# Presentation: 244582

## DSIDE Project

- CDC Webinar
- June 29, 2007
- Frank Drews, PhD
- Matthew Samore, MD
- Departments of Biomedical Informatics, Psychology, and Internal Medicine
- University of Utah

## Decision-support for infectious disease epidemiology

- Examine the process of decision-making in public health epidemiology
- Develop simulation-based tools adapted to public health decision-makers

## Background: Naturalistic Decision-Making (NDM)

| Traditional DM | NDM |
| --- | --- |
| artificial, well structured  | ill structured problems |
| clear and stable goals | shifting, ill defined or competing goals |
| one shot decisions | action feedback loops |
| ample time | time stress |
| non-consequential | high stakes |
| individual DM | multiple players |
| in a vacuum | organizational goals and norms |

## Public health research question

- What are the distinctive cognitive processes that underlie decision-making in public health epidemiology?
- Heuristics and biases
- Pattern recognition
- Information search strategies
- Perspectives on error

## Characteristics of Public Health Decision Making

- Population as target
- Uncertainty
- Operating with limited resources
- Under time pressure
- Limited staff
- Multiple players
- Competing goals
- Complex decision space
- Decision making is different from standard areas of investigation
- Puzzle versus mystery

## How to study decision making in public health?

- Interviews
- Information flow
- Problems and challenges
- After-action reports
- Analysis of protocols
- Scenarios
- Process tracing
- Information board

## Preliminary Themes

- Strategies (heuristics)
- Primacy of jurisdiction
- The “dominant exposure risk factor”
- Values
- Shared responsibility
- Decision denial
- Consistency
- Tradition
- Avoidance of confusion

- Mental models
- Population versus clinical perspective
- Meta-cognition (abstraction, explanation)
- Concept of error in government context

## Informatics research question

- Will interactive simulations and other data visualization tools that are adapted to the mental models of epidemiologists enhance public health decision-making in settings of high threat or uncertainty?

## Pertussis example

- Problems of decision-making and situation comprehension for which simulation and informatics tools may provide insight
- Assumptions about levels of under-reporting
- Changing use of diagnostic tests
- Definition of outbreak
- Evaluation of effect of contact tracing

## Time for Demonstration

## Next steps I

- Refinement of prototype
- Enhance interface
- Adjust disease model parameters
- Calibrate to real data
- Add logic for immunization and other missing components

## Next steps II

- Model validation in collaboration with Michigan team (Koopman)
- Extend approach to high fidelity simulations of large populations and metropolitan area, in collaboration with Virginia Bioinformatics Institute (Barrett, Eubank, Marathe)

## Next steps III

- Scripted experiments (scenarios)
- Probe of public health decision-making
- Model validity from public health perspective
- Utility from public health perspective
- Potential for insight and learning

## Next steps IV

- Additional case studies (disease domains)
- Familiar but important pathogens, which present decision-making  and policy-making challenges pertinent to current practice (i.e., pertussis, norovirus)
- Unfamiliar, high threat emergent pathogens, which present myriad potential decision-making challenges (i.e., pandemic influenza)