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Enhancing Detection Algorithms: Bridging Data Monitors and Developers for Effective Public Health Surveillance

This document explores the challenges faced by data monitors and algorithm developers in public health surveillance. It outlines issues such as the limited understanding of algorithms among monitors, the importance of user feedback, and the need for iterative improvement in detection methods. It discusses various detection algorithms, the implications of false positives, and the necessity of real-time data monitoring. It also highlights potential solutions for improving detection accuracy, data processing, and the importance of collaboration between developers and users to enhance public health outcomes.

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Enhancing Detection Algorithms: Bridging Data Monitors and Developers for Effective Public Health Surveillance

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Presentation Transcript

  1. Detection and Analysis Perspectives of Both Data Monitors and Algorithm Developers

  2. Data Monitors • Turbulence • Turnover • Training • Varied skill sets • Limited understanding of algorithms • Overworked and few • Feedback to providers critical

  3. Developers • Interaction and feedback from users is important • Iteration • Critical for developers to know users problems • Limited understanding of public health operations • Expense of false positive to users

  4. Available Detection Methods • BioSense, Essence, RODS, Red Bat… • Cusum, Smart Scores, RLS, EWMA, Wavelet, … • Spatial scan statistics, SatScan, zipcode • Multiple detection algorithms – how many are flagging? • Multiple detections corroborate problem • Real-time vs. batch?

  5. Real-time Detection • Data is unsettled when it arrives • Pressure for real-time may exacerbate existing problems • Is it sustainable? • Who will monitor it? • How valuable is it? • If not everyday – can we do it in a crisis?

  6. Weaknesses of Detection Algorithms • Cusum, EWMA – most widely used • Control chart has many assumptions • Normally distributed • Stationary assumption • Are assumptions being met? • Method must match data • Getting false alarms that exceed rate that would be expected may signal disconnect between data and algorithm

  7. Challenges (to name a few) • Syndrome categorization • More? • Fewer? • Subsetting? • Statistical significance does not equal public health significance • “False” alarms

  8. Issues for Users • False positives • Disconnect between developers and users • Difficulty evaluating what is a real alarm • Data quality issues • What user needs are being supported? • What are other uses for data? • Overall evaluation of syndromic surveillance utility

  9. Possible Approaches to Improving Detection • Explanation to user of why alarming • Pop-up? • Text-strings from physicians • Phased alerting system • Yelling, Anomalies, Alert • Improving alert qualification (RODS in Ohio)

  10. Possible Approaches to Improving Detection (continued) • Better pre-processing of data • Better post-processing of data • De-duplication of data, etc. • Failure analysis of false alarms • What are major contributors?

  11. Needs • Refined data • Improved algorithms • Human expert required to make determination • Domain knowledge • Local knowledge • Statistical analysis is only a tool • Good relationships among users • Federal, state, local, facility levels

  12. Summary • Systems: Alarm too often • Users: May become discouraged • Statistical challenges • Detection methods up to it? • Data problems? • Solutions: • Better data, better processing, more money

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