You are here

SBIR Phase I: Nursing Workforce Optimization Algorithm and Software

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2052208
Agency Tracking Number: 2052208
Amount: $255,997.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DH
Solicitation Number: N/A
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-05-15
Award End Date (Contract End Date): 2022-04-30
Small Business Information
1230 S 47TH STREET
United States
DUNS: 105354512
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Colin Plover
 (845) 242-2861
Business Contact
 Colin Plover
Phone: (845) 242-2861
Research Institution

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project improves nursing operations in hospitals for better patient outcomes. This will be achieved through the analysis of a health system’s data regarding nurse staffing, scheduling, and nurse-patient matching. This research will analyze data on nurses, patients, and inpatient clinical environments and their relationship to outcomes to develop unique algorithms, software, and datasets in care facilities. This is significant because the approach to nursing workforce management decisions influences care outcomes and the cost of delivery of quality care. This Small Business Innovation Research (SBIR) Phase I project involves advanced research techniques that aim to optimize nurse staffing, scheduling, and nurse-patient matching. Relationships will be examined between 1. independent variables associated with nursing operations and 2. dependent variables that include patient safety indicator variables developed by the Agency for Healthcare Quality and Research. The exploration of these relationships will help answer questions including 1) how many nurses to employ and deploy day-to-day (i.e. staffing), 2) how many and in what complement to deploy nurses on shifts (i.e. scheduling), and 3) how to match nurses to patients on each unit each shift (i.e. nurse-patient assignments) to optimize outcomes. The proposed optimization process enables a data- driven approach to address staffing, scheduling, and nurse-patient matching challenges. The methods involve multivariate regression analyses and machine learning techniques including autoregressive integrated moving average (ARIMA). The goals of this research involve the development of algorithms and software that empower hospital administrators with the insight and technology to improve nursing care and patient outcomes. 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.

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government