# Presentation: 071055

## Translational Research in Public Health Informatics: Linking Healthcare, Communities, and Public Health

- CDC Webinar
- May 30, 2007
- Utah Research Center of Excellence in Public Health Informatics

## Outline

- Introduction
- Translational research in public health context
- Overview of Utah’s research projects
- How these research activities advance public health
- Description of individual projects
- Scientific core
- Vision
- Integrated public health data resource
- Summary of progress

## Current model of translational research 

- JAMA. 2002; 289:1278-1287

## Translational Research in Public Health

- Missing from the current model
- Distinct perspective of public health practitioners
- Implementation and effectiveness research in the public health context
- Integration of behavioral and information sciences
- Later in this presentation
- Wu Xu will express her perspective on this topic

## Areas ripe for research

- Dissemination of best public health practice
- Implementing change, adopting new technologies, evidence-based “breaking with tradition”
- Surveillance science
- Basic and applied
- Systems thinking in public health
- See March, 2006 issue of American Journal of Public Health

## Overview of Utah’s CDC-funded Center of Excellence

## Organization of Projects

## Each project addresses:

- One or more public health domains or problems
- Basic and applied research questions
- Public health
- Informatics

## Building a Center

**Partnerships and synergies**
- Active participation of health departments at local and state levels
- Public health leadership or co-leadership of projects
- Opportunities for public health informatics trainees
- Weekly to quarterly conferences and retreats
**National collaboration**

## Cross-cutting public health domains

- Vaccine preventable illness
- Particularly, pertussis, chickenpox, influenza
- Emerging infections
- Particularly, pandemic influenza and other respiratory pathogens
- Novel health hazards associated with clinical care

## How these research activities advance public health

- Robert Rolfs, MD, MPH
- Utah State Epidemiologist

## How COE Projects Support Public Health

- Case Identification

- Case Investigation & Management

- Analysis

- Actions based on Results

- Data Dissemination

- RT-CEND

- Linkage projects

- DSIDE

- INTERACT

- Outbreak response, advisories, other actions

- Rx Drug Deaths Readmission/Mortality

## RT-CEND Real Time Clinical Electronic Notifiable Disease Reporting

- Scott Evans, PhD, MS
- Catherine Staes, BSN, MPH, PhD
- Department of Biomedical Informatics
- University of Utah

## Background: Automated infectious disease MONITORING

- Implementation
- Intermountain Healthcare
- Major integrated healthcare system in intermountain west
- More recently, University Health Care and Salt Lake VA (TheraDoc)
- Computer alerts at Intermountain Healthcare
- Reportable diseases
- Other infections including healthcare-associated infection, resistant bacteria, antimicrobial alerts

## Slide 15

## 2

- Medical
- Record

- Laboratory
- Data

- Data
- Driver

- Decision
- Support
- Engine

- Medical
- Logic
- Modules

- Code
- Tables

- 3

- 4

- Alert
- File

- 5

- 1

## Alert

- File

- Time
- Driver

- Reportable
- Disease
- Monitor

- Electronic
- Medical
- Record

- ICP Daily
- Printout

## Reportable Disease Monitor output

- Lab: organism, detection method, 	collection 	date, received date, status.
-  Encounter detail: facility, patient ID.
-  Patient detail: name, gender, address, 	phone
-  Physician: name, number, phone, 	location.
-  Infection control practitioner: name, email, phone, location.

## Intermountain Healthcare: # unique reportable disease alerts 

- Intermountain Healthcare: # unique reportable disease alerts

## Background: problems with notifiable disease REPORTING

- Manual steps contribute to:
- underreporting
- delayed reporting
- inefficient data gathering
- Multiple data sources
- hospitals, labs, clinics, patients/families, employers
- Multiple data receivers
- local and state health departments
- Multiple report formats
- phone call, fax, email, mail

## Background: CURRENT data flow

- Daily
- report

- NETSS

- faxed

- Manual entry

- Local Health Dept

- Intermountain HC

- State Health Dept

- Email or fax

-  Alert file

- NETSS

- Manual entry

## Proposed research

- Implement an automated notifiable disease reporting process
- Evaluate reporting from both the health care system side and the public health side

## Research questions

- Public health
- What is the expected and measured impact on control of notifiable diseases following automation of the notifiable disease reporting process?
- Informatics
- How will automated reporting of notifiable diseases impact surveillance system attributes and workflow?

## Methods

- Intermountain Healthcare:
- Automate data transfer from IHC to UDOH using the HL7 message format
- Automate HIPAA documentation
- Utah State Dept of Health:
- Develop database at UDOH to store case reports.
- Develop secure interface with Local Health Departments and/or develop processes to ensure data flows appropriately between state and local public health.

## Simplified view of NEW data flow

- Daily
- report

- NETSS

- faxed

- Email or fax

-  Alert file

- Study/
- NEDSS

- Data views, reports, extracts

- NETSS

- Manual entry

- Manual entry

- HL7message

- Local Health Dept

- State Health Dept

- Intermountain HC

## Planned evaluation

- Measure sensitivity, timeliness, accuracy, and completeness of reporting
- Assess impact on workflow for local and state health department staff and infection control practitioners.
- Use simulation to estimate projected effect on control measures
- Measure actual impact on diseases for which there is standard tracking of control measures

## INTERACT Project

- Carrie L. Byington, MD
- Professor of Pediatrics and Infectious Diseases
- University of Utah

## INTERACT

- Acronym designed to express the overall Aim of the project which is:
- Enhance and Encourage using Information Technology Interaction between Utah Public Health Systems, Primary Health Care Providers and Public Schools

## Background

- Communication between clinicians and public health personnel is
- Inefficient for public health
- Perceived as hassle by clinical personnel
- Data potentially useful for public health surveillance remain locked inside health care systems
- Potential for improved clinical practice if population data effectively used

## Public Health Research Question

- Does integrated pathogen-specific and non-specific surveillance data improve public health assessment, diagnosis, prediction, and response to known and unknown respiratory pathogens?

## Clinical Research Question

- Will implementation of guidelines that are linked to community level pathogen-specific data improve pediatric management vaccine-preventable illness?
- Disease focus
- Influenza and pertussis

## Informatics Research Question

- Will the efficiency of public health case investigation be enhanced by the implementation of a bidirectional electronic communication system?

## Summary of research plan

- Year 1
- Integrate pathogen-specific and non-specific data
- Baseline surveys
- Year 2
- Implement guidelines linked to community level pathogen-specific data
- Randomize pediatric urgent care clinics
- Pilot electronic communication system
- Year 3
- Implement and evaluate electronic communication system

## Evolution of surveillance strategies

- The limitations of notifiable disease surveillance led to (begat):
- Syndromic surveillance
- The limitations of syndromic surveillance are now increasingly recognized
- Is a new paradigm for public health surveillance needed?

## Population Analysis of Routine Viral Diagnostic Testing 

- Four Intermountain Healthcare laboratory facilities perform the direct fluorescent assay (DFA) to detect respiratory viruses.
- Routine testing for the following:
- influenza A and B,
- parainfluenza 1, 2, and 3
- respiratory syncytial virus
- human metapneumovirus
- The Germ Watch program was developed in 2001 as an automated reporting system to produce pathogen-specific epidemic curves for use by front line clinicians

## Positive viral tests versus percent of visits for respiratory complaints

## Pathogen Non-Specific Data Sources for Public Health Surveillance

- Sentinel healthcare sites and ILI data
- Influenza-like illness defined as fever (≥100° F) and cough and/or sore throat- in the absence of a known cause other influenza
- Sentinel health care providers report patient visits for ILI each week
- Sentinel schools and absenteeism data
- Syndromic data and RODS

## Public Health Influenza Surveillance 

- Pathogen-specific data
- Influenza associated hospitalization
- Pediatric influenza associated deaths
- Long-term care facility outbreaks
- Viral typing by the Utah Public Health Laboratory

## Proposed paradigm

- Integration of pathogen-specific and non-specific surveillance data from diverse sources.
- Retrospective analysis
- Epidemic curves of ILI from sentinel clinics, of reported hospitalized influenza cases, and of the specific viral pathogens included in the Germ Watch program were compared.
- Prospective implementation
- Web-based data visualization tools

## Cumulative influenza A cases per season from Germ Watch

## Preliminary results

- Pathogen-specific data revealed transmission dynamics and epidemic behaviors of individual viruses more clearly than ILI surveillance.
- Initial insights that have been gained will be further described at the PHIN conference

## DSIDE Project

- Frank Drews, PhD
- Matthew Samore, MD
- Departments of Biomedical Informatics, Psychology, and Internal Medicine
- University of Utah

## 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 as one disease model

- 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
- Agent-based simulation models at different levels of fidelity

## Scientific core

- Wu Xu, PhD
- Director, Office of Public Health Informatics
- Utah Department of Health

## Office of Public Health Informatics

- Goals
- Coordinate statewide eHealth initiatives
- Coordinate UDOH integration projects
- Create a laboratory for applied public health research
- Develop theory and methods that advance information and population science in the context of public health practice

## Action Research = TRIP or TPIR?

- _Action research_ can be described as a family of research methodologies which pursue action (or change) and research (or understanding) at the same time. _[http://www.scu.edu.au/schools/gcm/ar/whatisar.html](http://www.scu.edu.au/schools/gcm/ar/whatisar.html)_
- TRIP: Translate Research Into Practice
- TPIR: Translate Practice Into Research

## Action Research/TRIP on Development of an Integrated Public Health Data Resource

**Needs defined **
**by practitioners**

**Action Research Questions:**
- Select appropriate matching software
- Measure quality of linked data
- Develop standard uses of linked data
- Develop an operational system

- 1. Compared
- Automatch,
- Link-King &
- LinkSolve by
- de-duplicating
- hospital
- discharge
- Data &
- linking birth
- records

- NAHDO/RWJ?
- Other States

- 2. Develop
- quality
- Measures &
- track linking
- statistics:
- -Link rate
- -Unlinked bias

- Partners

- 3. Develop indicators:
- readmission rates
- Post-discharge
-   mortality rate

- 4. Develop a UDOH plan w.
- the Environmental Tracking
- System

- Partners

- HLN Consulting &
- voluntary participating
- public health programs

## Summary

- Matthew Samore, MD
- University of Utah and VA Salt Lake City Health Care System

## Progress to Date 

- Significant gelling and development of shared vision
- Planned presentations and posters at PHIN and other meetings
- Developing and expanding regional and national collaborations is a priority