Consolidating Information Participants Participants –H. Burkom- Barbara Spratkes-Wilkins –J. Coberly - Stella Tsai –K. Cox Questions Questions –How do we address/resolve the use of multiple systems? –How do we consolidate information, coordinate, and communicate across jurisdictions, systems & analytical results?

What is Consolidation? Combining diverse sources Combining diverse sources What sources? What sources? –Raw data –System results (information), ex. alerts On same data On same data On differing data (same streams), e.g., diagnosis vs. CC vs. lab orders vs. drug prescriptions within a given system On differing data (same streams), e.g., diagnosis vs. CC vs. lab orders vs. drug prescriptions within a given system

When Do You Consolidate? Is it really necessary? Is it really necessary? Need to fuse all results or just representative samples? Need to fuse all results or just representative samples? At what level is fusion needed? At what level is fusion needed? –Within a system –Between systems looking at the same data with different underlying algorithms –Between systems looking at different parts of the data –Between systems looking at different regions

How Do You Consolidate? Optimal – Do Not Consolidate Optimal – Do Not Consolidate –Consensus – Take time, not sure really want this 2 nd Best (Reality?) 2 nd Best (Reality?) –In your head –Statistically Fusing results from multiple data sources within a single system into a single alert Fusing results from multiple data sources within a single system into a single alert –Some misgivings Fusing results from multiple systems, meta-analysis-like Fusing results from multiple systems, meta-analysis-like –NOT recommended –Visually with a computer interface showing multiple sources/results simultaneously Good but have to have drill down capability Good but have to have drill down capability

Rules of Consolidation Must lead to actionable information Must lead to actionable information Data from the same underlying population Data from the same underlying population Must understand the characteristics & limitations of data being joined Must understand the characteristics & limitations of data being joined Data should be complementary Data should be complementary Carefully evaluate the utility of meta statistics Carefully evaluate the utility of meta statistics Must be able to drill down into data details in all data sets Must be able to drill down into data details in all data sets All data sets must (?) offer similar level of granularity All data sets must (?) offer similar level of granularity

What Is Needed? Common definitions Common definitions –Ex. Syndromes Have the system in place before an event Have the system in place before an event –Relationships –Protocols –Resources