Software Leveraging a Standards-Based Web Service Framework for Decision Support

Period of Performance: 09/01/2008 - 08/31/2010


Phase 2 STTR

Recipient Firm

Religent, Inc.
Morrisville, NC 27560
Principal Investigator


DESCRIPTION (provided by applicant): Despite their demonstrated effectiveness, clinical decision support systems are not widely used to assist decision making in routine clinical practice. The long-range goal of this proposal is to make clinical decision support more available and more usable in healthcare through the further development and productization of a standards-based Web service framework for decision support known as SEBASTIAN. SEBASTIAN is the basis of the recently approved Health Level 7 Decision Support Service international standard, and it is aligned with the Roadmap for National Action on Clinical Decision Support commissioned by the Office of the National Coordinator for Health Information Technology. By delivering clinical decision support capabilities through a standards-based service interface, SEBASTIAN enables the same machine-executable medical knowledge to be leveraged by multiple clinical applications that are deployed across various clinical and technical environments. This project responds directly to several areas of interest of the National Institutes of Health, including the development of "systems that facilitate utilization of electronic medical record systems in clinical practice for decision support" (National Library of Medicine) and the "development and validation of tools for use by healthcare providers/systems to improve diabetes care and prevention" (NIDDK). Through a Phase I STTR project, SEBASTIAN has been integrated into Duke University Health System's primary clinical information system to provide point-of-care decision support on diabetes management to clinicians throughout the health system. Additional clinical modules are being actively developed to support the management of other chronic diseases. Having successfully demonstrated the ability of SEBASTIAN to meet the decision support needs of a large university health system, this Phase II STTR project will focus on three specific aims to develop SEBASTIAN into a product that can be leveraged by a variety of healthcare organizations. Aim 1 is to convert SEBASTIAN from a software application used within an academic health system to a product that can be deployed in diverse settings. Aim 2 is to use a productized version of SEBASTIAN to implement point-of-care chronic disease management capabilities for diabetes and other conditions within a Regional Health Information Organization (RHIO) in southeastern North Carolina. Aim 3 is to evaluate process and care quality measures related to the use of SEBASTIAN for chronic disease management of diabetes and other conditions in a RHIO. This evaluation will be conducted through a cluster randomized controlled trial based on a sequentially staged rollout of the application. Duke University and Religent, Inc. will collaborate to develop the software components required to productize SEBASTIAN and to integrate it within the selected RHIO. Future uses for this system include supporting pay-for-performance initiatives for clinicians and improving care quality metrics for health plans. In addition, in two or three sentences, describe in plain, lay language the relevance of this research to public health. If the application is funded, this description, as is, will become public information. Therefore, do not include proprietary/confidential information. Successful development of a Web service-based decision support system should increase the availability and usability of decision support tools in diverse settings for various applications. It should also enable centralized management of medical knowledge resources and enable sharing of computable medical knowledge across a care delivery system or a geographic region. Increased use of decision support tools can be expected to improve care quality, reduce medical errors, lower healthcare costs, and augment disease management for patients and populations.