: Clinical data is required in order to derive value from most health information technology. Because the Nation's healthcare system is fragmented, these clinical data are fragmented and not available at the point of care. Health information exchange 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 will refine an established economic model of health information exchange (HIE), create a """"""""laboratory"""""""" in which that model can be tested and, finally, test the model's predictions in a randomized controlled trial. An existing HIE (the Indiana Network for Patient Care) will be used as the foundation for this project. In addition, several payers have been engaged in this project in order to provide part of the data, but also to lay the foundation for changes in reimbursement models based on the findings. The economic model of HIE, developed by the Center for Information Technology Leadership (CITL) from a national perspective, will be modified to support its use on a regional basis and then to validate the model using data from this project's randomized trial. In order to perform the randomized trial of HIE in the ambulatory setting, the applicant needs to create a """"""""laboratory"""""""" of physician practices and data sources that will enable the measurement of the effects of HIE in the ambulatory setting. The investigator will model this """"""""laboratory"""""""" after the emergency department model which has now been used for two large trials. Finally, investigators will carry out a randomized controlled trial of HIE in the ambulatory setting by delivering clinical data to providers from across the entire community. The country is faced with a crisis in health care costs, quality and safety and there is increasing evidence that health information technology may be part of the solution. In order for many of these technologies to deliver on their promise, they need clinical data, data that resides in fragmented and unconnected databases but that can be brought together by health information exchange. ? ?
| Shen, Changyu; Li, Xiaochun; Li, Lingling et al. (2011) Sensitivity analysis for causal inference using inverse probability weighting. Biom J 53:822-37 |
