A Social Welfare Maximization Matching Framework for Supplemental Nurse Staffing

The adequacy of hospital nurse staffing in Canada is essential for the delivery of quality health care to Canadians. In light of permanent nursing staff shortage in most of Canadian hospitals, using of supplemental nurses to bolster permanent nursing staff is widespread. Having suitably qualified staff on duty at the right time is a large determinant of service organization efficiency in providing continuity of care. On the other hand, attractive schedules are an important factor leading to successful recruiting and retaining valuable nursing personnel. Computing mutually beneficial staffing schedules in a dynamic supplemental nurse staffing environment at larger scale is a big challenge facing healthcare staffing agencies. In this project, we will develop machine learning algorithms, optimization models, dynamic scheduling structures to tackle the challenge. These proposed methods will be implemented in software modules and integrated in a software platform which can be hosted in Medialpha’s cloud computing environment. By using the proposed supplemental nurse staffing system, Medialpha will attract more nurses and hospitals to their platform. In addition, the integrated system will streamline Medialpha’s business process and lower its operational costs.

Faculty Supervisor:

Chun Wang

Student:

Xinkai Xu;Jie Gao;Andy Ta

Partner:

Medialpha Laboratories Inc

Discipline:

Engineering

Sector:

Health care and social assistance

University:

Concordia University