*Purpose: Coordinating the management of patients’ health statuses, tracking and scheduling testing, is a complicated process and involves a team of clinicians including transplant coordinators, nephrologists, surgeons and social workers. Currently, transplant coordinators must manually communicate with the other clinicians, track patient tests and follow up with patients who miss required testing. Such a process leaves a lot of room for human error and increases the burden and burnout of coordinators. Moreover, patients are confused by this process and may not be engaged in their care, resulting in worse health outcomes. To end this, we propose the development of a kidney transplant management and decision support system, which will automate test reminders, assignments and tracking, succinctly summarize and store relevant patient health statuses using a visual dashboard, and use patient data and machine learning techniques to learn a patient risk score that characterizes their risk/survivability of a kidney transplant. Such a system will improve coordination between the actors in the transplant care team, reduce transplant coordinator burnout, facilitate clinician decision making for patient treatment and waitlist decisions, and engage patients in their health management in order to improve transplant outcomes.

*Methods: The tracking management system organizes the transplant waitlist and patient statuses and facilitates test assignments by suggesting specific tests for patients based on their demographic information, in order to maximize efficiency and minimize the potential for human error. It also contains a patient portal allowing patients to view their health information and test results, as well as receive reminders about upcoming appointments

*Results: The decision support portion of the system facilitates clinician decision making by providing a machine learned and summary dashboards with visualizations that intelligently aggregate patient statuses. It also contains reminders and to do lists for clinicians and coordinators, contacting patients who have missed multiple tests, in order to reduce some of the burden placed on transplant coordinators.

*Conclusions: The system is a prototyped and will be deployed for use at the UVA kidney Transplant Clinic. Through the use of an intelligent and automated tracking management and a decision support this system will improve clinic efficiency and reduce the potential for error for both the clinical transplant teams and their patients.

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