Biography:
Elizabeth Miller Diamond had her undergraduate studies in Medical Technology (ASCP) from the University of Connecticut 1983. She moved to
Central America and managed the clinical laboratory at Western Regional Hospital in Belmopan, Belize. Upon her return to the United States in
1986, she pursued Medical Technology positions at the following organizations: Farmington, CT Chapter of the American Red Cross, Laboratory
Corporation of America, and Duke University Hospital Adult Blood and Bone Marrow Transplantation Clinic. Her Bioengineering Master’s degree
is from the renowned HBCU, North Carolina Agricultural and Technical State University. Elizabeth began her doctoral studies in Industrial and
Systems Engineering with a Fellowship from the National Science Foundation grant, NSF IS4GOOD NRT: Improving strategies for hunger relief and
food security using computational data science. She studied and volunteered at the industry partner, Community Support and Nutrition Program
(CSNP), developed by the One Step Further agency in Greensboro, NC.
Abstract:
Statement of the Problem: Located at 1806 Merritt Drive in Greensboro, NC, Community Support and Nutrition
Program (CSNP), is a food pharmacy market with an attached safety-net health clinic for patrons experiencing
food insecurity and chronic health conditions. Users’ needs go far beyond food even though that is why they come
to the food market. Administrator Susan Cox notes, “We know that food insecurity does not happen in isolation.”
To help patrons move away from food insecurity, we must involve the community and its resources. The purpose
of this study is to identify patrons who need further healthcare. Doctors, nurses, dietitians and interns at One
Health, LLC are One Definition Church member volunteers, who are healthcare professionals, work in tandem
with the CSNP food market to prescribe food that can help manage four chronic health conditions affecting
patrons. Methodology is longitudinal study from in depth interviews, conversations, and field notes with an
inductive method and thematic content analysis. Descriptive analysis with MATQDA software allows for data
processing. Then, data will be trained, validated, and tested for predictive analysis with R programming language.
Findings: Here we predict a patron to contract one of four commonly recognized chronic conditions based on the
socioeconomic, sociodemographic, and medical data. The conditions are Type II Diabetes, Hypertension, Renal
and Celiac Disease. Here we predict a patron to contract one of four commonly recognized chronic conditions
based on the socioeconomic, sociodemographic, and medical data.