Healthcare Systems E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
This book is centered around the development of agile, high-performing healthcare institutions that are well integrated into their environment. The aim is to take advantage of artificial intelligence, optimization and simulation methods to provide solutions to prevent, anticipate, monitor and follow public health developments in order to intervene at the right time, using tools and resources that are both appropriate and effective. The focus is on the people involved - the patients, as well as medical, technical and administrative staff - in an effort to provide an efficient healthcare and working environment that meets safety, quality and productivity requirements. Heathcare Systems has been written by healthcare professionals, researchers in science and technology as well as in the social sciences and humanities from various French-speaking countries. It explores the challenges and opportunities presented by digital technology in our practices, organizations and management techniques.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
Foreword
Preface
PART 1: Optimization and Simulation of Healthcare Systems
Summary of Contributions – Part 1
1 Towards a Prototype for the Strategic Recomputing of Schedules in Home Care Services
1.1. Introduction
1.2. Literature review
1.3. Description of the problem
1.4. Resolution method
1.5. Presentation of the prototype
1.6. Tests and results
1.7. Conclusion and perspectives
1.8. References
2 Home Healthcare Scheduling Activities
2.1. Introduction
2.2. State of the art
2.3. Description of the proposed approach
2.4. Experiments and results
2.5. Conclusions and perspectives
2.6. References
3 Optimal Sizing of an Automated Dispensing Cabinet Under Adjacency Constraints
3.1. Introduction
3.2. Problem statement
3.3. Mathematical formulation
3.4. Application example
3.5. Conclusion
3.6. References
4 Validation of an Automated and Targeted Pharmaceutical Analysis Tool at the CHU de Liège
4.1. Introduction
4.2. Methods
4.3. Results
4.4. Discussion and conclusion
4.5. References
5 Simulation of Countermeasures in the Face of Covid-19 Using a Linear Compartmental Model
5.1. Introduction
5.2. The compartmental model
5.3. A linear SIR model
5.4. Results
5.5. Conclusion
5.6. References
PART 2: Digital and New Technologies for Health Services
Summary of Contributions – Part 2
6 Towards a New Classification of Medical Procedures in Belgium
6.1. Introduction
6.2. Methodology
6.3. Results
6.4. Discussion
6.5. Conclusion
6.6. References
7 Digital Toolkit for the Ergonomic Evaluation of Workstations
7.1. Introduction
7.2. ProcSim and ergonomics
7.3. Ergonomic assessment process
7.4. Conclusion
7.5. References
8 Simulation on an RFID Interactive Tabletop with Tangible Objects of Future Working Conditions: Prospects for Implementation in the Hospital Sector
8.1. Introduction
8.2. State-of-the-art on the simulation of future working conditions
8.3. Proposal for a simulator on an interactive tabletop
8.4. Development of a first version of a simulator on an interactive tabletop
8.5. Application opportunities in the healthcare industry
8.6. Conclusion and perspectives in the healthcare industry
8.7. Acknowledgments
8.8. References
9 Robotic Geriatric Assistant: A Pilot Assessment in a Real-world Hospital
9.1. Introduction
9.2. Geriatric assessment: from needs to the proposed solution
9.3. Methodological approach: living lab approach
9.4. Pilot assessment
9.5. Conclusion
9.6. Acknowledgments
9.7. References
10 Perspectives on the Patient Experience (PX) of People with Disabilities in the Digital Age: From UX to Px
10.1. Introduction
10.2. State-of-the-art on Patient experience (PX)
10.3. Research methodology and proposal
10.4. Illustrations relating to the “user research” phase of the methodological framework
10.5. Case study: digital care journey of a patient with a disability
10.6. Conclusion
10.7. References
PART 3: Change Management and Organizational Innovations
Summary of Contributions – Part 3
11 Jointly Improving the Experience of All Stakeholders in Hospital 4.0: The ICSSURP Initiative
11.1. Introduction
11.2. Digital transformation to Hospital 4.0
11.3. Essential qualities of information systems of Hospital 4.0
11.4. Towards a joint security, safety, usability, resilience and performance engineering initiative (ICSSURP)
11.5. Conclusion and perspectives
11.6. References
12 A Tool-based Approach to Analyze Operating Room Schedule Execution: Application to Online Management
12.1. Introduction
12.2. Methodology used to generate our approach
12.3. Current version of the proposed tool-based approach
12.4. Applied example of our tool-based approach at the
Centre Hospitalier de Narbonne
12.5. Conclusion and perspectives
12.6. References
13 Planning Patient Journeys in Outpatient Hospitals to Support the Ambulatory Shift
13.1. Introduction
13.2. Background and state-of-the-art methods
13.3. State-of-the-art and field of application
13.4. Contribution
13.5. Discussion and perspectives
13.6. Conclusion
13.7. References
14 Treatment Protocols Generated by Machine Learning: Putting a Case Study of Hospitalization at Home into Perspective
14.1. Introduction
14.2. Context and perspective
14.3. The contribution of protocolization
14.4. Study and proposed methodology
14.5. Conclusion
14.6. References
15 Resilience of Healthcare Teams: Case Study of Two Cardiology Intensive Care Units
15.1. Introduction
15.2. Theoretical framework
15.3. Research methodology
15.4. Research results
15.5. Discussion
15.6. Conclusion
15.7. References
Conclusion and Perspectives
Glossary
List of Authors
Index
End User License Agreement
List of Tables
Chapter 2
Figure 2.1.
Genetic algorithm (source: (Vishnupriyan et al. 2008). For a color v...
Figure 2.2.
Online rescheduling process. For a color version of this figure, see...
Chapter 3
Figure 3.1.
Drawer configuration
Figure 3.2.
Types of compartments in a 3 x 3 drawer
Figure 3.3.
Flowchart of the allocation problem decision process
Chapter 4
Figure 4.1.
Presentation of IT solutions throughout the medication process (Seid...
Figure 4.2.
Schematic representation of the study. For a color version of this f...
Figure 4.3.
Example of a decision tree developed and institutionally validated
Figure 4.4.
Translated extract from the “web-report” generated by the automated ...
Chapter 5
Figure 5.1.
The compartmental model
Chapter 6
Figure 6.1.
The standardization and classification procedure
Chapter 7
Figure 7.1.
Data collection. For a color version of this figure, see www.iste.co...
Figure 7.2.
Analysis of collected data. For a color version of this figure, see ...
Figure 7.3.
Workstation modeling. For a color version of this figure, see www.is...
Figure 7.4.
Virtual reality tests. For a color version of this figure, see www.i...
Chapter 8
Figure 8.1.
The three stages of the activity simulation process of an interactiv...
Figure 8.2.
Example of a simulation on an interactive tabletop. For a color vers...
Figure 8.3.
Drag and drop stats menu selection. For a color version of this figu...
Chapter 9
Figure 9.1.
(a) Overview of the robotic system; and (b) a real patient interacti...
Figure 9.2.
Different iterative phaszes of development
Figure 9.3.
Different viewing angles to analyze activity
Figure 9.4.
Users’ preferred interaction modes
Figure 9.5.
Average test time: 10–11 minutes. The numbers within the circle deno...
Figure 9.6.
Assessment scores for interaction aspects (out of 5)
Chapter 10
Figure 10.1.
Iterative design cycle of interactive systems (adapted from Lallema...
Figure 10.2.
General view of the digital health concept. For a color version of ...
Figure 10.3.
Global view of the development methodological framework (inspired a...
Figure 10.4.
Persona: Alain. For a color version of this figure, see www.iste.co...
Figure 10.5.
Principle of holistic visualization through storyboards of the acti...
Figure 10.6.
Example of an eXperience map for an “appointment book” patient jour...
Chapter 11
Figure 11.1.
The modern-day digital context of Hospital 4.0. For a color version...
Figure 11.2.
Information systems of Hospital 4.0 (ISH4.0) and their essential qu...
Figure 11.3.
Information systems of Hospital 4.0 and the advanced conceptual mod...
Chapter 12
Figure 12.1.
Flowchart presenting the different stages of our retrospective anal...
Chapter 13
Figure 13.1.
Breakdown for types of patient flows. For a color version of this f...
Figure 13.2.
Classification of planning inputs for outpatient medicine. For a co...
Figure 13.3.
A quantified stream mapping of patient pathway, essential technical...
Figure 13.4.
Impact of the macro-planning for groups of pathways on resources (o...
Figure 13.5.
Process and adaptation of integrated planning (MRP2) for ambulatory...
Chapter 14
Figure 14.1.
Positioning of HaH and traditional medicine in relation to the pati...
Figure 14.2.
Analysis of expenses relating to HaH “Soins et Santé” (Lyon) stays ...
Figure 14.3.
Analysis of expenses relating to HaH stays over the year 2018. For ...
Figure 14.4.
Distribution of the daily cost of healthcare according to the numbe...
Figure 14.5.
Weighted distribution of patient characteristics in the forecasting...
Guide
Cover
Table of Contents
Title Page
Copyright
Foreword
Preface
Begin Reading
Conclusion and Perspectives
Glossary
List of Authors
Index
End User License Agreement
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Series Editor
Jean-Charles Pomerol
Healthcare Systems
Challenges and Opportunities
Edited by
Sondès Chaabane
Etienne Cousein
Philippe Wieser
First published 2022 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2022
The rights of Sondès Chaabane, Etienne Cousein and Philippe Wieser to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2021951467
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-799-6
Foreword
The first GISEH conference was in 2003 at Hôpital de la Croix-Rousse in Lyon, the result of a collaboration between the PRISMa laboratory at the Institut National des Sciences Appliquées de Lyon and the Pôle Nord des Hospices Civils de Lyon on the organization and management of operating theatres, which sought to question the adaptability of industrial engineering tools in a hospital setting. At that time, changes to funding of hospital activities through Activity-Based Costing (as part of the Hôpital 2007 plan) forced healthcare players to refocus on medico-technical platforms that created added value. This first conference attracted 106 participants from France, Belgium, Luxembourg, Switzerland, among others, with around 66 presentations directly addressing issues in the hospital world and in collaboration with hospital workers. GISEH takes is designed to span the fields of engineering, the social sciences and medicine and thus assumes a cross-cutting form.
A little over a year later in 2004, the city of Mons in Belgium hosted the second GISEH at the Facultés Universitaires Catholique de Mons (FUCaM). Belgian hospital workers are subject to the same constraints and share the same objectives as their French counterparts, and it is with this in mind that their projects and initial feedback is presented. This second conference was equally successful with about 100 participants and 57 publications. Mons gives its French-speaking dimension to GISEH and thus a greater dynamism.
2006 saw the third GISEH, this time hosted in Luxembourg at the Centre de Recherche Public Henri Tudor, which works on health and social security issues in collaboration with numerous hospitals. A total of 190 participants came to listen to 75 presentations. Special issues of the best works were published in French-language journals such as Logistique et Management, La Valeur or Gestion Hospitalière. This third conference cemented GISEH as a significant contributor in discussions on hospitals and healthcare.
The Ecole Polytechnique de Lausanne organized the 4th GISEH in 2008. A total of 160 participants, a third of whom were hospital-based, came to listen to 89 presentations. Logistical, operational, informational and decision-making activities were widely discussed and brought together in the GISEH melting pot. The fields under investigation at GISEH conferences were broadening and giving new meaning to the cross-cutting spirit between the engineering, social and medical sciences.
The 5th GISEH was organized in September 2010 at the Centre Hospitalier Universitaire de Clermont-Ferrand by LIMOS (Laboratoire d’Informatique, de Modélisation et d’Optimisation des Systèmes).Two hundred participants, including 73 hospital staff, considered new structures in order to meet the objectives set for them, in terms of patient satisfaction, quality of care, cost reduction and time management. This was a return to the original issues that are still very present and as such require innovative techniques.
In 2012, GISEH crossed the Atlantic and held its 6th conference in Quebec City at the Centre Interuniversitaire de Recherche sur les Résaux d’Entreprise, la Logistique et le Transport (CIRRELT). New techniques such as lean or risk management were widely discussed in areas ranging from hospitals to home care and general medicine. A total of 147 participants, including 67 hospital staff, came to listen to 70 presentations. The journey to the New World was a success.
The 7th GISEH conference took place in Liège in the summer of 2014, at the Centre Hospitalier Universitaire de Sart Tilman; it generated the highest turnout with 222 speakers. The theme for GISEH 2014, organized, run and supported by Liégeois hospitals was: “The Hospital: A Company Like Any Other?”. An exciting and particularly topical subject in 2014, due to the requirement for all healthcare establishments and organizations to ensure a certain level and quality of service, while at the same time guaranteeing cost control. Engineering of healthcare systems, management of care systems, information systems, organizational development and knowledge management brought together the various presentations and generated numerous discussions.
Having crossed the Atlantic, the next international destination was the Mediterranean, and so the 8th GISEH conference came to Casablanca in the summer of 2016. It was hosted at the Université Mohammed VI des Sciences de la Santéand organized under the theme, “The Hospital and the Territorialization of Health: Transversality as a Key Factor of Sustainable Performance”. It brought together 167 participants eager to discuss different health systems and multiple health situations. The French-speaking community of GISEH was thus fully established and served.
The 9th GISEH was organized in the summer of 2018 at the Hôpitaux Universitaires de Genève.The aim of GISEH 2018 was to focus on the performance of health systems, networks and institutions. Its objectives were to take stock of the situation, identify the causes of the problems and investigate the avenues and mechanisms that could be employed to improve the performance of health systems, networks and institutions. 260 participants promoted discussion on these themes through 76 presentations.
In 2020 the world was faced with a pandemic that led governments to confine over half of the human population. The Université Polytechnique Hauts-de-France continued to host the 10th GISEH, despite the travel constraints imposed by the health situation, by conducting the event through videoconferences. The theme for GISEH 2020 was, “Towards Innovative Health and Healthcare Institutions, Integrated in their Environments and Performance”. One of the objectives was to study the integration of the hospital into healthcare and prevention approaches. There was also a focus on the situation of vulnerable people: children, the elderly, people with disabilities, people with cancer, etc. Thirty-five presentations were delivered despite the Covid-19 pandemic.
Thus far, 10 GISEH conferences have been held in French-speaking countries over the past 18 years. Academics and hospital staff, driven by the social, economic and environmental efficiency of essential human systems working towards the well-being of humanity, will have shared their ideas, built projects, put their solutions into practice and built common skills. At the ripe age of 63, I am leaving for the militant world of mutual insurance companies in healthcare. I leave the GISEH conferences in the capable hands of Philippe Wieser and Sondès Chaabane, two friends who have already made invaluable contributions to the success of these conferences.
Alain Guinet
Emeritus Professor
Chairman of the GISEH Steering Committee, 2003–2020
October 2021
Preface
Healthcare institutions will evolve towards becoming more innovative institutions by pursuing defined paths that consider risk factors and the collective and individual context of patients. They will take advantage of information technologies, scientific methods and mass data to prevent, anticipate, monitor and follow developments in public health, in order to intervene at the right time with the right tools. They will focus on people, be it the patient, the citizen or the caregivers by providing a high-performance health environment that meets safety, quality and productivity requirements.
The theme of this book centers around the engineering and management of healthcare and healthcare institutions, with the aim of making them agile and integrated into their environments. The book thus deals with the “hospital” system and the path to good health: issues, prevention approaches, data/IT, organizational innovations and recent technological solutions. It also opens up new opportunities for improving hospital systems with advances in artificial intelligence, information systems and service robotics.
The current context of the Covid-19 pandemic that we have been experiencing since early 2020 has only confirmed this need for openness, modernization and innovation in our healthcare systems. This crisis has shown us that we are vulnerable, that everyone is affected by these public health issues and that these can only be resolved through a collective and global effort. So, one wonders, what is the role of the hospital? Does it wait for a request for care? It is clear that the hospital cannot take care of everything and yet it should pay attention to everything. Our motivation for this book is to present scientific work that will allow our hospitals to evolve into innovative hospitals, open to their environment and efficient where health is concerned, not only to deliver care but also allowing citizens to prevent diseases and put their health at the heart of their priorities.
This book is presented in three different parts, each composed of five chapters that have been written by health professionals, researchers in science and technology, as well as in the human and social sciences from different Frenchspeaking countries. The first part presents the various works that have been addressed optimization and simulation in healthcare systems. This research shows the potential and the opportunities offered by these techniques to support institutions in their management and organization of care methods. The second part focuses on digital and electronic technologies with an emphasis on artificial intelligence methods, allowing for the intelligent use of data or even robotization in the integration of technologies our healthcare offers, the working conditions of our caregivers or the assistance being afforded to fragile and vulnerable people. These first two parts highlight opportunities for innovative organizations and better management, as well as the threats related to changes in the daily life and practices of our institutions. The third part of the book therefore responds to the problems of change management and the impacts on our practices, our organizations and our management methods.
Sondès Chaabane
October 2021
PART 1Optimization and Simulation of Healthcare Systems
Summary of Contributions – Part 1
Towards a Prototype for the Strategic Recomputing of Schedules in Home Care Services,by Cléa MARTINEZ, Maria DI MASCOLO, Marie-Laure ESPINOUSE and Jérôme RADUREAU.
Home care is an alternative to traditional hospitalization to cope with aging populations and the increase in the number of vulnerable people whilst ensuring a good quality of life for patients. Even though these structures are developing more and more, the planning of their activities remains manual and can be time-consuming and exceedingly complex on a large scale. It is therefore essential to have effective solutions for planning interventions with an update mechanism to compensate for unforeseen events. This contribution therefore offers a decision support tool to solve the problem of long-term re-scheduling in the home care sector. A weekly planning update prototype was developed to meet a need expressed by a home care company operating in Auvergne Rhône-Alpes: Adomni-Quemera.
Home Healthcare Scheduling Activities,by Rym BEN BACHOUCH JACQUIN and Jihene TOUNSI.
This chapter examines the problem of planning rounds in hospitalization at home (HaH) by taking into account uncertainties and integrating dynamic re-scheduling. The authors propose an approach based on a genetic algorithm which considers re-scheduling in real time to resolve any conflicts that may arise as a result of unforeseen disruptions which may occur. This approach takes place in two phases. The first phase is carried out “offline”, making it possible to establish the schedules of the nursing staff. The second phase is “online”, making it possible to manage unforeseen events in real time, such as the absence of staff and delays in care. In the disrupted context, a new schedule is generated in order to cope with the disruption. The results obtained show the efficiency and robustness of the proposed approach with rapid computation time.
Optimal Sizing of an Automated Dispensing Cabinet under Adjacency Constraints,by Khalid HACHEMI, Didier GOURC and François MARMIER.
This contribution focuses on the dispensing phase of the medication circuit. This phase corresponds to the validation of the prescription, preparation and delivery of the medication. The main objective of the study is to minimize the errors that occur during this phase and, more specifically, in the case of automated dispensation cabinets. Such errors can occur when the cabinet is filled with the wrong product in the wrong place resulting in the wrong medication being delivered to the patient. To resolve this problem, the authors propose an algebraic model for the calculation of boundary conditions necessary for the allocation of medications to different compartments of a cabinet. This method must ensure that certain products are not placed in neighboring compartments due to the risk of confusion, which may lead to distribution errors, for example, medicinal products having a similar appearance, nomenclature or dosage. A modeling of the maximum permissive target problem (MPTP) has been proposed, as well as its resolution through a real-world digital application.
Validation of an Automated and Targeted Pharmaceutical Analysis Tool at the CHU de Liège,by Sophie STREEL, Nathalie MAES, Véronique GONCETTE, Laurence SEIDEL, Denis MENAGER, Adelin ALBERT, Philippe KOLH and Didier MAESEN.
This next contribution also looks at the medication flow, which can be a major challenge for a hospital. This flow is a complex and multidisciplinary process, which includes the clinical pathways (prescription, distribution, administration) and logistics (supply, transport, storage). The computerization of the medication flow at the “CHU de Liège” is widely implemented. This study describes how an automated and targeted pharmaceutical analysis tool was built, implemented and evaluated at the “CHU de Liège”. The objective of this tool is to provide reliable and solid support to pharmacists for the verification of prescriptions so as to optimize and meet the hospital’s accreditation criteria. Various pharmaceutical validation algorithms were thus constructed, and the computer validation tool was developed and tested on these bases which generated very encouraging results.
Simulation of Countermeasures in the Face of Covid-19 Using a Linear Compartmental Model,by Alain GUINET.
Since the start of 2020, the whole world has been confronted by a pandemic, which led to the confinement of over half of the world’s inhabitants. We are disarmed in the face of the coronavirus (SARS-CoV-2), and containment seems to be the only countermeasure capable of containing the pandemic despite the consequential economic cost. This chapter presents a simulation of the epidemic based on the SIR (Susceptible–Infected–Recovered) model, which is known as a compartmental model in epidemiology. This simulation makes it possible to calculate, by period, the people at different stages of the disease, receiving different medical treatment, and to propagate the flows of people between the states by period. A discrete representation of the SIR model over a horizon of daily periods was used. The results showed the effectiveness of the health countermeasure chosen by half of the world’s countries to deal with the Covid-19 pandemic. Containment of the population seems to be a well-suited action in the absence of an efficient treatment, such as viral treatment or a vaccine.
1Towards a Prototype for the Strategic Recomputing of Schedules in Home Care Services
1.1. Introduction
Home healthcare agencies are an alternative to standard medical or paramedical organizations, providing services directly to the beneficiary’s home. The aging of Western populations is accompanied by an increase in the number of vulnerable people and an explosion in demand for home healthcare and associated services (Guinet 2014), to which the organizations concerned must adapt. Service routing and scheduling is usually done by hand by experienced employees but this is time-consuming and extremely complex on a large scale. The design of decision support tools is becoming essential to automate the routing and scheduling process and build schedules that are satisfactory for the employer, the careworkers and the beneficiaries.
Having effective solutions to plan interventions is unfortunately not always sufficient to meet the challenge of routing and scheduling in the home care sector. Indeed, many eventualities can make a theoretically optimal schedule unfeasible. It is thus necessary to update the schedules to compensate for these unforeseen events. We are particularly interested in changes in the configuration of staff and beneficiaries of a home healthcare organization. Over the weeks, a beneficiary’s state of health can deteriorate, which in turn leads to changes in their needs and therefore in the services required, or even bidding farewell to the organization in the event they require hospitalization, for example. When beneficiaries leave, the establishment can, if the number of careworkers is high enough, accept new beneficiaries who will have to be included in the schedules. Likewise, there is a high turnover of staff because the careworkers are often subjected to difficult, stressful working conditions.
From a strategic, decision-making point of view, it would be inappropriate to recalculate optimal routes every time the schedules are disrupted. In a context where the human aspect is essential, it is necessary to take into account the schedule in progress, the assignments of careworkers or even the start time of the interventions, in order to satisfactorily reconstruct disrupted routes. Indeed, continuity is a key factor in patient satisfaction. Consequently continuity of care constraints must be respected by, on the one hand, keeping fixed start and end times (these are also defined contractually) and on the other hand, always assigning the same group of careworkers to the same patients. In order not to aggravate these instabilities, it is also crucial to take careworker satisfaction into account. Schedules that are not satisfactory for the staff increase staff turnover, which impacts the company’s quality of service. In a field where competition is increasingly fierce (Béguin 2018), providing good working conditions is not only a central argument for recruiting qualified careworkers but it also plays a decisive role in limiting turnover.
It is important to note that updating long-term schedules consists of creating new sustainable weekly or monthly schedules, in line with changes to staff and beneficiaries, while short-term rescheduling instead aims to provide a quick fix to the problem; for example, a daily schedule compromised by a one-off disruption, such as the sudden absence of a worker for a day.
In this chapter, we present a prototype developed to respond to a long-term weekly rerouting and rescheduling problem encountered by a home care services company operating in Auvergne Rhône-Alpes, France: Adomni-Quemera. We first offer a brief review of existing work on this theme in the literature, followed by a more specific description of the problem under consideration. In section 1.4, we briefly advance our resolution strategy, before presenting the prototype developed to propose solutions in a practical setting in section 1.5. The experiments carried out on real data are detailed in section 1.6. Finally, we approach avenues of research for future work in section 1.7.
1.2. Literature review
The Home Health Care Routing and Scheduling Problem (HHCRSP), i.e. the problem of planning home care routes, appears for the first time – to our knowledge – in 1997 in the article by Begur et al. (1997). Their method is based on adaptations of heuristics derived from algorithms for solving vehicle routing problems (VRP); in particular the classical methods developed in Clarke and Wright (1964) and Lin and Kernighan (1973). The modeling of the HHCRSP as a variant of the VRP is quite classic and Cheng and Rich (1998) were the first to adapt it to mixed-integer linear programming. They model the problem through a multi-depot vehicle routing problem (MDVRP) with multiple time windows, over a single-period horizon. The goal is to minimize overtime and tests are conducted on small case studies, made up of 4 nurses and 10 patients.
Since then, much research has focused on solving such problems, as they constitute a topical issue, both in practice and in the field of research with real scientific obstacles. For further details, the reader can refer to recent literature reviews: Cissé et al. (2017), Fikar and Hirsch (2017), Grieco et al. (2020) and Di Mascolo et al. (2021).
The problem is NP-hard, and thus difficult to solve in practice due to the presence of numerous business and industry-specific constraints, which are often treated with metaheuristics (Decerle et al. 2018) or with decomposition methods, such as the approach developed in the prototype presented here. In Grenouilleau et al. (2017), the authors propose a two-step algorithm to solve the routing and scheduling problem with minimization of overtime, staff qualification constraints and the possibility of not providing all the services requested. An LNS algorithm makes it possible to generate feasible routes, then a set partitioning model whose linear relaxation is repaired through a constructive heuristic, making it possible to select the routes that constitute the final schedule. In Fikar and Hirsch (2015), identification of potential routes precedes the overall scheduling optimization phase. The problem can also be broken down into a first step of assigning services to careworkers, then solving a traveling salesperson (TSP) problem for each of them. Issaoui et al. (2015) add a third step to this decomposition, in which they improve the routes obtained using a heuristic.
Most of the time, the objectives studied relate to the economic aspects of the problem, namely cost minimization (Fathollahi-Fard et al. 2019). However, there is also a particular interest in patient satisfaction (Mosquera et al. 2019). Careworker satisfaction, which is rarely studied, mainly consists of balancing the workload of careworkers (Cappanera and Scutellà 2014). The three stakeholders of the problem, namely the careworkers, the beneficiaries and the employer of the organization, generally have divergent interests and objectives. In Carello et al. (2018), a linear program, with several objective functions integrated into a threshold method, makes it possible to establish a compromise between all the stakeholders of the problem.
While the research cited above addresses the problem of static scheduling, it should be noted that in the home healthcare industry, it is rare to be able to maintain a functional schedule over a long period. Indeed, the instability inherent in the health sector quickly makes scheduling obsolete or not suited in the dynamic aspects of the problem (Cappanera et al. 2018).
In Heching et al. (2019), a Benders decomposition is used to accurately solve the problem of re-scheduling following the departure or arrival of patients. The first step is to solve an assignment problem with a mixed linear program. Next, a constraint programming model is used to plan the routes. The objective is to maximize the number of patients visited over a weekly period while respecting continuity constraints. In Nickel et al. (2012), the re-scheduling problem is solved by integrating new patients into the system with an insertion heuristic, then improving the solution with an LNS-type algorithm. The evolution of the patient population is considered in these two articles, while the composition of the staff remains unchanged.
In the literature, a wide range of constraints and objectives have been considered. However, much of the work remains theoretical and does not make it possible to deal with actual cases or to offer operational solutions given that, for example, legal constraints are often only partially addressed. In Szander et al. (2018), the authors want to reduce this gap between theoretical research and real-life situations through a case study of a home care center in Hungary, where the appointment times are fixed, and the objective is to minimize travel costs while maximizing beneficiary satisfaction through an MILP. In Gomes and Ramos (2019), the authors deal with a re-scheduling problem with the constraints of non-continuity of care that are not very common in the literature but which stem from actual cases encountered in Portugal: a non-profit organization and a Catholic parish where part of their mission is home help. Using different mixed linear programs, they are developing a multi-objective approach that aims to reduce travel times while minimizing the disruption associated with the departure and arrival of new patients. Once again, careworker changes are not considered here.
Thus, more and more works are interested in actual cases, even though their applied aspect is often limited to experiments on real data. Nevertheless, a few methods developed in direct relation with actors in the sector have made it possible to develop prototypes that can be used in practice and even software that is now deployed in practice. To the best of our knowledge, the first decision support system for this type of problem was proposed by Begur et al. (1997). An optimization module for the routing phase is integrated into a geographic information system that allows us to view routes. The LAPS CARE tool (Eveborn et al. 2006), now used in care units in Sweden, makes it possible to plan routes according to patient preferences and Swedish regulations. The proposed routes do not necessarily satisfy all the constraints and must be modified by the users when necessary. However, the scheduling task is made easier through the use of this tool, especially when it comes to quickly re-optimizing routes to deal with a last-minute, unforeseen event. More recently, a decision support system was created to support the Ottawa Hospital in its setup for home dialysis (Kandakoglu et al. 2020). Schedules are set by the day and last-minute disruptions are managed with the help of “floating” nurses who can act as replacements at a moment’s notice.
Here, we are interested in a real-world scenario that includes more constraints than most of the work in the literature, as we consider almost all of the constraints arising from French national collective agreements (lunch breaks, work shifts, effective working time). We pay particular attention to the continuity of care, which constitutes a real scientific challenge. We consider the variations in beneficiaries as well as in the staff, and consider updating schedules on a strategic level, while most of the work in the literature is concerned with managing disruptions in the shorter term. Our aim is to propose solutions applicable in practice which therefore respect both the legislation and several rules of use and internal functioning of the organization with which we collaborate. We present a prototype whose objective is to allow the decision-makers to generate new schedules themselves, to keep the database up-to-date as careworkers and beneficiaries evolve. It should be noted that we find optimal solutions for real-size instances (up to 92 beneficiaries and 337 services).
1.3. Description of the problem
We consider the case of a home care organization that employs careworkers characterized by different levels of qualifications, specific time availability and a contractual and individualized monthly work volume. The organization plans its routes to provide a set of services required by a set of beneficiaries who need careworkers with different qualifications.
We assume that a schedule has already been established but has become obsolete due to changes in staff or beneficiaries. We consider the cases where one or more beneficiaries and/or careworkers leave the organization and where new beneficiaries and/or careworkers must instead be integrated into the schedules.
1.3.1. Constraints
New routes should be designed so that all services are provided while respecting constraints that we have divided into several categories: continuity constraints, legal constraints and constraints arising from the field.
1.3.1.1. Continuity constraints
Two types of continuity constraints are considered: time continuity constraints and human continuity constraints. These constraints are characteristic elements of the routing and scheduling update.
We must respect the continuity of the schedule by keeping the start and end times unchanged from those set initially. In our case study, these start and end times are indeed contractual and cannot be called into question for each change in the schedule.
Next, we must respect so-called “human” continuity constraints. We define a continuity rate for each beneficiary, depending on the number of careworkers they know and the maximum number of different careworkers they can tolerate. Depending on the value of this rate, the intervention of employees still unknown to the beneficiary may be authorized. This makes it possible both to integrate new arrivals into the schedules and to compensate for the departure of careworkers.
To our knowledge, the constraints of time continuity are rarely studied in the literature, if at all. As for the constraints of human continuity, they represent a scientific challenge.
1.3.1.2. Legal constraints
These constraints result from French collective agreements (Legifrance 2012).
This involves limiting the amplitude of a work day, the effective working time of a day, a week, and of a month, guaranteeing breaks, in particular the lunch break, and prohibiting too many breaks within a single day and, more specifically, long breaks.
By amplitude we refer to the total duration of a work day, except for the first and last trip of the day. The waiting time under the collective agreements corresponds to the time not worked that is less than 15 minutes between two successive interventions. As for the effective working time, this is the total duration of the services performed during the day with waiting times strictly less than 15 minutes between two services and travel times, except the first and last visit of the route. We make sure that each careworker has a lunch break and takes a break when working for longer than 6 hours at a time.
Finally, a careworker must have the necessary qualifications to provide a service and overqualified work is allowed.
1.3.1.3. Internal policies
Some constraints are internal policies inherent in the home care industry or those specific to the organization in which we are interested. We build quarter-hour schedules, for obvious practical reasons. We allow for overtime but within a limit of 20% of the contractual hourly volume of the careworker concerned. It is possible to run two services consecutively with overlapping schedules if the duration of this overlap (including travel time) does not exceed 10 minutes. Finally, when the schedule is carried out for a week, the monthly contractual hourly volume is reduced to a weekly volume by dividing the monthly volume by 4.33.
1.3.2. Objective function
The goal is to minimize the total waiting time induced by the schedule, i.e., the sum of all the careworker waiting times over the planning horizon. As we want to provide satisfactory schedules for careworkers, we count the waiting times as the sum of all the time not worked that is less than 90 minutes between two successive interventions, not counting the travel times. Indeed, when careworkers have an inactivity time of more than 90 minutes, we consider that they can take advantage of this period to conduct personal activities.
1.4. Resolution method
Our resolution method is an exact method that is based on a decomposition of the problem into two sub-problems: the generation of the set of admissible daily routes and then the selection of a set of daily routes to carry out a weekly schedule (or possibly a monthly schedule).
In a previous article (Martinez et al. 2019), we proposed an algorithm which allowed us to solve a similar theoretical problem. In the prototype presented here, we have adapted it in order to take into account all the routing and scheduling constraints and cover all the perturbations considered, and not just the departure of beneficiaries. We are not focusing here on the developed method but rather on its implementation and its integration into the prototype.
1.4.1. Route generation
We start by generating a set of graphs, one for each day and careworker, in order to represent our problem. For a particular graph, a set of vertices represents the set of services that could be provided by this careworker, that is, the services for which he or she is qualified, whose start and end times correspond to their availability, and for which the beneficiary is compatible with the continuity constraints of the problem. To this set of vertices, we add two fictitious services representing the start and end points of a route.
We then create arcs between all the pairs of services which could be performed consecutively by the same careworker. It is thus a matter of ensuring that the start and end times of the two services are compatible, in particular with regard to the travel time between the homes of the two beneficiaries concerned.
A route from the source to the sink thus represents a sequence of compatible services, i.e., a daily route. This route will be said to be admissible if it respects all the daily legal constraints. By constructing the graph, we have already made sure that the constraints of continuity and qualifications are respected.
We weight each arc by the travel time between the two homes of the beneficiaries concerned, the duration of the arc’s first service and the waiting time between the two services, in terms of the collective agreements. If we generate a route from the source to the sink by limiting its length, then we can control the amplitude, the effective working time and the waiting time of the route to which it corresponds. By adapting the algorithm proposed in Rizzi et al. (2015), we can efficiently compute all the admissible daily routes for a careworker on a given day.
1.4.2. Route selection
Once all the routes have been generated, we need to assign one route per careworker per day, to ensure that the weekly and/or monthly legal constraints are respected and that the overall waiting time of the careworkers is minimal. For this purpose, we use a linear integer program to solve this set partitioning problem. We add constraints to ensure that the weekly (or monthly) working regulations defined in the French collective agreements are respected, as well as the human continuity constraints.
1.5. Presentation of the prototype
The optimization module described above was developed in Java. We have also developed a basic graphical interface in Excel, which also contains all the data relating to the beneficiaries and the staff of the home care service organization under consideration, as well as the parameters resulting from our case study, which are necessary for the execution of the algorithm. The interface provided allows the user to make changes to the database and software settings and then generate routes.
Thanks to dedicated forms, the user can add new beneficiaries or careworkers, modify those who are already entered in the database or delete them. The distance and travel time calculations between beneficiaries are then calculated automatically from the addresses entered, using the data provided by Google Maps via its geolocation API.
The parameters relating to the rules that are the result of the collective agreements and internal policies of our applied case can also be modified, as well as the sectorization of the territory covered by the company.
