Intelligent Patient and Resource Management for the Medical Enterprise

Scheduling of medical appointments across the military enterprise presents a number of problems. Studies done of the responsiveness of numerous Military Treatment Facilities have highlighted the need for improvements in the scheduling process. In response to these findings, the Department of Defense has set ambitious targets for improvements in patient responsiveness, such as minimal waiting times for intake, and minimal times from appointment request to appointment date, and the ability to do same day appointments for ancillary laboratory visits. The ability to deliver on these service commitments will hinge upon being able to not only do optimal (with respect to service commitments) scheduling amongst the units of a single MTF, but amongst facilities that are geographically dispersed. It will require the integration of appropriate patient information and integration with other existing infrastructure that facilitate the patient's ability to determine appointment times based upon their preferences. It will require interfaces that allow providers to enter appropriate business logic for their unit in natural ways, as well as facilitate the use of advance scheduling algorithms that can effectively information about resource requirements and availabilities, staff availabilities, and coverage constraints to fast appointment and resource planning. Recent developments in artificial intelligence point to a number of approaches to scheduling that could yield significant improvements in the patient experience, if applied. We propose to investigate such approaches using our Aurora scheduling system as a core technology. We will extend this system to allow for integration with existing DOD healthcare infrastructure.