PurposeAnalytical decision-support methods hold significant promise for enhancing planning and control in complex service organizations. Yet, many of these technically sophisticated solutions struggle to gain traction in practice. We study the planning and scheduling of operating rooms (ORs), central to the management of operations in hospitals. Using research methodology and theoretical lenses in operations management (OM), we study the gap between technical theory on OR scheduling and the complex realities of healthcare practice. Design/methodology/approachWe construct the theoretical perspective from a synthesis of the scheduling literature in Management Science and Operations Research (MS/OR), identifying its core premises. The practical perspective is obtained from an elaborate case study in nine hospitals. We find six key discrepancies between scheduling theory and healthcare practice, which we subsequently interpret through the lenses of the Theory of Swift and Even Flow and related OM theory. FindingsThe basic problem structure assumed in the scheduling literature was validated in practice. However, the complex and ambiguous goal structure, dynamic operating conditions and underdefined constraints complicate the optimization logic and ex ante planning approaches predominant in the scheduling literature. We develop propositions to strengthen alignment between scheduling theory and practice, grounded in OM perspectives on coordination and flow, continuous improvement, satisficing and flexibility in complex service systems. Originality/valueOur study offers an empirically grounded, OM-centered analysis of the conditions and design principles under which algorithmic scheduling approaches may be effectively deployed. In doing so, we connect OM and MS/OR, two related fields studying healthcare operations from distinct angles.

Neonatology care, the care for premature and severely ill babies, is increasingly confronted with capacity challenges. The entire perinatal care chain, including the Neonatal Intensive Care Unit (NICU), operates at high occupation levels. This results in refusals, leading to undesirable transports to other centers or even abroad, which affects quality of care, length of stay, and safety of these babies, and places a heavy burden on patients, their families, and involved caregivers. In this work we assess the improvement potential of network collaboration strategies that focus on reducing the number of patient transports, by allowing flexible deployment of nurses over the existing NICUs to match short-term changes in patient demand. We develop a discrete event simulation with an integrated optimization module for shift allocation and transfer optimization. A case study for the Dutch national NICU network, involving 9 NICU locations and current transport of 15% of all NICU patients in case of no flexible deployment, shows the potential of transporting staff instead of patients: About 70% of patient transports can be eliminated in case of 15-50% capacity sharing, and about 35% of nationwide transports is eliminated with up to 15% capacity sharing in the Dutch's main conurbation area only.

Predictions of bed census are crucial for hospital capacity management choices, encompassing ward sizing, staffing, patient bed assignments, and surgical scheduling. Presently, these predictions heavily rely on doctors’ estimated Expected Discharge Date (EDD). This paper introduces two probabilistic models that integrate EDD with Length of Stay (LoS) distributions derived from data. By employing the Poisson binomial distribution and probabilistic convolution, we generate full census distributions. Applying our approach to real hospital data demonstrates its ability to provide precise predictions, leading to valuable managerial insights.

Home healthcare capacity is under great pressure due to demographic developments. Existing literature has exclusively focused on the planning, scheduling, and routing of non-acute care activities. However, similar to other healthcare settings, home healthcare also experiences acute care activities that disrupt operational performance. We study the planning and control of an acute care team for dealing with unplanned and urgent home healthcare activities. Particularly, we focus on determining the number of nurses per care level and their standby locations. The primary aim of this study is to introduce this novel problem, which we define as the acute care team location problem. We formulate this problem as a chance-constrained program. We solve the single location problem to optimality, and the multi-location problem with sample average approximation. The results show that our approach enables decision makers to optimally configure their acute care team, to respond quickly to acute care incidents. From a managerial perspective, our research provides a model that supports tactical capacity planning in HHC organisations and presents a benchmark for acute care management policies.

This paper studies the discrete-time Stochastic Knapsack with Periodic Scheduled Arrivals(SKPSA). The goal is to find a schedule such that the capacity usage of the unconstrainedcousin of the knapsack is as close as possible to a target utilization. Weapproximate the SKPSA with aWasserstein distance based Distributionally Robust Optimization(DRO) model, resulting in the DRO-SKPSA.We present an algorithm that efficientlysolves this model, and show that the DRO-SKPSA produces robust schedules.The problem arises in particular in healthcare settings in the development of MasterSurgical Schedules (MSSs). We discuss managerial insights for MSSs with downstreamcapacity constraints.

Volunteer responder systems (VRS) alert and guide nearby lay rescuers towards the location of an emergency. An application of such a system is to out-of-hospital cardiac arrests, where early cardiopulmonary resuscitation (CPR) and defibrillation with an automated external defibrillator (AED) are crucial for improving survival rates. However, many AEDs remain underutilized due to poor location choices, while other areas lack adequate AED coverage. In this paper, we present a comprehensive data-driven algorithmic approach to optimize deployment of (additional) public-access AEDs to be used in a VRS. Alongside a binary integer programming (BIP) formulation, we consider two heuristic methods, namely Greedy and Greedy Randomized Adaptive Search Procedure (GRASP), to solve the gradual Maximal Covering Location (MCLP) problem with partial coverage for AED deployment. We develop realistic gradually decreasing coverage functions for volunteers going on foot, by bike, or by car. A spatial probability distribution of cardiac arrest is estimated using kernel density estimation to be used as input for the models and to evaluate the solutions. We apply our approach to 29 real-world instances (municipalities) in the Netherlands. We show that GRASP can obtain near-optimal solutions for large problem instances in significantly less time than the exact method. The results indicate that relocating existing AEDs improves the weighted average coverage from 36% to 49% across all municipalities, with relative improvements ranging from 1% to 175%. For most municipalities, strategically placing 5 to 10 additional AEDs can already provide substantial improvements.

This paper gives an overview of five decades of operational research applied to healthcare, structured along nine key application domains: personnel scheduling, blood supply chain management, cancer diagnosis and treatment, emergency medical response and disaster relief, infectious diseases, long-term conditions, diagnostic imaging, public health, and operating room scheduling. Each section summarises the main contributions, developments, recent trends and future research. The review focuses on the European context, which is dominated by public healthcare systems with its specific strengths and challenges.

Waiting time in healthcare is a significant problem that occurs across the world and often has catastrophic effects. There are various terms used for waiting time ("sojourn", "throughput" etc.) and there is no consensus on how these terms are defined. Ambiguous definitions of waiting time make it difficult to compare and measure the problems related to waiting times and delays in healthcare. We present a systematic search and review of the Operations Research and Management Science (ORMS) literature on delays in healthcare services. We search for articles from 2004 to 2019 and base our search strategy on a well-known healthcare planning and control decision taxonomy. An important step towards reducing the ambiguity in the definitions is to distinguish between access time and waiting time. We provide clear definitions and examples of access time and waiting time, and we classify our search results according to three categories: article type, healthcare service investigated and ORMS technique used to solve the delay problem. We find that half of the ORMS research on the waiting and access time problem is done on Ambulatory Care services. We provide tables for each healthcare service that highlight key definitions, the techniques that are used most often and the healthcare environment where the research is done. This research highlights the significant ORMS research that is done on access and waiting time in healthcare as well as the remaining research opportunities. Moreover, it provides a common language for the ORMS community to solve critical waiting time issues in healthcare.

ABSTRACT Waiting time in healthcare is a significant problem that occurs across the world and often has catastrophic effects. There are various terms used for waiting time (“sojourn”, “throughput” etc.) and there is no consensus on how these terms are defined. Ambiguous definitions of waiting time make it difficult to compare and measure the problems related to waiting times and delays in healthcare. We present a systematic search and review of the Operations Research and Management Science (ORMS) literature on delays in healthcare services. We search for articles from 2004 to 2019 and base our search strategy on a well-known healthcare planning and control decision taxonomy. An important step towards reducing the ambiguity in the definitions is to distinguish between access time and waiting time. We provide clear definitions and examples of access time and waiting time, and we classify our search results according to three categories: article type, healthcare service investigated and ORMS technique used to solve the delay problem. We find that half of the ORMS research on the waiting and access time problem is done on Ambulatory Care services. We provide tables for each healthcare service that highlight key definitions, the techniques that are used most often and the healthcare environment where the research is done. This research highlights the significant ORMS research that is done on access and waiting time in healthcare as well as the remaining research opportunities. Moreover, it provides a common language for the ORMS community to solve critical waiting time issues in healthcare.

ABSTRACT Waiting time in healthcare is a significant problem that occurs across the world and often has catastrophic effects. There are various terms used for waiting time (“sojourn”, “throughput” etc.) and there is no consensus on how these terms are defined. Ambiguous definitions of waiting time make it difficult to compare and measure the problems related to waiting times and delays in healthcare. We present a systematic search and review of the Operations Research and Management Science (ORMS) literature on delays in healthcare services. We search for articles from 2004 to 2019 and base our search strategy on a well-known healthcare planning and control decision taxonomy. An important step towards reducing the ambiguity in the definitions is to distinguish between access time and waiting time. We provide clear definitions and examples of access time and waiting time, and we classify our search results according to three categories: article type, healthcare service investigated and ORMS technique used to solve the delay problem. We find that half of the ORMS research on the waiting and access time problem is done on Ambulatory Care services. We provide tables for each healthcare service that highlight key definitions, the techniques that are used most often and the healthcare environment where the research is done. This research highlights the significant ORMS research that is done on access and waiting time in healthcare as well as the remaining research opportunities. Moreover, it provides a common language for the ORMS community to solve critical waiting time issues in healthcare.

Waiting time in healthcare is a significant problem that occurs across the world and often has catastrophic effects. There are various terms used for waiting time ("sojourn", "throughput" etc.) and there is no consensus on how these terms are defined. Ambiguous definitions of waiting time make it difficult to compare and measure the problems related to waiting times and delays in healthcare. We present a systematic search and review of the Operations Research and Management Science (ORMS) literature on delays in healthcare services. We search for articles from 2004 to 2019 and base our search strategy on a well-known healthcare planning and control decision taxonomy. An important step towards reducing the ambiguity in the definitions is to distinguish between access time and waiting time. We provide clear definitions and examples of access time and waiting time, and we classify our search results according to three categories: article type, healthcare service investigated and ORMS technique used to solve the delay problem. We find that half of the ORMS research on the waiting and access time problem is done on Ambulatory Care services. We provide tables for each healthcare service that highlight key definitions, the techniques that are used most often and the healthcare environment where the research is done. This research highlights the significant ORMS research that is done on access and waiting time in healthcare as well as the remaining research opportunities. Moreover, it provides a common language for the ORMS community to solve critical waiting time issues in healthcare.

Background: Frailty has been shown to be associated with poor chance of survival after an in-hospital cardiac arrest (IHCA).However, no studies have assessed functional outcomes among elderly survivors which might help guide patients and doctors considering decisions about life-sustaining treatment and when planning post-resuscitation care.The aim was to assess association between clinical frailty scale, (CFS) and cerebral performance category (CPC) and health related quality of life (HRQoL) after an IHCA.Methods: Patients at least 65 years old reported to have a cardiac arrest at Karolinska University Hospital and registered in the national Swedish Registry for Cardiopulmonary Resuscitation (SRCR) between 2013-2021 were included.A telephone interview was performed based on a questionnaire sent 6 (±3) months post cardiac arrest, including measures of HRQoL, i.e. the EQ-5D-5L and the Hospital Anxiety and Depression Scale (HADS).All survivors were retrospectively assessed according to CFS based on review of the clinical records.The assessment was done without knowledge of the outcomes[T2].CFS was categorized into non-frail (1-4 points) and frail (5-7 points), difference in CPC and HRQoL was assessed with descriptive statistics and p-values.Results: We identified 817 patients suffering IHCA aged at least 65 years, 233 (29%) surviving to 30-days and out of them 26% (n = 60) were considered frail.No difference in change from CPC at admittance to CPC at discharge was found between non-frail and frail patients (84% and 98%, respectively).The median score on EQ-VAS was better among non-frail compared to frail patients (70 points and 50 points, respectively).Likewise, a higher portion of frail patients reported mild-severe symptoms of depression than non-frail (50% and 16%, respectively, p-value 0.01) Conclusion: Frail patients suffering an IHCA survive with largely the same neurological function as they were admitted to hospital with.However, they report more symptoms from depression than non-frail patients.

Patient no-shows and cancellations are a significant problem to healthcare clinics, as they compromise a clinic's efficiency. Therefore, it is important to account for both no-shows and cancellations into the design of appointment systems. To provide additional empirical evidence on no-show and cancellation behaviour, we assess outpatient clinic data from two healthcare providers in the USA and EU: no-show and cancellation rates increase with the scheduling interval, which is the number of days from the appointment creation to the date the appointment is scheduled for. We show the temporal cancellation behaviour for multiple scheduling intervals is bimodally distributed. To improve the efficiency of clinics at a tactical level of control, we determine the optimal booking horizon such that the impact of no-shows and cancellations through high scheduling intervals is minimised, against a cost of rejecting patients. Where the majority of the literature only includes a fixed no-show rate, we include both a cancellation rate and a time-dependent no-show rate. We propose an analytical queuing model with balking and reneging, to determine the optimal booking horizon. Simulation experiments show that the assumptions of this model are viable. Computational results demonstrate general applicability of our model by case studies of two hospitals.

Hospital operating theaters often face the problem of unscheduled emergency arrivals that should be treated as soon as possible. In practice different policies are used to allocate these emergency patients to the operating rooms. These policies are (1) keeping operating rooms empty and available for emergency arrivals; (2) treating emergency patients in elective operating rooms, postponing elective patients; and (3) a mix of these two policies. The use of a specific policy affects performance (e.g., utilization, waiting times, overtime). Currently, these effects are not clear, and there is no agreement on what works ‘best’ for a specific hospital. Using discrete-event simulation, we evaluate the policies for many case characteristics such as hospital size, patient case mix, and fraction of (emergency) patients. We gathered the simulation results in a tool called OR analyzer. This tool is made available online and allows healthcare practitioners to gain insight into the effects of the scheduling policies in settings similar to their specific hospital setting. In addition, this tool allows others researching emergency scheduling policies to frame their hospital settings and compare results.

This book, Handbook of Healthcare Logistics, presents solutions that have been successfully implemented at a variety of healthcare facilities.

An admission lounge is an emerging type of hospital facility that potentially improves the efficiency and efficacy of the perioperative process. We propose a five-step approach for the design, suitability assessment, and optimisation of an admission lounge. The approach uses a case mix optimisation method to select patients for the admission lounge, clinical ward, or for both. Also, it determines the required admission lounge and clinical ward capacities using an Erlang loss model combined with a novel analytical model. The approach is integrated into a decision support system, which helps hospitals to identify the suitability of the admission lounge concept, optimise its configuration, and identify the potential bed reduction in the clinical ward. The decision support system is validated and tested in a case study of a Dutch hospital using their historical data. (C) 2020 The Author(s). Published by Elsevier Ltd.

Dit sectorbeeld van de ontwerpende ingenieurswetenschappen beschrijft de grote gemeenschappelijke deler van de verschillende ontwerpdisciplines in Nederland. In aanloop naar het schrijven van dit sectorbeeld hebben we gezamenlijk bepaald waar onze sterkte ligt, en waar we concreet kunnen bijdragen aan het oplossen van maatschappelijke knelpunten. Implementatie van technologische innovaties in aansluiting op maatschappelijke uitdagingen omvat een ontwerpopgave. Dit vereist in toenemende mate wetenschappelijk onderbouwde ontwerpmethodieken. Het brede Nederlandse ontwerplandschap kan hierbij de rol van verbinder goed vervullen. Teneinde deze brugfunctie optimaal te versterken worden drie gebieden voor verdere investeringen gezien: Onderzoek Er is meer onderzoek en onderzoeksfinanciering nodig voor het volbrengen van ontwerpuitdagingen die in de Nederlandse maatschappelijke missies worden gesteld, evenals voor de verdere ontwikkeling van Key Enabling Methodologies als basis voor effectief ontwerp. Onderwijscapaciteit Er is een ruimere onderwijscapaciteit en verdere ontwikkeling van ontwerp gestuurde didactiek nodig om te kunnen voldoen aan de groeiende vraag naar ontwerpers, een vraag die voortkomt uit de opkomende behoefte aan ontwerpaanpakken in nieuwe onderzoeksprogramma’s binnen Horizon Europe en NWO. Toegang tot technologie Er moet voortdurend toegang gegarandeerd zijn tot de zich snel ontwikkelende technologische disciplines voor professionals die zowel de technologie doorgronden als de onderzoekende ontwerpuitdaging aankunnen. Dit sectorbeeld van de ontwerpende ingenieurswetenschappen beschrijft de grote gemeenschappelijke deler van de verschillende ontwerpdisciplines in Nederland. In een toekomstig sectorplan zullen bovenstaande inversteringsgebieden verder en doelgericht worden uitgewerkt.