Data centric approach to CME

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All learners in the continuing medical education (CME) space acknowledge and recognize the value of the content. However identifying, collecting and using data to show evidence of needs for new education and the impact of the learning on quality of care and changes in physician behaviors continue to be challenging. CME is also challenged with the diversity in terms of learners, their learning styles, actual vs. perceived needs and regulatory changes. In addition, most data that is reported to show evidence of change in physician behavior due to the educational intervention are self reported. New requirements like Practice Improvement (PI) CME are opportunities to offer educational formats that could allow for the collection of data that showed evidence of impact on patient outcomes. Integration of technology in the design of education and its delivery through various media has significantly enhanced the engagement and interaction levels in an educational activity or program, but has not made measurement of outcomes and collection of data to show impact of learning any easier. Electronic medical records are considered to be an essential tool to improve health care efficiency and enhance outcomes1. Communication and documentation of clinical practices, decision making and individual physician behaviors recorded through the EMR could provide a means for collection of data that helps identify the professional practice gaps and hence educational needs. In addition, observing patterns in clinician performance and practices can provide evidence of outcomes. Well integrated clinical information systems in a community practice setting, a small or large hospital or hospital network can provide significant amount of data towards educational needs and outcomes in the using the following data

• Adherence to or deviations from clinical guidelines during patient care • Patterns in single orders and order sets for diagnosis of diseases • Approaches to managing long term and short term treatment plans • Medications prescribed for treatment of diseases and measures of adverse drug effects, near misses and successes • Patient demographics, population health statistics • Billing, coding and trends in business practices

All of these data coupled with clinical research, public health data and mapping trends in practices to competencies can provide valuable information towards needs and outcomes for CME. Reporting for pay for performance, physician e-portfolios and medical registries are also sources of data. The key is in analyzing all of these data, identifying the most appropriate ones, and creating a framework for their use. Aggregate data from the EMR can be used to identify areas of improvement or gaps for physicians which when mapped to competencies can be used as needs data. The physicians can then go through an educational intervention geared towards addressing the gaps after which their behaviors and approaches to diagnosis, treatment plans and management can be evaluated. This allows for changes in behavior or any improvements can be assessed. The willingness for physicians to share their performance data, especially with a focus to find areas of gaps or needs is a challenge.

Data from the EMR can be a valuable source to help CME providers identify practice gaps real time, evaluate changes over a period of time, work across distributed network of doctors and communities. Defining a methodology for use of such data for use in CME could help resolve many of the issues related to outcomes measures and identifying needs for CME.


Nissen et al., The role of technology to enhance clinical and educational efficiency, J.AM.Coll.Cardiol. 2004; 44; 256-260

McKean et al., Curriculum Development: The venous thromboembolism quality improvement resource room, Society of Hospital Medicine, 2006, published online at

Horsky I, Kuperman J.G., Patel L.M., Comprehensive analysis of a medication dosing error related to CPOE. J Am Med Inform Assoc. 2005, 122: 377-382

Submitted by Chitra Pathiavadi