Optimal health depends not only on access and utilization of care but also on the analysis and implementation of knowledge gained from data. Electronic Health Record (EHR) data has greatly increased the quantity of data available to understand and improve population health. However, the lack of standardization in dental EHR and the lack of integration with medical records creates unique challenges in dental public health research. This webinar presents the role of big data in understanding oral health, highlighting the complex nature of the data for analysis, and the promises and pitfalls associated with its use.
Topics discussed will include:
- Analytical roadblocks experienced when using large-scale electronic health records datasets and how to overcome them
- Research using medical/dental integrated data to improve health, with examples
- EHR interoperability and opportunity to improve outcomes
The webinar closes with a discussion about how increased interoperability of dental and medical data can be used to improve the overall and oral health of patients.
- To improve knowledge of big data analytics and its limitations within the current system
- To better understand how the application of data, through analysis of diabetes and antibiotic stewardship, can improve and maintain the health of patients
- To highlight and identify where policy, care, and interoperability gaps exist, and how closure could significantly improve outcomes
- Julie Hawley, Ph.D., CAE, Director, Analytics & Evaluation, DentaQuest Partnership for Oral Health Advancement
- Eric Tranby, PhD, Data & Impact Manager, Analytics & Evaluation, DentaQuest Partnership for Oral Health Advancement
- Tamanna Tiwari, MPH, MDS, BDS, Assistant Professor, Department of Community Dentistry & Population Health, Associate Director, Center for Oral Disease Prevention & Population Health Research,
School of Dental Medicine University of Colorado
- Munder Ben-Omran, BDS, MS, Postdoctoral Fellow in Oral Health Informatics, National Institute of Dental and Craniofacial Research