Equity is the absence of systematic disparities in healthcare delivery (e.g., access, quality, cost and outcomes) between groups of people.  Access has many elements including distance and resources for healthcare. This seed grant initiated our first detailed examination of equity in access across the state of Georgia, and it laid the foundation for a number of other ongoing studies.

Aim 1: Quantify the access to healthcare for pediatric patients, including access to specialized care.

Medically complex children refer to patients who need intense medical care due to multisystem dysfunction, technology dependence, or complex medication needs. Medically complex patients often consume largely disproportionate amount of different care resources in hospitals. Understanding the nature of complexity and predicting the dynamics of complexity in patients can help design better personalized care plans in order to improve quality and reduce cost.

This project created a interactive digital website to explain the value of early intervention for children with ASD.  The project created a comprehensive and original synthesis of the literature on early intervention for ASD, by age of first intervention and severity of diagnosis.  We estimated the total lifetime costs of direct services for children, as well as the indirect services needed by the family.  We designed interactive tools to explain these costs and outcomes to a lay audience, intended for GA legislative staff and lawmakers.

The GT team built a discrete event model to test various schedule templates and resource allocation, and worked with the Sibley team to create an optimized scheduling model which minimized patient waits and visit durations, while giving the Sibley team time in the middle of the day with no patients in the clinic. Through these simulations, processes creating delays in the system were identified, and the Sibley team was able to modify these practices, through education and making necessary information available more quickly.

The goal of this proposal is to develop and evaluate an integrative prediction model for diagnosis of pediatric heart transplant rejection that uses both genomic and histopathological image data. Rejection of the donor heart by the recipient is the most common cause of mortality in the pediatric heart transplant population. Unfortunately, diagnosis of rejection using current technology (i.e., histological analysis of endomyocardial biopsy samples [EMB]), is subjective, inaccurate, and imprecise.