Doctor of Nursing Practice, Mount St. Joseph University , 2020, Department of Nursing

Executive Summary Lack of a standardized patient report from the emergency department nurses to the patient flow coordinators provided insufficient information to place patients correctly and efficiently. Incomplete information resulted in multiple phone calls to retrieve additional information, incorrect patient placement, and delayed decision-making. Delayed inpatient admissions contributed to overcrowding in the emergency department (ED), nurses pulled away from patient care to seek clarifying information, inefficiency of the flow coordinator in planning for inpatient admissions, and delayed inpatient care. A standardized ED patient report form was developed to decrease repetitive phone calls, improve efficiency of decision-making, and decrease the time patients spend in the ED. The purpose of this Doctor of Nursing Practice (DNP) project was to create a standardized report form to guide the ED nurses in providing sufficient patient information to the patient flow coordinators for accurate and efficient patient placement. The Epic Software program was used to identify time stamps for tracking the flow of patients throughout the hospital. These metrics included the time stamp for requesting admission, floor assigned, and arrival to the floor.This DNP project determined if a standardized patient placement report would decrease the time to place a non-critical care patient from the ED to the inpatient area. Findings of this project revealed that although nurses were more informed about what information was needed for patient placement, the approach to improving patient placement is multi-factorial. Recommendations from this project included establishing a standardized report for patient placement in Epic that could be reviewed by multiple teams, thereby removing verbal report between the ED nurse and patient flow coordinator.

Committee: Laura Valle DNP, APRN-CNM (Advisor) Subjects: Health Care Management; Nursing

Master of Science, The Ohio State University, 2024, Biostatistics

Accurate forecasting of weekly number of influenza (flu) lab tests and positive cases is vital for hospitals to provide adequate patient care at the right time. It also helps prevent shortages or overages of staffs and supplies. In this paper we present a practical implementation of a Bayesian Kalman filter to forecast weekly flu test and positive cases in a hospital environment. By integrating real time hospital data, this framework offers a robust methodology for accurately predicting flu volume one to four weeks out with a reasonable accuracy.

Committee: Grzegorz Rempala (Advisor); Eben Kenah (Committee Member); Fernanda Schumacher (Committee Member) Subjects: Biostatistics; Health Care Management; Mathematics; Medicine; Statistics