Improving Medical Staffing in Long Term Care During Pandemic

Ozlem Ergun

MIE Professor Ozlem Ergun was awarded a $101K NSF RAPID grant for “Collecting Supply, Demand, and Matching Data for Assigning Medical Staff to Long Term Care Facilities During the COVID-19 Pandemic”.

See Related News: Matching COVID Care Workers with Open Mass. Jobs

See Related News.

Abstract Source: NSF

The ongoing COVID-19 pandemic has resulted in a surge in demand and shortages of clinical and non-clinical personnel to treat patients in long term care facilities. Long term care facilities have proven to be hotspots for COVID-19 outbreaks and medical personnel in these facilities themselves faced increased risk of disease, making staffing decisions very difficult. This Rapid Response Research (RAPID) project will collect data on staffing, disease prevalence, and absenteeism from a centralized authority that oversees staffing in these facilities. These data will help support better decision support tools to dynamically match medical staff with different skills and preferences to long term care facilities on a daily basis.

Working with the the Massachusetts Executive Office of Elder Affairs, the project will collect and archive data from the long term care facility portal on daily needs for clinical and non-clinical medical staff, job applications, absenteeism rates, facility testing data, as well as qualitative information from daily state-wide phone conference status updates. The data collected as part of this project will be valuable in improving the dynamic matching process for regional staffing of long term care facilities.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Related Faculty: Ozlem Ergun

Related Departments:Mechanical & Industrial Engineering