Managing Data Quality in an Integrated Surveillance SystemRachelle Boulton, MSPHDCP Informatics Program

April 27, 2016

NETSS STD MIS

TIMS eHARSArboNet

Historically Siloed Databases

NETSS

STD MIS

TIMS

eHARS

ArboNet

UT-NEDSS

NETSS

STD MIS

TIMS

eHARS

ArboNet

UT-NEDSS

Blood Lead

HAI

IntegrationBenefits Challenges

Streamline data collection

Acceptability

Reduce redundancy User varietyStandardization StandardizationDisease overlap Siloed federal

databases

Electronic Data Collection•More standardization•Volume and velocity

Electronic Data Collection•More standardization•Volume and velocity

ELR

Where Do We Start?

DCP Informatics Program, 2014

Updated 4/26/2016

Jennifer BrownDivision Director

Kurt LiedtkeJava Programmer

Susan Mottice, PhDELR Coordinator

Jon ReidHealth Informatics Manager

Josh RidderhoffPHP Programmer

Rachelle Boulton, MSPHEpidemiology Liaison

Data Management

DCP Informatics Program, 2016

Updated 4/26/2016

Jennifer BrownDivision Director

Kirk Benge, MPHELR Coordinator

Rachelle Boulton, MSPHEpidemiology Liaison

Data Management

Theron Jeppson, MEd, CHESHealth Promotion Liaison

ELR, Syndromic Surveillance Onboarding

VacantHealth Informatics Manager

Joel Hartsell, MPHeCR Coordinator

Amanda Whipple, MPHProject Coordinator

Rocio RamosResearch Analyst

Glenda GarciaOffice Specialist II

Joe Jackson, MBADTS IT Manager

JoDee Baker, MPHNEDSS Product Manager

Allyn NakashimaState Epidemiologist

Kurt LiedtkeJava Engineer

Josh RidderhoffPHP Developer

Doug McGowanPHP Developer

Mike WhisenantJava Engineer

Define Data Quality

Define Data Quality•Two separate concepts

▫Data integrity management▫Process management

•Two separate processes▫Quality control▫Quality assurance

Next Steps•Identify quantifiable parameters•Develop protocols•Test it!

Metrics•Completeness•Timeliness•Data source•Accuracy•Validity•Precision

FlowchartsType Process ComponentProcess Mapping

Surveillance Quality

Process Management

Decision Support

Investigation Quality Data Integrity

Classification Data Quality Data Integrity

No Interest!

Trainings1. Speak the same language2. Roles and responsibilities3. Identify barriers4. Introduce metrics and flowcharts

Roadblocks to Data Quality•Undefined data quality roles•No accountability•High staff turnover•Poor documentation and dissemination•Poor training•Limited standardization•Difficult, ambiguous process for change

Solutions to RoadblocksProblem: Undefined data quality roles

UDOH epidemiologists – surveillance managersNEDSS surveillance and data quality manager

Solutions to RoadblocksProblem: No accountability

NEDSS manager position

Solutions to RoadblocksProblem: Poor documentation and

disseminationKnowledge management system

Solutions to RoadblocksProblem: Poor training

Prioritized higherDedicated resources

Solutions to RoadblocksProblem: Process for change

Streamlined protocol

What’s Next?