The Comparative Effectiveness Research Hub (CER Hub)
The primary goal of this project is to create and evaluate an Internet-based “Comparative Effectiveness Research Hub” (CER Hub) where researchers can collaboratively develop standardized problem-specific processors of electronic clinical data (with medical classifier applications, or MediClass) in support of CER studies. The project aims to develop a platform for CER with the capacity to generalize along dimensions to function in any EMR data environment, to integrate health care information from diverse settings and clinical practices, and to address any CER questions where both free text and coded EMR data would be required. The project team is a consortium of researchers from 6 diverse health systems assembled to answer key CER questions in asthma control therapy and smoking cessation care delivery. For more information, visit www.cerhub.org.
1. Create and evaluate a platform for collaborative development and conduct of healthcare research and quality studies that use distributed, heterogeneous, electronic clinical data; and
2. Make a proven natural language processing (NLP) technology broadly available for enhancing use of EMR data.
Primary Research Aims
Research projects using CER Hub technologies are formed as investigator-led communities focused on CER. Current projects include observational analysis of heterogeneous clinical data for asthma control therapy and smoking cessation care delivery.
Primary care or ambulatory clinics, Inpatient facilities
Geographic scope type
Locations of Focus
multiple regions across the US. Areas covered by the network include the states of Georgia, Hawaii, Oregon and Washington; the city of Houston, TX; and the region of southern California (Bakersfield to Los Angeles)
Population Network Size
Centralized web platform with distributed processors of heterogeneous data. Data model is derived from HL7 Clinical Document Architecture. Distributed data processor is built from the MediClass medical record processing engine. The MediClass system (a “Medical Classifier”) incorporates natural language processing and knowledge based systems technologies to automatically code clinical data of all types to address many CER questions. The system uses an emerging standard for representing the complete medical record; therefore data from any EMR implementation can be uniformly processed.
Kaiser Permanente Northwest (Lead Site); Kaiser Permanente Southern California; Kaiser Permanente Hawaii; Kaiser Permanente Southeast; Baylor Health Care System; OCHIN Inc. (a consortium of Community Health Centers and FQHC organizations); Veterans Affairs Puget Sound Healthcare System
CER/PCOR Study Priority Populations
Low-income groups, Minority groups, Individuals who need chronic care
Outcome(s) of Interest
Optimal use of asthma medications; Increased smoking cessation rates
Acknowledgement of Funders
“The CER Hub project (www.cerhub.org) is funded by grant R01HS019828 from the Agency for Health Care Research and Quality (AHRQ), US Department of Health and Human Services.