Value of Health Information Exchange in Ambulatory Care

Clinical data is required in order to derive value from most health information technology. Due to the fragmentation of the Nation's health care system, these clinical data are fragmented and not available at the point of care. Health information exchange (HIE) is the term used to describe efforts to aggregate clinical data for patients across disparate organizations in order to form a more complete picture of their care that improves clinical care and quality, research, and public health.

This project modified an existing economic model of HIE, developed by the Center for Information Technology Leadership from a national perspective, and applied it to a specific geographic community (Indianapolis metropolitan statistical area) in order to determine the expected savings for the community. This economic model was modified to support its use on a regional basis and then validated using data from this project's randomized trial. In order to perform the randomized trial of HIE in the ambulatory setting, the Regenstrief Institute from the Indiana University School of Medicine created a "laboratory" of physician practices and data sources that will enable the measurement of the effects of HIE in the ambulatory setting. The investigator modeled this "laboratory" after the emergency department model, which has been used for two large trials. In addition, investigators are in the process of carrying out a randomized controlled trial of HIE in the ambulatory setting by delivering clinical data to providers from across the entire community. Initial outcomes of this initiative show that from the providers' perspective, most of the data accessible through the HIE duplicates what they can access through their system, so the HIE works better as a "pull" approach, rather than a "push" approach as originally thought.

An important finding includes a method to categorize projected savings, including three categories - hard, soft and shadow savings. Hard savings are those that a practice can actually expect to achieve. Soft savings are those that free resources for other purposes but do not actually result in a reduction of expenditures. Finally, shadow savings are those that the model predicts the practice should achieve, but in fact, the practice is not doing those activities, so no savings will occur even though value might be added.