Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes

An increasing incidence of cancer has led to high patient volumes and time challenges in ambulatory oncology clinics. By knowing how many patients are experiencing complex care needs in advance, clinic scheduling and staff allocation adjustments could be made to provide patients with longer or shorter timeslots to address symptom complexity. In this study, we used predictive analytics to forecast the percentage of patients with high symptom complexity in one clinic population in a given time period. Autoregressive integrated moving average (ARIMA) modelling was utilized with patient-reported outcome (PRO) data and patient demographic information collected over 24 weeks. Eight additional weeks of symptom complexity data were collected and compared to assess the accuracy of the forecasting model. The predicted symptom complexity levels were compared with observation data and a mean absolute predicting error of 5.9% was determined, indicating the model’s satisfactory accuracy for forecasting symptom complexity levels among patients in this clinic population. By using a larger sample and additional predictors, this model could be applied to other clinics to allow for tailored scheduling and staff allocation based on symptom complexity forecasting and inform system level models of care to improve outcomes and provide higher quality patient care.

Cost Optimization and Risk Minimization During Teamwork Organization

In the paper the problem of personnel allocation under threat was presented. The possibilities of undertaking optimization measures in the process of workers’ health and safety and expenses incurred were emphasized. A mathematical model for this issue has been formulated. An algorithm solving the problem of staff allocation was presented. The evaluation criterion for this assignment was the minimization of worker safety risks. Simultaneous optimization of expenses incurred in the implementation of production tasks was taken into account. The productivity of the staff and all existing jobs with the skills of the employees also was considered. This problem was solved using GNU Octave. The example presented in the paper shows that in case of the most unfavorable allocation of tasks to employees, it will lead to a significant reduction in profits and may increase the risk of undesirable situations. The proposed analysis is the starting point for determining the risk in case of multi-position work.

Interactive Planning of Competency-Driven University Teaching Staff Allocation

This paper focuses on a teacher allocation problem that is specifically concerned with assigning available academic lecturers to remaining courses from a given student curriculum. The teachers are linked to tasks according to competencies, competence requirements enforced by the curriculum as well as the number and type of disruptions that hamper the fulfilment of courses. The problem under consideration boils down to searching links between competencies possessed by teachers and competencies required by the curricula that will, firstly, balance student needs and teacher workload and, secondly, ensure an assumed robustness level of the teaching schedule. The implemented interactive method performs iterative solving of analysis and synthesis problems concerned with alternative evaluation/robustness of the competency framework. Its performance is evaluated against a set of real historical data and arbitrarily selected sets of disruptions. The computational results indicate that our method yields better solutions compared to the manual allocation by the university.

A Framework for Sharing Staff between Outbound and Inbound Airport Processes

This paper proposes an advanced simulation-optimization approach to evaluate and optimize the passenger flows within international airports. This approach allocates resources intelligently during the simulation process and balances demand and service quality. The resource allocation performed by our Advanced Resource Management (ARM) algorithm was used to develop an integrated system for arranging resources, identifying the proper resources, and allocating them throughout the model. It was used to investigate the influences of different staff allocation techniques on the inbound and outbound processes of an airport terminal. The purpose of the proposed simulation-optimization approach is to enhance passenger satisfaction through ensuring reasonable wait times during processing at the lowest cost possible (minimal staff hours).

‘Your country needs you’: the ethics of allocating staff to high-risk clinical roles in the management of patients with COVID-19

As the COVID-19 pandemic impacts on health service delivery, health providers are modifying care pathways and staffing models in ways that require health professionals to be reallocated to work in critical care settings. Many of the roles that staff are being allocated to in the intensive care unit and emergency department pose additional risks to themselves, and new policies for staff reallocation are causing distress and uncertainty to the professionals concerned. In this paper, we analyse a range of ethical issues associated with changes to staff allocation processes in the face of COVID-19. In line with a dominant view in the medical ethics literature, we claim, first, that no individual health professional has a specific, positive obligation to treat a patient when doing so places that professional at risk of harm, and so there is a clear ethical tension in any reallocation process in this context. Next, we argue that the changing asymmetries of health needs in hospitals means that careful consideration needs to be given to a stepwise process for deallocating staff from their usual duties. We conclude by considering how a justifiable process of reallocating professionals to high-risk clinical roles should be configured once those who are ‘fit for reallocation’ have been identified. We claim that this process needs to attend to three questions that we consider in detail: (1) how the choice to make reallocation decisions is made, (2) what justifiable models for reallocation might look like and (3) what is owed to those who are reallocated.