Four Approaches to Healthcare Enterprise Analytics. Herb Smaltz, Ph.D., FHIMSS, FACHE Chairman & Founder Health Care DataWorks

Transcription

1 1 Four Approaches to Healthcare Enterprise Analytics Herb Smaltz, Ph.D., FHIMSS, FACHE Chairman & Founder Health Care DataWorks

2 4 Options (plus a myriad of hybrid approaches) 1. Leverage Core Vendors EMR ERP Reporting and/or analytics capabilities that core vendors such as EMR or ERP vendors provide 2. Leverage Focused Point Solutions Very focused point solutions that provide analytics and/or data intensive workflow applications (e.g. a physician performance scorecard)

3 4 Options (plus some a myriad of hybrid approaches) 3. Leverage Data Discovery & Visualization Tools EMR Tools that organizations use to attach either directly to key applications (such as an EMR) or to a database and build their own visualizations (e.g. scorecards, reports, etc.) 4. Leverage a Data Warehouse EDW A separate data repository to house key data generated in the myriad of health system and practice management applications Contemporary EDWs provide dashboards, adhoc reporting, data mining, predictive modeling and can logically leverage external data sets

4 Option 1 Leverage Core Vendors EMR ERP Reporting and/or analytics capabilities that core vendors such as EMR or ERP vendors provide Pros Provides about half of a health system s reporting needs Can be integrated into core vendor embedded workflows Cons Incomplete data: generally only able to provide reporting on data contained within application Historically not a focus area of core vendors (though many now have strategies to address this need) Unable to deal with growing Big Data need (velocity, variety, and volume of data)

5 Option 2 Leverage Focused Point Solutions Very focused point solutions that provide analytics and/or data intensive workflow applications (e.g. a physician performance scorecard) Pros Typically good at providing reporting/analytics in the focused area Often includes benchmarking data in the focused area Provides departmental autonomy in meeting individual needs Cons Siloed data: only able to report on data contained within application Challenging to analyze data across multiple applications Unable to deal with growing Big Data need (velocity, variety, and volume of data)

6 Option 3 Leverage Data Discovery & Visualization Tools EMR Tools that organizations use to attach either directly to key applications (such as an EMR) or to a database and build their own visualizations (e.g. scorecards, reports, etc.) Pros Rapidly explore different data sets and create dashboards and scorecards Eliminate the need to extract data from existing applications or databases Enable users to explore data with little training Cons Unable to normalize heterogeneous data from multiple applications/databases Potential to degrade the performance of the applications/databases Requires skilled resources to build and maintain metadata/data dictionary Limited ability to deal with growing Big Data needs

7 Option 4 Leverage a Data Warehouse EDW A separate data repository to house key data generated in the myriad of health system and practice management applications Contemporary EDWs provide dashboards, adhoc reporting, data mining, predictive modeling and can logically leverage external data sets Pros Curated data provides a single version of the truth Self-help access by greatest number of authorized users The only comprehensive, enterprisewide solution Cons Requires organizational maturity, enterprise data governance, and management to implement Takes the longest time to fully implement LEDWs able to deal with growing Big Data need (velocity, variety, and volume of data)

8 8 Gartner Research: State of Data Warehousing in 2013 and Beyond (Edjlali & Beyer) Data marts are driven by specific end-user needs. If organizations have data marts that are not part of a data warehousing strategy, they must bring them into the data warehouse architecture, rather than run them as silos, and put in place design standards that make this possible in an affordable way. Despite doubts about its future importance, the data warehouse will remain one of the largest if not the largest information repositories in the enterprise. It will continue to be the best solution for housing metadata that enables the analysis and use of the rapidly increasing volume and variety of information. As adoption of the logical data warehouse expands, concerns about the value of the traditional data warehouse will fade.

9 Enterprise Data Warehouse 9

10 Logical Enterprise Data Warehouse 10

11

12

13

14

15

16 16 Thank you For more information please contact Herb Smaltz