Staff Rostering Research Articles

Strategic Optimization of Patient Flow and Staffing Schemes during the COVID-19 Pandemic through Operations Management in a Neonatal Intensive Care Unit.

The COVID-19 pandemic posed challenges in making time-bound hospital management decisions. The University of the Philippines -Philippine General Hospital (UP-PGH) is a tertiary COVID-19 referral center located in Manila, Philippines. The mismatch of increasing suspected or confirmed COVID-19 infected mothers with few documented cases of infected infants has caused significant patient overflow and manpower shortage in its NICU. We present an evaluated scheme for NICU bed reallocation to maximize capacity performance, staff rostering, and resource conservation, while preserving COVID-19 infection prevention and control measures. Existing process workflows translated into operational models helped create a solution that modified cohorting and testing schemes. Staffing models were transitioned to meet patient flow. Outcome measurements were obtained, and feedback was monitored during the implementation phase. The scheme evaluation demonstrated benefits in (a) achieving shorter COVID-19 subunit length of stay; (b) better occupancy rates with minimal overflows; (c) workforce shortage mitigation with increased non-COVID workforce pool; (d) reduced personal protective equipment requirements; and (e) zero true SARS-CoV-2 infections. Designed for hospital operations leaders and stakeholders, this operations process can aid in hospital policy formulation in modifying cohorting schemes to maintain quality NICU care and service during the COVID-19 pandemic.

The impact of electronic and self-rostering systems on healthcare organisations and healthcare workers: A mixed-method systematic review.

To synthesise evidence from studies that explored the impact of electronic and self-rostering systems to schedule staff on healthcare organisations and healthcare workers. Mixed-method systematic review. Studies were screened by two independent reviewers and data were extracted using standardised data extraction tables. The quality of studies was assessed, and parallel-results convergent synthesis was conducted. Academic Search Complete, CINAHL, ERIC, MEDLINE, PsycINFO and PsycARTICLES were searched on January 3, 2023. Eighteen studies were included (10 quantitative descriptive studies, seven non-randomised studies and one qualitative study). Studies examined two rostering interventions including self-rostering (n = 12) and electronic rostering (n = 6). It was found that the implementation of electronic and self-rostering systems for staff scheduling impacted positively on both, healthcare workers and healthcare organisations. Benefits included enhanced roster efficiency, staff satisfaction, greater control and empowerment, improved work-life balance, higher staff retention and reduced turnover, decreased absence rates and enhanced healthcare efficiency. However, self-rostering was found to be less equitable than fixed rostering, was associated with increased overtime, and correlated with a higher frequency of staff requests for shift changes. The impact of electronic and self-rostering systems to schedule staff on healthcare organisations and healthcare workers' outcomes was predominantly positive. Further randomised controlled trials and longitudinal studies are warranted to evaluate the long-term impact of various rostering systems, including electronic and self-rostering systems. Rostering is a multifaceted responsibility for healthcare administrators, impacting patient care quality, workforce planning and healthcare expenditure. Given that healthcare staffing costs constitute a substantial portion of global healthcare expenditure, efficient and strategic resource management, inclusive of healthcare staff rostering, is imperative. The 27-item Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. No Patient or Public Contribution.

Assessing the Effects of the COVID-19 Pandemic on the Aviation Sector and Exploring Strategies for Industry Recovery

One of the most impacted industries among all, is the Airlines industry. Airlines need to study the consequences of a pandemic, what is important is that they adapt to the uncertainty and complex changes. Companies must understand how to manage their capabilities, strategy, mission, and vision, with a fast and changing environment. Each airline can get the benefit of having a low-cost strategy and use the cargo shipment strategy that makes it easy to ship different products. Basically, reaching efficiency and effectiveness to lead the uncertain changes. For the research report it considers the highly affected industry in covid era. Qatar airways operated in a high premium business model but in covid times, demand of the luxury segment got affected. Indigo does their business mainly in India, so they are the worst hit in these times since India shows the second highest number of covid cases in the world. Due to the resurgence of Covid-19, Cathay Pacific have struggled with staff rostering due to Hong Kong’s strict quarantine rules.

Integrated task scheduling and personnel rostering of airports ground staff: A case study

In dealing with personnel management in companies, two fundamental planning issues have to be handled: staff rostering and activities assignment. These two problems have historically been treated separately, in a sequential way; however, it is evident how strongly they are tied to each other and that solution quality inevitably drops if this connection is not taken into proper account. For this reason, also taking advantage of the massive recent advances in software and hardware technologies, the integrated task scheduling and personnel rostering problem (TSPR) has been formalized along with suitable algorithmic approaches to tackle both planning stages altogether. In this paper, we describe how the peculiar and complex case of airport ground staff was handled in a scenario defined by real-world data from a large airport in Italy. Specifically, we show that the problem can be cast into a mixed integer linear programming model. We then show that, to make the problem computationally tractable, the introduction within the model of a set of suitable valid inequalities is crucial. Indeed, as opposed to the base model from the literature, the novel, improved formulation allowed to effectively obtain near-optimal solutions in reasonable time even for the considered large-scale and real-world scenario.

Rostering of air traffic controllers – current practices in Europe, key performance trade-offs and future opportunities

The process of staff planning and rostering is one of the key factors affecting the performance of an Air Navigation Service Provider in terms of capacity and cost-efficiency delivered to airspace users. This paper gives a summary of previous works and current rostering practices applied at Area Control Centres (ACCs) in Europe. Furthermore, it identifies key trade-offs between rostering flexibility, capacity and productivity by establishing a relationship between ACC demand, rostering flexibility in place and the number of Air Traffic Control Officers (ATCOs) needed, as well as by exploring how varying rostering practices applied in ACCs affect spare capacity in the system. To that end, an initial mathematical model for optimizing ATCO staffing requirements has been proposed and tested on a case study involving three representative ACCs, reflecting different hourly variability in demand. The results suggest that - for different combinations of shift patterns and capacity profiles - substantial variation exists in both the minimum number of ATCOs and the associated spare ACC capacity. It has also been shown that a certain amount of spare capacity is inevitably present in the system due to inability to perfectly match ATCO hours with demand. Different options and enablers for exploiting such capacity are also discussed in the paper.

A shift-based model to solve the integrated staff rostering and task assignment problem with real-world requirements

Due to the computational complexity, the staff scheduling problem is generally decomposed into two subproblems, i.e. staff rostering and task assignment problems. Very often, this could result in suboptimal solutions. Thus, we study an integrated staff rostering and task assignment (ISRTA) problem in this paper. Previous studies formulate this problem as task- and roster-based ISRTA models, which can only solve small-scale problems. To overcome this limitation, this paper proposes a novel shift-based ISRTA model, which exhibits less symmetry and greatly reduces the number of variables and constraints. We extend the shift-based ISRTA model to six real-world requirements, i.e., shift flexibility, qualification, travel time, contractual rules, task connection preference, and fairness, which ensure the applicability of the result schedule. To efficiently solve the shift-based ISRTA model, we propose a clique-based aggregated model that enhances the performance of the basic shift model. Further, two heuristics, i.e., rolling horizon algorithm and iterative shift selection algorithm, are proposed to speed up the solution process. Based on randomly generated 24 benchmark instances and 16 real-world instances from a major airline, computational results show that the shift-based ISRTA model outperforms the task- and roster-based ISRTA models. For small- and mid-scale problems, the shift-based ISRTA model can be solved optimally or near-optimally directly using a commercial solver, and the rolling horizon algorithm and iterative shift selection algorithm can solve large-scale problems and obtain high-quality solutions.

A bi-objective home care routing and scheduling problem considering patient preference and soft temporal dependency constraints

A Home Health Care Routing and Scheduling Problem (HHCRSP) is one of the most practical branches in Home Health Care (HHC) optimization. The main focus of this study is to investigate an HHCRSP with both soft and Hard Time Windows (HTWs) associated with caregivers and patients, respectively. Furthermore, five different types of soft temporal dependency constraints are considered, which specify different relations amongst the starting time of dependent visits. Accordingly, a bi-objective Mixed-Integer Programming (MIP) model is devised to incorporate staff rostering, vehicle routing, and scheduling simultaneously. This model aims at minimizing the system’s total cost while maximizing the total satisfaction of patient preference. Since the problem is NP-hard, Iterated Local Search (ILS) is applied to solve large-sized problems in high frequencies and within reasonable computational time. Computational results on some real-world-inspired benchmark instances highlight the overall efficiency of the employed algorithm compared to the Non-dominated Sorting Genetic Algorithm (NSGA-II).

Practical recommendations for staff rostering justified by real-world optimization

Staff rostering is a difficult and time-consuming problem that every company or institution that has employees working on shifts or on irregular working days must solve. The main goal is the performance of staff on financial efficiency. Other important goals include fairer workloads and employee satisfaction. Furthermore, the staff rostering optimization should address the health, safety, and well-being of the employees. The Finnish Institute of Occupational Health, which operates under the Ministry of Social Affairs and Health, published their latest recommendations for shift work in 2019. The recommended values for more than ten individual factors are well justified. However, problems arise when all these recommendations should be satisfied together in real-world staff rostering. We show the gap between the ideal recommendations, theoretical expectations and the everyday reality. We publish our five most important practical recommendations for shift work that researchers should consider when they implement staff rostering algorithms. The PEASTP metaheuristic is used for optimization test runs.

Nurse staffing and inpatient mortality in the English National Health Service: a retrospective longitudinal study

ObjectiveTo examine the impact of nursing team size and composition on inpatient hospital mortality.DesignA retrospective longitudinal study using linked nursing staff rostering and patient data. Multilevel conditional logistic regression models...

Adoption Factors of Artificial intelligence in Human Resources Management

The phenomenon of artificial intelligence has been widely studied in several areas. In opposite, in terms of AI in HRM, the literature shows limited research on the adoption factors of artificial intelligence (AI) in HRM. AI has been enrolled in several HRM’s areas starting from staffing till management performance or compensation. A set of suggestions on how to adopt AI in HRM has been raised. This piece of research aims to identify the adoption factors of six scenarios of AI in HRM. These scenarios are turnover prediction with artificial neural networks, candidate search with knowledge-based search engines, staff rostering with genetic algorithms, HR sentiment analysis with text mining, résumé data acquisition with information extraction and employee self-service with interactive voice response. As a result, compatibility, relative advantage, complexity, managerial support, government involvement, and vendor partnership are determinants affected factors of AI adoption in HRM. This paper tries to address new insights for practitioners and academics by minimizing the risks associated with AI adoption in some areas of HRM through exploring determinant factors of adoption.

Estimating hourly work schedule risk in railway traffic controllers

Around-the-clock staffing in safety-critical environments calls for an accurate risk assessment of work schedules. In this paper we introduce and evaluate an hourly version of the “Risk Index”, an established risk model in the UK rail industry. This “Hourly Risk Index” is the first risk-based model that allows the prediction of risk at the hourly level, and is founded on the most recent scientific insights in fatigue-related accident risk. It considers the build-up of risk over consecutive shifts, recovery during days off, quick returns, and time-of-day and time-on-duty effects. Improved understanding of hourly risk is highly relevant for both ex-ante (staff rostering) and ex-post (accident and incident analysis) evaluations. We evaluated the performance of the Hourly Risk Index by analyzing a purpose-built real-world dataset from Belgium’s railway traffic control centers (containing close to 8 million round-the-clock working hours in the period 2013–2018). Results not only show that the occurrence of safety-critical errors is significantly associated with higher Hourly Risk Index values, but also that error severity is significantly related to a further increase in risk. In addition, the Hourly Risk Index outperformed the original Risk Index. Our empirical results also emphasize the importance of job demands, by indicating a higher performance of the Hourly Risk Index under conditions of low or high workload. Given these outcomes, it is currently being deployed and tested at Belgian national railways. This should allow the further refinement of the Hourly Risk Index in an iterative manner.

Comparison of home ambulatory type 2 polysomnography with a portable monitoring device and in-laboratory type 1 polysomnography for the diagnosis of obstructive sleep apnea in children.

To compare type 2 polysomnography (T2PSG) to the gold standard type 1 in-laboratory polysomnography (T1PSG) for diagnosing obstructive sleep apnea (OSA) in children; validate home T2PSG in children with suspected OSA. Eighty-one participants (ages 6-18) with suspected OSA had simultaneous T1PSG and T2PSG in the sleep laboratory, 47 participants (ages 5-16) had T1PSG in the sleep laboratory and T2PSG performed at home. Sleep scientists staged and scored polysomnography data, and pediatric sleep physicians assigned a diagnosis of normal or OSA. Participant demographics, polysomnography variables, and diagnoses were compared using chi-square and Fisher's exact tests for nominal variables, t test for continuous variables and Cohen's kappa to assess concordance. Acceptable recordings were obtained for every home T2PSG. When T1PSG and T2PSG were simultaneous, correlation between the number of arousals, respiratory disturbance index, and sleep stages was excellent. T2PSG at home demonstrated less stage 2 sleep, more rapid eye movement sleep, and higher sleep efficiency. Comparison of home T2PSG to T1PSG for diagnosing OSA showed a false-positive rate of 6.6% and false-negative rate of 3% for those performed at home. T2PSG in the home is feasible with excellent concordance with T1PSG for the purposes of diagnosing OSA in children aged 5-18 years. Home T2PSG may be more representative of a "normal" night for children and could benefit those suspected of having OSA by reducing waiting times for laboratory PSG, improving access to PSG and possibly reducing costs of investigating and treating OSA. Withers A, Maul J, Rosenheim E, O'Donnell A, Wilson A, Stick S. Comparison of home ambulatory type 2 polysomnography with a portable monitoring device and in-laboratory type 1 polysomnography for the diagnosis of obstructive sleep apnea in children. J Clin Sleep Med. 2022;18(2):393-402.

Quantifying and enforcing robustness in staff rostering

Employee absences are inevitable in practice due to illness, heavy workloads or accidents. These unforeseen events result in the disruption of employee shift rosters, which are then typically repaired using re-rostering methods. Despite their widespread use, repair methods often require last-minute changes to rosters, negatively affecting employees’ personal lives. Robust rosters are thus crucial when it comes to minimizing the negative impact of these unforeseen events. The academic literature, however, currently lacks a metric capable of measuring the robustness of staff rosters. This study introduces two complementary approaches for quantifying roster robustness without the use of simulation. The first metric enables an a priori estimation of how well unexpected employee absences can be accommodated, while the second metric approximates the costs incurred by repair methods. An integer programming-based methodology is proposed for generating robust rosters in a controlled manner. A computational study using public instances based on real-world scenarios demonstrates the effectiveness of the proposed methodology. A significant reduction of costs is observed when enforcing an appropriate level of robustness, compared against when robustness is ignored.

Effects of the COVID-19 pandemic on provision of electroconvulsive therapy.

COVID-19 has had a heavy impact on healthcare provision worldwide, including delivery of electroconvulsive therapy (ECT). A survey was completed in the UK and Republic of Ireland in April and July 2020 by 95 and 89 ECT clinics respectively. In April 2020, 53% of the clinics provided only emergency treatment and 24% had closed. Reasons included unavailability of anaesthetists, infection control measures and staff sickness. Restrictions persisted in July, with disruption to an estimated 437 individuals' treatment and poor outcomes, including clinical deterioration and readmission. Risk stratification, longer clinic sessions, improvements in ventilation, regular virus testing, pragmatic staff rostering and availability of personal protective equipment will protect against service disruption in subsequent waves of the pandemic.

Survey of laboratory medicine's national response to the Covid-19 pandemic in the Republic of Ireland.

Background The global SARS-CoV-2 pandemic placed Irish Laboratory Medicine services under sustained and massive strain. Rapid reconfiguration was required to introduce new assays at high capacity for diagnosis and monitoring of COVID-19, while maintaining existing services.AimThe aim of this national survey was to capture Laboratory Medicine’s response across the Republic of Ireland during the first wave of the COVID-19 pandemic.MethodsAn electronic survey developed using Microsoft Forms® was emailed on 5 October 2020 to 53 local representatives of the PeriAnalytic and Laboratory Medicine Society (PALMSoc), reaching 38 separate pathology departments in the country.ResultsA total of 45 responses from 38 laboratories were received (72% response rate) representing a range of departments and disciplines. Most laboratories (63%) introduced new tests, and in a time frame of less than 6 weeks (80%). Point-of-care testing (POCT) played a significant role in the response to COVID-19, with almost half of respondents (47%) reporting that additional equipment was introduced. Maintenance of the Quality Management System (QMS) proved challenging, with 60% of respondents indicating that not all aspects were sustained. When asked about changes to staff rostering, 98% of respondents reported that changes were made. All adjustments were made despite staffing challenges; only 18% of respondents described the staffing levels in their department as 100% prior to the onset of the first wave.ConclusionsThis study confirms an agile and resilient response to the COVID-19 pandemic from Ireland’s Laboratory Medicine services despite many economic and staffing challenges.

A Successful Three-Phase Metaheuristic for the Shift Minimization Personal Task Scheduling Problem

Workforce scheduling process consists of three major phases: workload prediction, shift generation, and staff rostering. Shift generation is the process of transforming the determined workload into shifts as accurately as possible. The Shift Minimization Personnel Task Scheduling Problem (SMPTSP) is a problem in which a set of tasks with fixed start and finish times must be allocated to a heterogeneous workforce. We show that the presented three-phase metaheuristic can successfully solve the most challenging SMPTSP benchmark instances. The metaheuristic was able to solve 44 of the 47 instances to optimality. The metaheuristic produced the best overall results compared to the previously published methods. The results were generated as a by-product when solving a more complicated General Task-based Shift Generation Problem. The metaheuristic generated comparable results to the methods using commercial MILP solvers as part of the solution process. The presented method is suitable for application in large real-world scenarios. Application areas include cleaning, home care, guarding, manufacturing, and delivery of goods.

Binary Programming Model for Rostering Ambulance Crew-Relevance for the Management and Business

The nature of health care services is very complex and specific, thus delays and organizational imperfections can cause serious and irreversible consequences, especially when dealing with emergency medical services. Therefore, constant improvements in various aspects of managing and organizing provision of emergency medical services are vital and unavoidable. The main goal of this paper is the development and application of a binary programming model to support decision making process, especially addressing scheduling workforce in organizations with stochastic demand. The necessary staffing levels and human resources allocation in health care organizations are often defined ad hoc, without empirical analysis and synchronization with the demand for emergency medical services. Thus, irrational allocation of resources can result in various negative impacts on the financial result, quality of medical services and satisfaction of both patients and employees. We start from the desired staffing levels determined in advance and try to find the optimal scheduling plan that satisfies all significant professional and regulatory constraints. In this paper a binary programming model has been developed and implemented in order to minimize costs, presented as the sum of required number of ambulance crews. The results were implemented for staff rostering process in the Ambulance Service Station in Subotica, Serbia. Compared to earlier scheduling done ad hoc at the station, the solution of the formulated model provides a better and equable engagement of crews. The developed model can be easily modified and applied to other organizations with the same, stochastic, nature of the demand.

"How about We Give It a Go?": A Case Study on Supporting Breastfeeding Families in an Australian Child Care Centre.

The World Health Organization (WHO) recommends exclusive breastfeeding for six months and continuation of breastfeeding for up to two years. Formal child care has an important role in supporting breastfeeding, as many Australian infants commence care before two years of age. Currently, little is known about support or barriers in child care contexts. The present qualitative instrumental case study explores practices which protect, promote and support breastfeeding at a child care centre located in the Australian Capital Territory’s outer suburbs. Extending from a previously published collective case study, a cultural-institutional focus of analysis was used to explore the roles of proximity, flexibility and communication in supporting breastfeeding within a child care centre located close to an infant’s home. Interviews with centre staff and mothers, triangulated with observations of the centre environment and policy documents provide insight into the environment. Affirming the roles of flexibility in routine and staff rostering and two-way communication, findings suggest longer-term benefits may be derived from selecting a child care centre close to an infant’s home, provided mothers can overcome barriers to breastmilk expression in the workplace. The study recognises the role of non-lactating caregivers in the transition to formal child care, and of the support culture for educators who breastfeed. This study extends the knowledge base of breastfeeding support interventions in the child care setting to inform future research and policy.

A Hybrid Personnel Scheduling Model for Staff Rostering Problems

The problem of staff scheduling in the airline industry is extensively investigated in operational research studies because efficient staff employment can drastically reduce the operational costs of airline companies. Considering the flight schedule of an airline company, staff scheduling is the process of assigning all necessary staff members in such a way that the airline can operate all its flights and construct a roster line for each employee while minimizing the corresponding overall costs for the personnel. This research uses a rostering case study of the ground staff in the aviation industry as an example to illustrate the application of integrating monthly and daily schedules. The ground staff in the aviation industry case is a rostering problem that includes three different types of personnel scheduling results: fluctuation-centered, mobility-centered, and project-centered planning. This paper presents an integrated mixed integer programming (MIP) model for determining the manpower requirements and related personnel shift designs for the ground staff at the airline to minimize manpower costs. The aim of this study is to complete the planning of the monthly and daily schedules simultaneously. A case study based on real-life data shows that this model is useful for the manpower planning of ground services at the airline and that the integrated approach is superior to the traditional two-stage approach.

Staff rostering, split team arrangement, social distancing (physical distancing) and use of personal protective equipment to minimize risk of workplace transmission during the COVID-19 pandemic: A simulation study

BackgroundThe recent global survey promoted by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Taskforce on COVID-19 (coronavirus disease 2019) described staff rostering and organization as significant operational challenges during the COVID-19 pandemic. MethodA discrete event simulation was used to explore the impact of different permutations of staff roster, including the number of shifts per day, the number of staff on duty per shift, overall number of staff accessible to work in the laboratory (i.e. overall staff pool), the frequency of shift changes (i.e. number of consecutive days worked), fixed work-rest days and split team arrangement on workplace transmission of COVID-19 by a simulated index staff who acquired the infection from the community over 21 days. Additionally, the impact of workplace social distancing (physical distancing) and use of personal protective equipment (PPE) were investigated. ResultsA higher rate of transmission was associated with smaller overall staff pool (expressed as multiples of the number of staff per shift), higher number of shifts per day, higher number of staff per shift, and longer consecutive days worked. Having fixed work-rest arrangement did not significantly reduce the transmission rate unless the workplace outbreak was prolonged. Social distancing and PPE use significantly reduced the transmission rate. ConclusionLaboratories should consider organizing the staff into smaller teams/shift and reduce the number of consecutive days worked. Additionally, our observation aligns with the IFCC biosafety recommendation of monitoring staff health (to detect early infection), split team arrangement, workplace social distancing and use of PPE.