227107 Electronics health records, knowledge management, informatics and the potential to differentiate health disparities

Monday, November 8, 2010 : 2:50 PM - 3:10 PM

Peter Kos, MS, ME, PhD candidate , School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI
Vincent Fusaro, PhD , Center for Biomedical Informatics, Harvard Medical School, Boston, WI
Matt Tector, PhD , Aurora Health Care, Milwaukee, WI
Peter Tonellato, PhD , Center for Biomedical Informatics, Harvard Medical School, Boston, WI
EHRs are a primary data source used to differentiate public health disparities. The value of EHR's is limited by technical complications of normalizing and merging data with secondary public health data sources. To overcome this difficulty, we created a process to merge healthcare and secondary public health information into a cohesive de-identified public health research system. We use methods developed for biomedical informatics research and demonstrate the value of these methods to develop evidence associated with health disparity. Our initial effort repurposes and merges EHR data to create a Milwaukee Area Public Health Research Database (MAPHRD) using a cloud computing enabled ‘i2b2' platform. A combination of de-identified health records coupled to clinical ‘avatar' health records (fictitious records that reflect actual data and are statistically consistent with patient populations) are used to demonstrate the process. The MAPHRD HIPAA compliant, automated pipeline can be customized and anonymizes and transforms EHR data into i2b2 standardized format following federal HIT standards.

The rapid expansion of EHRs provides opportunity to calibrate and quantify outcomes of standardized health practice. “Meaningful use” outcome metrics can then be computed and used to quantify health disparities thereby providing evidence to modify public health policy to minimize or remove the factors causing the disparity. Our system and method produces standard data from complex medical care EMR data and defines relationships between complex EMR data elements and public health outcome objectives correlated to the recently released HHS meaningful use criteria of ‘data mining key health issues and reporting public health measures'.

Learning Areas:
Basic medical science applied in public health
Communication and informatics
Public health or related research

Learning Objectives:
1. Evaluate recently announced HHS “meaningful use” outcomes. 2. Explain the importance of meaningful use criteria to public health objectives. 3. Identify standardized data models used to analyze EHR and secondary public health data.

Keywords: Public Health Informatics, Public Health Research

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a Professor of the School of Public Health, University of Wisconsin, Milwaukee, WI who directs a research lab focused on the use of electronic health records and other sources of public health data in comutational simulations and analysis to predict public health outcomes related to the use of new methods, devices, or genetic tests. In addition, my research demonstrates the potential for health care disparities.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.