An Integrated Two-Level Integer Linear Program (ILP) Model for Elective Surgery Scheduling: A Case Study in an Italian Hospital

The urban population is increasing worldwide. This demographic shift generates great pressure over public services, especially those related to health-care. One of the most expensive health-care services is surgery, and in order to contain this growing cost of providing better services, the efficiency of surgical centers must be improved. This work proposes an integer linear programming model (ILP) considering the case-mix planning (CMP) and the master surgical scheduling (MSS) problems. The case-mix planning problem deals with the planning of the number of operating rooms to be assigned to surgical specialties. The master surgical scheduling is related to when to assign the rooms to the different specialties. The developed model uses a data set from a hospital of the city of Turin, Italy. The results are very promising, showing a reduction from 240 weeks to 144 weeks to empty the surgical waiting list (WL). Moreover, if changes to the hospital situation are implemented, including the introduction of two new surgical teams into one of the hospital’s specialties, the time to empty the surgical WL could decrease to 79 weeks.

Classification of the social distance during the COVID-19 pandemic from electricity consumption using artificial intelligence.

Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium-sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4-months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID-19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID-19 pandemic.