managing data-quality-in-an-integrated-surveillance-system
TRANSCRIPT
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?