Executive Summary

In 2004, President Bush set a national agenda for health care information technology adoption when he shared his vision with the nation that every American have an electronic health record (EHR) by 2014. While some progress has been made, overall EHR adoption rates remain low in small physician practices. Simon et al. (2007) report adoption rates are lower in smaller practices, those not affiliated with hospitals, and those that do not teach medical students or residents.

The focus of the Economic Analysis of Health Information Technology (EA HIT) in the Ambulatory Setting project was to develop and evaluate a simulation tool/computational model (Model) used to predict adoption rates of Electronic Health Records (EHR) in small physician practices (less than 10 physicians).

iTelehealth Inc. supported MDM Strategies, Inc. in leading the EA HIT Phase II work for the Department of Health and Human Services (DHHS), Assistant Secretary for Programs and Evaluation (ASPE). The EA HIT project focused on:

• Developing a predictive Model that incorporates guidance from an objective expert panel team
• Developing a presentation tool to make the Model available to users
• Conducting evaluation of the Model using third-party data sources
• Collecting data for the model through a limited online study of small practice physicians adoption perceptions and variables
• Based on Model outcomes, identify/validate attributes related to the EHR adoption and implementation issues (e.g., policies, incentives, cost of ownership, etc.)

Successful execution of the Phase II activities resulted in completing a process of Model
development by collecting of a large amount of independent data, the normalization of that data, and the development of four (4) predictive Models. With each Model version, the usability, robustness, and predictive power of the Models increased. The modeling process showed that the decision to adopt an EHR is a very complex process, involving many inter-related factors and perceptions. In order to effectively encourage adoption, any solution has to account for multiple factors to include, but not limited to: clinical, economic, demographic, technological, and perceptual. Given the results of the EA HIT modeling exercise, single factors or overly simplified initiatives are likely to have unintended results. The EA HIT Model provides policymakers with a tool that allows for some prospective quantification of these results, supporting more finely tuned policy initiatives. The Model can support complex and detailed “what-if” scenario analysis as well as the identification of perceptions and factors that bear on practice adoptions choices. Once identified, these factors can be better studied and/or addressed.