluating data anomalies, & benefit from having identified data anomalies • For health situational awareness, cost & benefit to assist in investigation & mgmt of a disease outbreak • Certain characteristics (e.g. timeliness, flexibility, acceptability, stability) are relevant for all uses 19

20. Mayo Clinic-Mount Sinai School of Medicine Proposal Proposed Methodology System utility rationale (proof-of-concept) • Catalog all methods and protocols along with the use cases that led to their generation; perform analysis of workflow Clinical accuracy review and descriptive statistics • Two reviewers will review 1,000 randomly selected clinical records associated with BioSense submissions (from three sites, one being Johns Hopkins University) Cost-benefit analysis • Provide predictions of potential cost savings when shifting the response to an earlier time when using BioSense 20

21. Project Objectives • What are we really attempting to do? • Opportunity to move forward the bi-directional inter-relationship between Electronic Health Records and Public Health Information Systems • Help give shape to the future of the integration between public health and clinical practice to improve the quality of health care

22. Example Surveys & Findings • Public Health Authorities • Healthcare Organizations

24. Known problems with case/event detection and management • 52% Did not identify any problems • Problems reported: • Need higher resolution than CC, triage notes, nurses notes, diagnoses data • Need better data resolution/ quality of data • Understaffed IT department • Siloed databases • Queries do not meet daily needs • Incorrect parsing • Poor connections, system goes down regularly • LOINC codes and local codes are used • Receive late reports w/ incorrect intervetions implemented • Difficult system that does not meet daily needs • No support for current program • Current database is over capacity, just need a new system

25. Data Gaps in System • 57% did not identify any data gaps • Data gaps reported: • Need data from Urgent Care Clinics and Primary Care Physicians • E-lab needs better patient demographics • Demographic data gaps • In long term care, rates for death from flu not reported • Data needs to be reported in a better format • Current forms may not capture all risk factors

26. Gaps in Current System • 52% did not report any gaps in current system • Gaps reported: • No archive-ability • Graph analysis for longitudinal data analysis • E-mail alerts • Integrating lab data • Integrating diagnosis data • School absenteeism integration • School clinic diagnosis data • Need better syndroming and trending for communicable disease surveillance • More linkages with epi system EHR-S, GIS, RHIO • Data that is not real-time is not useful, data needs to be real time and a flag should produce as soon as the info hits the EMR. Differential Diagnosis would also be helpful. • Need to integrate with other data sources so there is one system, one password • Need to be able to add new fields with drop-down lists of answers for data entry • Automated analysis of incoming data with alarm that is pushed to me • Need to integrate laboratory results file with clinical case files • Ability to link patients w/ same conditions across jurisdictions and investigations • System integration for surveillance and case mgmt. data, threshold alerts, geocoding and jurisdiction names

27. Next Steps • Accuracy of the Biosense Data • Data Sharing Agreements with JHU in place • Is the data submitted accurate at JHU and at the CDC? • Only 47 of 3000 ICD9-CM Codes matched the data from the chief complaints of patient data submitted on the same day by the same patient to the CDC. • Could the data be better using SNOMED CT based whole record analysis? • Discussions are ongoing toward formulating a data sharing agreement with Aurora Healthcare System • PHIRMS

28. Public Health Information Resource Management Solution (PHIRMS) HK00014 (U38) Collaborators And our CDC collaborators

29. PHIRMS Goal • Solution to capture the data needed to: • Evaluate BioSense • Public Health Survey • Evaluate any Public Health Intervention in terms of Cost-Benefit Ratios • Benchmark current and future Public Health practice

30. PHIRMS Conceptual Framework

31. PHIRMS Data PHIRMS data consists of: • Healthcare Data: • History • Physical Exam (PE) • Lab (Results, Orders) • Procedure Notes • Radiology (Results, Orders) • Nursing notes • Impressions • Demographic Data Symptoms, signs, findings, diagnoses and procedures.

32. PHIRMS Data (continued) PHIRMS data consists of: • Resource Data • Costs • Personnel • Available • Utilized • Resources • Available • Utilized • By Activities

33. LBI Codified Health Record The Mayo Clinic’s Laboratory: Biomedical Informatics (LBI) stores patient Healthcare and Demographic data in a codified Health Record to which other PH data could be added. • SNOMED CT • LOINC • RxNorm • ICD9-CM • CPT • Others

38. Biomedical Informatics Research Collaborative:LBI Codified Resource Record The Laboratory of Biomedical Informatics (LBI) proposes to add resource relateddata to codified health record data

39. Public Health Workflow (Handling of Epidemics: Case/Event Detection, Monitoring and Response Management) • Case/Event Detection • Anonymization of Records for Data Mining • Epidemics • Clusters of Cases • Cases • Suspected • Confirmed • Mandatory Public Health Reporting • Ad Hoc Case Reports

40. Conceptual Organization of Knowledge Epidemic Cluster Case Case Case Case Cluster

41. Public Health Workflow (Continued) • Case/Event Management • Case/Event Monitoring • Case/Event Response • Resources Management • Resource Identification • Resource Allocation and Utilization

42. Local PH Data Flow • Local PH communicates with HCO, State PH and CDC HCO & RHIO State PH CDC Local PH

43. Public Health Agencies Data Flow • State Public Health communicates with Local Public Health and CDC State PH Local PH CDC

44. PHIRMS Vision • Simultaneous information sharing to improve workflow. Local HCO State CDC

45. Work Flow – Data Flow HCO • Data flowing from the Healthcare Organization (HCO) and RHIOs Biosense Public Health Grid CDC HCO & RHIO State PH PHIN (NEDSS) Local PH PHIRMS

46. Utilities – Data Aggregate • Data could be Aggregated by: • HCO • Zip code • Local PH • State PH • National • Diagnosis • Suspected • Confirmed • Symptoms/Signs

47. Utilities – Data Analysis • Analysis of the data would provide information regarding: • Trends • Time Series data • Survey Data • Satisfaction • Other • Costs (Resource Utilization by Case-Cluster-Epidemic) • Benefits’ Analysis

48. Data Communication • Data could be made available in various formats: • By Aggregation / Stratification • Graphical Representations • Use of Active Graphics • Push and Pull • Subscribe and Publish • Web / e-mail / Phone Calls

49. PHIRMS: eQualityMonitoring Solution Payor Resource Management Hospital EHRS State Public HealthSurveillance System 9 - Monitor ER visits, hospitalizations data from EMRs & utilization data 1 – Conduct Routine Check-ups 4 – Prescribe Medication and Treatment Plan 11 – Send reports Ambulatory Care 12– Conduct Surveys (BRFSS) 5 – Monitor Treatment 2 – Order cholesterol test 7 – Report Data to Schools Encoded Biosurveillance & Quality Guidelines eQuality System run by HCO / RHIOs 3 – Report test results DHHS PHIRMS Expert System 10 – Conduct Health Education 6 – Fill Prescription P U B L I C 8– Coordinate Care Encoded Data Internal Quality Reporting Aggregate Anonymized Reports Media Decision Support Laboratory Pharmacy School

50. Healthcare Organization SNOMED CT Encoded EHR Data CDC EHR And / Or Health Information Exchange (HIE) State Public Health Authority SNOMED CT Encoded EHR Data EHR Local Public Health Authority Biosurveillance & Adhoc Rule Sets Expert System Shell Healthcare Organization