- Email: [email protected]
Patient-centered multi agent system for health care
Information Control Problems in Manufacturing Proceedigs Proceedigs of of the the 15th 15th IFAC IFAC Symposium Symposium on on May 11-13, 2015. Ottawa, Canada Information Control Problems Information Control Problems in in Manufacturing Manufacturing Available online at www.sciencedirect.com May May 11-13, 11-13, 2015. 2015. Ottawa, Ottawa, Canada Canada
ScienceDirect IFAC-PapersOnLine 48-3 (2015) 710–714
Patient-centered multi agent system for health care Patient-centered Patient-centered multi multi agent agent system system for for health health care care N. Benhajji* D. Roy** D. Anciaux*** N. Benhajji* D. N. Benhajji* D. Roy** Roy** D. D. Anciaux*** Anciaux*** * Ecole Nationale d’Ingénieurs de Metz, 1 route d'Ars Laquenexy, CS 65820, 57078 Metz, France (Tel: +33(0)6de 52Metz, 36 7311 78; e-mail: * route d'Ars Laquenexy, * Ecole Ecole Nationale Nationale d’Ingénieurs d’Ingénieurs de Metz, route d'Ars [email protected]) Laquenexy, CS CS 65820, 65820, 57078 57078 Metz, Metz, France France ** Ecole Nationale des de 36 Metz, 1 route d'Ars Laquenexy, CS 65820, 57078 Metz, France (Tel: +33(0)6 73 e-mail: [email protected]) (Tel:Ingénieurs +33(0)6 52 52 36 73 78; 78; e-mail: [email protected]) (Tel: +33(0)3 87 79 68 26; e-mail: [email protected]) ** Ecole Ecole Nationale Nationale des des Ingénieurs Ingénieurs de Metz, Metz, route d'Ars Laquenexy, CS 65820, 65820, 57078 57078 Metz, Metz, France France ** de 11 route d'Ars Laquenexy, CS *** Université de Lorraine – UFR-MIM, île de saulcy, 57045 Metz, France (Tel: +33(0)3 87 79 68 26; e-mail: (Tel: +33(0)3 87 79 68 26; e-mail: [email protected]) [email protected]) : +33 (0)3 87 31 54 ––52UFR-MIM, e-mail: [email protected]) *** Université de île ***(Tel Université de Lorraine Lorraine UFR-MIM, île de de saulcy, saulcy, 57045 57045 Metz, Metz, France France (Tel : +33 (0)3 87 31 54 52 e-mail: [email protected]) (Tel : +33 (0)3 87 31 54 52 e-mail: [email protected]) Abstract: The increasing demand for health care led healthcare organizations to efficiently and effectively their process. Indeed, hospitals are led confronted to budget restrictions certification Abstract: The demand for care healthcare organizations to efficiently and Abstract: organize The increasing increasing demand for health health care led healthcare organizations to and efficiently and obligations. To this end, planning and control in health care has received an increasing amount of effectively organize their process. Indeed, hospitals are confronted to budget restrictions and certification effectively organize their process. Indeed, hospitals are confronted to budget restrictions and certification attention over the last ten years, both in practice and literature. The challenge is twofold: on one hand, obligations. To this end, planning and control in health care has received an increasing amount obligations. To this end, planning and control in health care has received an increasing amount of of healthcareover institutions to provide with theliterature. best possible On the hand, to attention the ten both in and The challenge is twofold: on one hand, attention over the last last have ten years, years, both patients in practice practice and literature. The care. challenge is other twofold: onthey oneaim hand, balance workload and have maximize resource utilization. The present paper presents patient-centered multi healthcare institutions to patients with best possible care. On hand, to healthcare institutions have to provide provide patients with the the best possible care. On the theaother other hand, they they aim aim to agents system for and healthcare to plan and control the patient flow.paper The proposition is a decision support balance workload maximize resource utilization. The presents multi balance workload and maximize resource utilization. The present present paper presents aa patient-centered patient-centered multi system for healthcare managers to improve their functioning and manage unpredictable hazards and agents system for healthcare to plan and control the patient flow. The proposition is support agents system for healthcare to plan and control the patient flow. The proposition is aa decision decision support disruptions. system for healthcare managers to improve their functioning and manage unpredictable hazards and system for healthcare managers to improve their functioning and manage unpredictable hazards and disruptions. disruptions. © 2015, IFAC (International of patient Automatic Control) Hostingsystem, by Elsevier Ltd. All rights reserved. Keywords: hospital logistics,Federation healthcare, flow, multi agents patient-centered. Keywords: hospital logistics, healthcare, patient flow, multi agents system, patient-centered. Keywords: hospital logistics, healthcare, patient flow, multi agents system, patient-centered. 1. INTRODUCTION 1. 1. INTRODUCTION INTRODUCTION Hospital is a complex system formed by a large number of autonomous wards and with strong Hospital system formed aa large number of Hospital is is aa complex complex systemancillary formed by byunits large number of interrelationship. The complications involve the relationship autonomous wards and ancillary units with strong autonomous wards and ancillary units with strong between patient,The facility, nursing involve staff, the providers, the interrelationship. complications relationship interrelationship. The complications involve the relationship complexity of diseases, treatment options, medications, etc. between patient, facility, nursing staff, providers, between patient, facility, nursing staff, providers, the the The emphasis should betreatment given tooptions, promoting the triptych complexity of medications, etc. complexity of diseases, diseases, treatment options, medications, etc. (cost, quality, time). Thebe is toto the best compromise The should given promoting the The emphasis emphasis should begoal given tofind promoting the triptych triptych between these time). three criteria. a patient(cost, The is the best (cost, quality, quality, time). The goal goalTherefore, is to to find findwe thepropose best compromise compromise centered multi-agent systemTherefore, for healthwe care to achieve the between these these three criteria. criteria. Therefore, we propose patientbetween three propose aa patientgoal outlined above. It is a decision support system to help centered multi-agent system for health care to achieve centered multi-agent system for health care to achieve the the both medical staffIt the health process and goal outlined above. is support system to goal the outlined above. Itthroughout is aa decision decision supportcare system to help help the providingthe dynamic decision-making both the staff throughout health process bothmanagement the medical medical staff staff by throughout the health care care process and and Model scenarios with a more optimized resource distribution. the management staff by providing dynamic decision-making the management staff by providing dynamic decision-making this complex turn out to beresource necessary. ModelingModel helps scenarios with aa more optimized distribution. scenarios withreality more optimized resource distribution. Model to analyze, understand the functioning of the investigated this complex reality turn out to be necessary. Modeling this complex reality turn out to be necessary. Modeling helps helps system, giveunderstand a true and fair of it and areas in to the functioning of the to analyze, analyze, understand the view functioning of pinpoint the investigated investigated which is required to develop it. in system,further give aaknowledge true and and fair fair view of ofinit it order and pinpoint pinpoint areas in system, give true view and areas We used both industrial practices and artificial intelligence which further knowledge is required in order to develop which further knowledge is required in order to develop it. it. techniques to industrial model our system, as intelligence multi-agent We practices and artificial We used used both both industrial practices and such artificial intelligence paradigm. Indeed, the multi-agent paradigm enhances the techniques to model our system, such as techniques to model our system, such as multi-agent multi-agent ability to model, design and implement complex, inherently paradigm. Indeed, the multi-agent paradigm enhances paradigm. Indeed, the multi-agent paradigm enhances the the distributed, and highly adaptable and cooperative ability design and complex, inherently ability to to model, model, design and implement implement complex,systems. inherently Our reflection basedadaptable on the fact patient satisfaction distributed, and and cooperative systems. distributed, andishighly highly adaptable andthat cooperative systems. is increasingly important goals set by health care Our is the that patient satisfaction is Our reflection reflection is based based toon onachieve the fact factthe that patient satisfaction is organizations. Thus, theto increasingly achieve the set by care increasingly important important tochanges achieve occurring the goals goals in sethospitals by health healthtoday care require a change in patient-centered management. organizations. Thus, the in organizations. Thus, the changes changes occurring occurring in hospitals hospitals today today require a change in patient-centered management. require a change in patient-centered management.
The remainder of this paper is structured as follows: Section two Agents, they are and how Section we can The remainder of paper structured as The reviews remainder of this thisincluding paper is is what structured as follows: follows: Section use them in health care. Based upon this, a patient-centered two reviews Agents, including what they are and how two reviews Agents, including what they are and how we we can can Multi-Agent System (MAS) is developed in the third section. use them in health care. Based upon this, a patient-centered use them in health care. Based upon this, a patient-centered This paper closes with conclusions and an future Multi-Agent System (MAS) is in the Multi-Agent System (MAS) is developed developed in outlook the third thirdtosection. section. works in thecloses fourthwith section. This paper paper closes with conclusions and and an an outlook outlook to to future future This conclusions works works in in the the fourth fourth section. section. 2. RELATED WORKS 2. 2. RELATED RELATED WORKS WORKS 1.1 Patient flow 1.1 1.1 Patient Patient flow flow The patient flow in hospital can be assimilated to the production must be incan the be bestassimilated conditions of The patient patientprocess flow that in to the The flow in hospital hospital can be assimilated tocost, the quality and safety. Thus, patient flow models are very useful. production process that must be in the best conditions of production process that must be in the best conditions of cost, cost, Indeed, changing aboutflow patient careare and treatment quality safety. Thus, models very useful. quality and and safety.decisions Thus, patient patient flow models are very useful. impacts resource use and costs. These effects can be Indeed, changing decisions about patient care and treatment Indeed, changing decisions about patient care and treatment explored.Modeling techniques include Markov, semi-Markov impacts resource use and costs. These effects can impacts resource use and costs. These effects can be be and stochastic simulation (Armony, 2011) explored.Modeling techniques include semi-Markov explored.Modeling techniques approaches include Markov, Markov, semi-Markov (Irvine, 1994) (Marcon, 2006).approaches and simulation and stochastic stochastic simulation approaches (Armony, (Armony, 2011) 2011) Stochastic models (Irvine, (Marcon, (Irvine, 1994) 1994) (Marcon, 2006). 2006). In queuingmodels models, discrete Markov and semi-Markov Stochastic Stochastic models models, patients are assumed belongand to homogeneous In queuing discrete Markov semi-Markov In queuing models, models, discrete to Markov and semi-Markov groups, and transfer from one state to another. models, patients are assumed to belong to homogeneous models, patients are assumed to belong to homogeneous Patient can befrom modeled as ato groups, and one another. groups, flow and transfer transfer from one state state toqueuing another.network, where patients are the customers and medical bedswhere and Patient flow can be modeled as a queuing network, Patient flow can be modeled as a queuing staff, network, where equipments are the servers. patients are the customers and medical staff, beds patients are the customers and medical staff, beds and and Armony et al. analyze the Fastest Servers First (FSF) equipments are the equipments are(2011) the servers. servers. routing that assigns customers to the fastest available Armony et analyze the Servers First (FSF) Armonypolicy et al. al. (2011) (2011) analyze the Fastest Fastest Servers First (FSF) pool. It policy is beenthat shown that customers FSF is asymptotically in routing assigns to available routing policy that assigns customers to the the fastest fastestoptimal, available pool. It is been shown that FSF is asymptotically optimal, pool. It is been shown that FSF is asymptotically optimal, in in
Copyright © 2015 IFAC 743 2405-8963 © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright © 2015 IFAC 743 Copyright 2015 responsibility IFAC 743Control. Peer review©under of International Federation of Automatic 10.1016/j.ifacol.2015.06.166
INCOM 2015 May 11-13, 2015. Ottawa, Canada
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
the sense that it stochastically minimizes the stationary queue length and waiting time, as the arrival rate and number of servers grow large. Markov model assumes a probabilistic behavior of patients moving around the system and therefore gives a realistic representation of the actual system. Irvine, et al. (1994) describe the development of a continuous time stochastic model of patient flow. Essentially, it is a twostage continuous-time Markov model that describes the movement of patients through geriatric hospitals. The behavior in the model can be regarded as states and the probabilities of patients moving within those states can be calculated. Patients are initially admitted to the hospital in the acute state from which they transfer to the long-stay state or leave the hospital completely through discharge or death state. Discrete event simulation Discrete event simulation describes the activities of individuals as time progresses. Events can occur at any time and are not limited to particular time intervals. In a hospital simulation, the individuals are usually patients who progress from one ward or hospital facility to another. Marcon and Dexter (2006), for instance, use discrete-event simulation to examine how standard sequencing rules, such as longest case first or shortest case first, may assist in reducing the peak number of patients in both the holding area and the post anesthesia care unit.
711
1. Usually, the knowledge is spatially distributed in different locations. 2. The coordination between different individuals with several skills and functions is frequently required. 3. It is difficult to find standard software engineering solutions for health care problems. Applications in health care The multi-agent paradigm has been proposed to deal with many different kinds of problems in the health care domain (Müller et al., 2014): patient scheduling (Paulussen, 2006), organ and tissue transplant management (Moreno, 2001), community care (M. Beer, 2003), resource allocation (Hosseini et al., 2014), and decision support system (Heine, 2003) (Kirn, 2003). Beside those different application’s sectors, these approaches are linked by a common objective, which is also ours: create cooperation between agents in systems where decisions and information are highly distributed. Among the several coordination/negotiation techniques existing techniques, we can firstly note a decentralized auction mechanism in which each agent makes a bids based on its utility function to gain a specific time slot. Then, a novel health-state dependent utility function is introduced, with which patient-agent generate bids for the time slot auctions at the resource-agents (Paulussen, 2006). Secondly, coordination could be achieved through agents that keep track of personnel schedules and the availability of the facilities (both described as time-tables divided into slots of thirty minutes) (Moreno, 2001). And lastly, using a specific agent as coordinator could be considered, like in the INCA (Integrated Community Care) system (M. Beer, 2003) where the coordinator is responsible for preparing the plan and different care providers’ supply various services. Another research way is based on open framework design allowing the hospital legacy system considering and new behaviors or innovative solutions application simulating. Agent Hospital (Kirn, 2003) is such an open agent-based framework for highly distributed health care systems. As an application of this framework, the ADAPT project (Heine, 2003) tried to optimize the processes as well as the information flow. As a result of this, resource allocation, time scheduling and tactical planning should improve with respect to efficiency and control. The main motives and goals of this research are: improvement of distributed appointment scheduling, support for decisions about participation in clinical trials and operationalization of study protocols. The works mentioned above are domain specific, e.g. patient scheduling, organ and tissue transplant management, decision support system, etc. These systems and approaches are therefore not capable of handling the problem as presented below. Almost all systems and approaches, as far as we know, treat specific departments (emergency department (Daknou, 2010), oncology department (Moreno, 2001), etc.) or specific pathway. More precisely, the efficacy of agents in that specific case has still not been explored. We thereby describe the problem in the next section.
1.2 Multi-agent systems applied in Health Care Multi-agent paradigm has been used to develop complex systems in various fields. Therefore, multi-agent paradigm can be considered as an effective approach to design and build a health care system. The purpose of this literature review is to explore the use of intelligent multi-agent approach for developing such a system. Isern et al. (Isern, 2010) reviewed several agent-based solutions for health care are concluded that such an approach has positive affects in terms of modularity, efficiency, decentralization, flexibility, personalization, distributed planning, monitoring, pro activity and security. Intelligent agent For all we know, there is still no unified definition about Agent. An agent can be defined as a software system that is situated in an environment, which it can perceive, and that is capable of autonomous action in this environment to meet its design objectives (Ferber, 1999). Other features of an agent are its sociability and pro activity. In fact, agents are able to run without the direct intervention of person or other agent, communicate with each other in the form of negotiations and coordinations, and perform beneficial tasks to users. They can take initiatives and perform actions proactively that may help them to reach their goals. Multi-agent system A MAS is a distributed autonomous system composed of multiple agents able to complete their tasks. In the system, each agent is an autonomous computing entity which has objective(s), knowledge and abilities. The agents work together to solve problems. Therefore, multi-agent approach can be considered as an effective approach to design and implement health care systems, for the reasons set out below:
3. PROBLEM DESCRIPTION Nowadays, there are several continuing challenges in health care field. On one hand, there is a growing demand for a high 744
INCOM 2015 May 11-13, 2015. Ottawa, Canada
712
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
quality and efficient health care services provision. On the other hand, the cost of health care services has become important. In this regard, in France, Haute Autorité de Santé (HAS “High Health Authority”) requires certifications from hospitals to evaluate their functioning such as V2012 and V2014 accreditations. Our collaborator CH de Sarreguemines – Robert Pax hospital obtained from HAS the V2010 accreditation in October 2013. The aim is to be certified with V2014 accreditation. Five strategic guidelines were defined for this accreditation: 1. reinforce the capacity to manage risk. 2. Strengthen the impact on the management of the institutions 3. Continually improve the institution’s quality. 4. Develop patient-centered approaches. 5. Recognize the value of institutions. Our focus is on the fourth guideline. By improving patient flow, hospital can save money on staffing, decrease wait times and boost patient and provider satisfaction. We propose a patientcentered architecture based on a multi-agent paradigm able to manage and control the patient flow in order to increase the effectiveness. The aim of this labor is to model a system able to support both the medical staff throughout the health care process and the hospital managers by providing management staff dynamic decision-making scenarios with more optimized distribution of resources. The optimized distribution of resources emerges from the interaction between agents. Based on a detailed analysis of discussions with Robert Pax, the hospital’s director of care and the health care managers, we identified their processes and validate established models as work has progressed.
In order to model the patient flow, realistic representation of the patient flow is required. Based on detailed analysis and discussions with Robert Pax hospital’s director of care and health care managers, we identified their processes and patient flow and validate established models as work has progressed. The hospital flow is detailed in Figure 1.
4. SYSTEM DESCRIPTION Figure 1: Hospital diagram flow
Management and control of patient flow is a complex problem where coordination of many hospital personnel working in different areas is required. The properties of intelligent agents, including autonomy, pro-activity, social capacity and multi-agent systems’ characteristics, such as distribution, communication, coordination and negotiation seem appropriate to model the described system. To provide adequate decision support, we propose a centeredpatient multi-agent system based on eight types of agents: the “Patient Agent”, “Physician Agent”, “Nurse Agent”, “Secretary Agent”, “Stretcher Agent”, “Imaging Agent”, “Laboratory Agent”, and “Health Manager Agent”. Before presenting the architecture of the proposed system, it is crucial to stress the difference between the “Patient Agent”, which is a cognitive agent and the patient itself, who is the recipient of health care. Indeed, the “Patient Agent” is a software program which represents the physical patient, its beliefs, goals and behaviors. The patient is not involved in the management of its own pathway. Nevertheless the “Patient Agent” manages and controls the pathway of the associated patient in collaboration with the other agents.
The graph below (figure 2) illustrates the architecture of the centered-patient multi-agent system that has been designed on the basis of the diagram flow exposed above. In this figure, “Patient Agents” are represented by circles. The eleven large boxes represent the shared resources between patients, which belong to the same hierarchy level. We represent with parallel squares the fact that some resources exist in different instances. The dotted line between the “Patient Agents” and the resource agents indicates the information flow, while the physical flow (i.e. everything which is physic except the patients themselves: medical sample, meals, etc.) is represented by the solid line. It must be emphasized that we don't treat the pharmaceutical flow which is widely treated in the literature. We can see from this graph that the proposed system is heterarchical. Two levels are distinguished. The upper level, which is composed of “Patient Agents” and the lower level, which is composed of share resources agents. We have adopted heterarchical architecture for the following reasons: 1. According to the activity to be coordinated, the level of control can change. 2. The communication can be achieved by cooperation, coordination and negotiation. 3. New entities can be introduced or removed without significant structural changes. The main idea of our reflection is that the “Patient Agent” manages its own care path, according to the doctor's recommendations obviously. In other words, the “Patient
4.1. Architecture of the proposed system To preserve relative simplicity and flexibility, we have chosen an agent-based modular and heterarchical architecture by placing around a central agent, “Patient Agent” (which represent the physical patient), seven peripheral agents (which represent the shared hospital resources). 745
INCOM 2015 May 11-13, 2015. Ottawa, Canada
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
Agent” schedules its own needed medical services, such as, treatments, examinations or surgeries. For this propose,
713
“Patient Agent” interact with resource agents to reserve slots.
Figure 2: Centered-Patient Multi-Agent System simplified architecture
There may, however, be cases where “Patient Agents” or even resource agents are in conflict. To remedy this situation, we propose a negotiation process. Two conflicts were differenced: Conflicts between resource agents and conflicts between “Patient Agents” and resource agents. “Patient Agents” negotiate with the resource agents to reach a consensus about the shared resources and overcome the allocation conflicts. If the horizontal negotiation failed, we propose a higher level of negotiation. Indeed, “Patient Agents” lunch a negotiation process with other(s) “Patient Agent(s)”. Figure 3 illustrates the interactions between resource agents in consultation service. Resource agents interact in order to overcome the allocation conflicts and satisfy the whole agent’s preferences. Assuming that each consultation service is composed from nm multipurpose rooms and nd dedicated rooms. Multi-purpose rooms can be occupied by n doctors who are assisted by nn nurses. However, careful consultations are programmed in dedicated rooms where advance practice nurses manipulate delicate instruments. As illustrated in the figure, rooms, nurses or doctors (i.e. their representing agents in the system, not the real persons themselves) interact with one another to reach a desired scheduling. For instance, if a doctor has to change his schedule for any reason, he can communicate with other doctors to exchange their time slot. This shall be also applied to solve the conflicts of interest between nurses. Thus, the scheduling is adjusted locally according to the preferences of the agent resources.
We will now provide a brief description of each of the agents of the system.
Figure 3: Interaction between resource agents (consultation services)
Patient Agent The “Patient Agent” is the central agent of the system. This agent requests for needed services from the other agents (appointment request, consulting request, etc.). It interacts and coordinates with the other “Patient Agents” to reach an
4.2. Agents’ behaviors
746
INCOM 2015 May 11-13, 2015. Ottawa, Canada
714
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
agreement according to their constraints and preferences. It is then a mixed agent. Physician Agent The “Physician Agent” provides an intuitive Human-Machine Interface (HMI) to the physical physician. This agent expresses its availabilities through an HMI. It transmits also its decisions/propositions via the HMI. The physician agent is reactive. The Physician Agent is the meta-agent of the whole patricians and physician in hospital. Such as: Doctor Agent, Surgeon Agent, Radiologist Agent and Anesthetist Agent. Nurse Agent The “Nurse Agent” fulfills the availabilities data board by using HMI. It is a reactive agent. Secretary Agent The main functions of this reactive agent are to create patient record, follow up billings and manage appointments. Stretcher Agent The task of the “Stretcher Agent” is to planning the daily stretcher's routes to ensure patient's transport into the hospital. The agent's cognition lies in elaborate routing algorithms. Imaging Agent This agent is responsible to make a planning for the imaging tests. It should likewise transfer the result to the concerned agent. The “Imaging Agent” is a cognitive agent because it treats two main problems; queuing problems and resources allocation problems. Laboratory Agent The behavior of this agent is similar to the previous agent. Health manager Agent The role of this agent is to maintain the planning and manage hazards. It can also create new agents in need.
motivated by our study in collaboration with centre hospitalier de Sarreguemines-hôpital Robert Pax. REFERENCES Armony, M., Israelit, S., Mandelbaum, A., Marmor, Y. N., Tseytlin, Y., & Yom-Tov, G. B. (2011). Patient flow in hospitals: A data-based queueing-science perspective. Submitted to Stochastic Systems, 20. Beer, M., Huang, W., & Hill, R. (2003). Designing community care systems with AUML. Mexico: ehealth - Application of Computing Science in Medicine and Health Care. Daknou, A., Zgaya, H., Hammadi, S., & Hubert, H. (2010, February). A dynamic patient scheduling at the emergency department in hospitals. In Health Care Management (WHCM), 2010 IEEE Workshop on (pp. 1-6). IEEE. Ferber, J. (1999). Multi-agent systems: an introduction to distributed artificial intelligence (Vol. 1). Reading: Addison-Wesley. Heine, C., Herrler, R., Petsch, M., & Anhalt, C. (2003). ADAPT: Adaptive Multi-Agent Process Planning and Coordination of Clinical Trials. AMCIS 2003 Proceedings, 235. Hosseini, H., Hoey, J., & Cohen, R. (2014). A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare. arXiv preprint arXiv:1407.1584. IRVINE, V., MCCLEAN, S., & MILLARD, P. (1994). Stochastic models for geriatric in-patient behaviour. Mathematical Medicine and Biology, 11(3), 207-216. Isern, D., Sánchez, D., & Moreno, A. (2010). Agents applied in health care: A review. International journal of medical informatics, 79(3), 145-166. Kirn, S., Herrler, R., Heine, C., & Krempels, K. H. (2003, June). Agent. Hospital-agent-based open framework for clinical applications. In Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003. WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on (pp. 36-41). IEEE. Marcon, E., & Dexter, F. (2006). Impact of surgical sequencing on post anesthesia care unit staffing. Health Care Management Science, 9(1), 87-98. Moreno, A., Valls, A., & Bocio, J. (2001). Management of hospital teams for organ transplants using multiagent systems. In Artificial Intelligence in Medicine(pp. 374-383). Springer Berlin Heidelberg. Müller, J. P., & Fischer, K. (2014, January). Application Impact of Multi-Agent Systems and Technologies: A Survey. In Agent-Oriented Software Engineering(pp. 27-53). Springer Berlin Heidelberg. Paulussen, T. O., Zöller, A., Rothlauf, F., Heinzl, A., Braubach, L., Pokahr, A., & Lamersdorf, W. (2006). Agent-based patient scheduling in hospitals (pp. 255-275). Springer Berlin Heidelberg.
5. CONCLUSIONS A patient-centered multi agent system dedicated to manage and control the patient flow in medical care facilities was presented in this paper. This approach models the patients as well as the hospital resources as autonomous agents. We described in this paper a heterarchical architecture with two levels. The patients form the higher one and the different shared resources the lower one. The main purpose of this architecture is to make the (agent) patient the main important actor in its own path in healthcare system. Then, the “Patient Agent” can negotiate in two ways to solve schedule problems it could encounter along his medical path. The originality of our model consists on a patient centered view of the problem and on the two level of negotiation proposed to ensure that all agents (patients and shared resources) are satisfied of the proposed allocation. Future work will focus on the negotiation mechanism and the implementation of the patient-centered multi agent system. Our ultimate objective is the deployment of the system into Robert Pax hospital. ACKNOWLEDGEMENTS This work has been supported by the urban community of Sarreguemines-France and the region of Lorraine-France and 747
ScienceDirect IFAC-PapersOnLine 48-3 (2015) 710–714
Patient-centered multi agent system for health care Patient-centered Patient-centered multi multi agent agent system system for for health health care care N. Benhajji* D. Roy** D. Anciaux*** N. Benhajji* D. N. Benhajji* D. Roy** Roy** D. D. Anciaux*** Anciaux*** * Ecole Nationale d’Ingénieurs de Metz, 1 route d'Ars Laquenexy, CS 65820, 57078 Metz, France (Tel: +33(0)6de 52Metz, 36 7311 78; e-mail: * route d'Ars Laquenexy, * Ecole Ecole Nationale Nationale d’Ingénieurs d’Ingénieurs de Metz, route d'Ars [email protected]) Laquenexy, CS CS 65820, 65820, 57078 57078 Metz, Metz, France France ** Ecole Nationale des de 36 Metz, 1 route d'Ars Laquenexy, CS 65820, 57078 Metz, France (Tel: +33(0)6 73 e-mail: [email protected]) (Tel:Ingénieurs +33(0)6 52 52 36 73 78; 78; e-mail: [email protected]) (Tel: +33(0)3 87 79 68 26; e-mail: [email protected]) ** Ecole Ecole Nationale Nationale des des Ingénieurs Ingénieurs de Metz, Metz, route d'Ars Laquenexy, CS 65820, 65820, 57078 57078 Metz, Metz, France France ** de 11 route d'Ars Laquenexy, CS *** Université de Lorraine – UFR-MIM, île de saulcy, 57045 Metz, France (Tel: +33(0)3 87 79 68 26; e-mail: (Tel: +33(0)3 87 79 68 26; e-mail: [email protected]) [email protected]) : +33 (0)3 87 31 54 ––52UFR-MIM, e-mail: [email protected]) *** Université de île ***(Tel Université de Lorraine Lorraine UFR-MIM, île de de saulcy, saulcy, 57045 57045 Metz, Metz, France France (Tel : +33 (0)3 87 31 54 52 e-mail: [email protected]) (Tel : +33 (0)3 87 31 54 52 e-mail: [email protected]) Abstract: The increasing demand for health care led healthcare organizations to efficiently and effectively their process. Indeed, hospitals are led confronted to budget restrictions certification Abstract: The demand for care healthcare organizations to efficiently and Abstract: organize The increasing increasing demand for health health care led healthcare organizations to and efficiently and obligations. To this end, planning and control in health care has received an increasing amount of effectively organize their process. Indeed, hospitals are confronted to budget restrictions and certification effectively organize their process. Indeed, hospitals are confronted to budget restrictions and certification attention over the last ten years, both in practice and literature. The challenge is twofold: on one hand, obligations. To this end, planning and control in health care has received an increasing amount obligations. To this end, planning and control in health care has received an increasing amount of of healthcareover institutions to provide with theliterature. best possible On the hand, to attention the ten both in and The challenge is twofold: on one hand, attention over the last last have ten years, years, both patients in practice practice and literature. The care. challenge is other twofold: onthey oneaim hand, balance workload and have maximize resource utilization. The present paper presents patient-centered multi healthcare institutions to patients with best possible care. On hand, to healthcare institutions have to provide provide patients with the the best possible care. On the theaother other hand, they they aim aim to agents system for and healthcare to plan and control the patient flow.paper The proposition is a decision support balance workload maximize resource utilization. The presents multi balance workload and maximize resource utilization. The present present paper presents aa patient-centered patient-centered multi system for healthcare managers to improve their functioning and manage unpredictable hazards and agents system for healthcare to plan and control the patient flow. The proposition is support agents system for healthcare to plan and control the patient flow. The proposition is aa decision decision support disruptions. system for healthcare managers to improve their functioning and manage unpredictable hazards and system for healthcare managers to improve their functioning and manage unpredictable hazards and disruptions. disruptions. © 2015, IFAC (International of patient Automatic Control) Hostingsystem, by Elsevier Ltd. All rights reserved. Keywords: hospital logistics,Federation healthcare, flow, multi agents patient-centered. Keywords: hospital logistics, healthcare, patient flow, multi agents system, patient-centered. Keywords: hospital logistics, healthcare, patient flow, multi agents system, patient-centered. 1. INTRODUCTION 1. 1. INTRODUCTION INTRODUCTION Hospital is a complex system formed by a large number of autonomous wards and with strong Hospital system formed aa large number of Hospital is is aa complex complex systemancillary formed by byunits large number of interrelationship. The complications involve the relationship autonomous wards and ancillary units with strong autonomous wards and ancillary units with strong between patient,The facility, nursing involve staff, the providers, the interrelationship. complications relationship interrelationship. The complications involve the relationship complexity of diseases, treatment options, medications, etc. between patient, facility, nursing staff, providers, between patient, facility, nursing staff, providers, the the The emphasis should betreatment given tooptions, promoting the triptych complexity of medications, etc. complexity of diseases, diseases, treatment options, medications, etc. (cost, quality, time). Thebe is toto the best compromise The should given promoting the The emphasis emphasis should begoal given tofind promoting the triptych triptych between these time). three criteria. a patient(cost, The is the best (cost, quality, quality, time). The goal goalTherefore, is to to find findwe thepropose best compromise compromise centered multi-agent systemTherefore, for healthwe care to achieve the between these these three criteria. criteria. Therefore, we propose patientbetween three propose aa patientgoal outlined above. It is a decision support system to help centered multi-agent system for health care to achieve centered multi-agent system for health care to achieve the the both medical staffIt the health process and goal outlined above. is support system to goal the outlined above. Itthroughout is aa decision decision supportcare system to help help the providingthe dynamic decision-making both the staff throughout health process bothmanagement the medical medical staff staff by throughout the health care care process and and Model scenarios with a more optimized resource distribution. the management staff by providing dynamic decision-making the management staff by providing dynamic decision-making this complex turn out to beresource necessary. ModelingModel helps scenarios with aa more optimized distribution. scenarios withreality more optimized resource distribution. Model to analyze, understand the functioning of the investigated this complex reality turn out to be necessary. Modeling this complex reality turn out to be necessary. Modeling helps helps system, giveunderstand a true and fair of it and areas in to the functioning of the to analyze, analyze, understand the view functioning of pinpoint the investigated investigated which is required to develop it. in system,further give aaknowledge true and and fair fair view of ofinit it order and pinpoint pinpoint areas in system, give true view and areas We used both industrial practices and artificial intelligence which further knowledge is required in order to develop which further knowledge is required in order to develop it. it. techniques to industrial model our system, as intelligence multi-agent We practices and artificial We used used both both industrial practices and such artificial intelligence paradigm. Indeed, the multi-agent paradigm enhances the techniques to model our system, such as techniques to model our system, such as multi-agent multi-agent ability to model, design and implement complex, inherently paradigm. Indeed, the multi-agent paradigm enhances paradigm. Indeed, the multi-agent paradigm enhances the the distributed, and highly adaptable and cooperative ability design and complex, inherently ability to to model, model, design and implement implement complex,systems. inherently Our reflection basedadaptable on the fact patient satisfaction distributed, and and cooperative systems. distributed, andishighly highly adaptable andthat cooperative systems. is increasingly important goals set by health care Our is the that patient satisfaction is Our reflection reflection is based based toon onachieve the fact factthe that patient satisfaction is organizations. Thus, theto increasingly achieve the set by care increasingly important important tochanges achieve occurring the goals goals in sethospitals by health healthtoday care require a change in patient-centered management. organizations. Thus, the in organizations. Thus, the changes changes occurring occurring in hospitals hospitals today today require a change in patient-centered management. require a change in patient-centered management.
The remainder of this paper is structured as follows: Section two Agents, they are and how Section we can The remainder of paper structured as The reviews remainder of this thisincluding paper is is what structured as follows: follows: Section use them in health care. Based upon this, a patient-centered two reviews Agents, including what they are and how two reviews Agents, including what they are and how we we can can Multi-Agent System (MAS) is developed in the third section. use them in health care. Based upon this, a patient-centered use them in health care. Based upon this, a patient-centered This paper closes with conclusions and an future Multi-Agent System (MAS) is in the Multi-Agent System (MAS) is developed developed in outlook the third thirdtosection. section. works in thecloses fourthwith section. This paper paper closes with conclusions and and an an outlook outlook to to future future This conclusions works works in in the the fourth fourth section. section. 2. RELATED WORKS 2. 2. RELATED RELATED WORKS WORKS 1.1 Patient flow 1.1 1.1 Patient Patient flow flow The patient flow in hospital can be assimilated to the production must be incan the be bestassimilated conditions of The patient patientprocess flow that in to the The flow in hospital hospital can be assimilated tocost, the quality and safety. Thus, patient flow models are very useful. production process that must be in the best conditions of production process that must be in the best conditions of cost, cost, Indeed, changing aboutflow patient careare and treatment quality safety. Thus, models very useful. quality and and safety.decisions Thus, patient patient flow models are very useful. impacts resource use and costs. These effects can be Indeed, changing decisions about patient care and treatment Indeed, changing decisions about patient care and treatment explored.Modeling techniques include Markov, semi-Markov impacts resource use and costs. These effects can impacts resource use and costs. These effects can be be and stochastic simulation (Armony, 2011) explored.Modeling techniques include semi-Markov explored.Modeling techniques approaches include Markov, Markov, semi-Markov (Irvine, 1994) (Marcon, 2006).approaches and simulation and stochastic stochastic simulation approaches (Armony, (Armony, 2011) 2011) Stochastic models (Irvine, (Marcon, (Irvine, 1994) 1994) (Marcon, 2006). 2006). In queuingmodels models, discrete Markov and semi-Markov Stochastic Stochastic models models, patients are assumed belongand to homogeneous In queuing discrete Markov semi-Markov In queuing models, models, discrete to Markov and semi-Markov groups, and transfer from one state to another. models, patients are assumed to belong to homogeneous models, patients are assumed to belong to homogeneous Patient can befrom modeled as ato groups, and one another. groups, flow and transfer transfer from one state state toqueuing another.network, where patients are the customers and medical bedswhere and Patient flow can be modeled as a queuing network, Patient flow can be modeled as a queuing staff, network, where equipments are the servers. patients are the customers and medical staff, beds patients are the customers and medical staff, beds and and Armony et al. analyze the Fastest Servers First (FSF) equipments are the equipments are(2011) the servers. servers. routing that assigns customers to the fastest available Armony et analyze the Servers First (FSF) Armonypolicy et al. al. (2011) (2011) analyze the Fastest Fastest Servers First (FSF) pool. It policy is beenthat shown that customers FSF is asymptotically in routing assigns to available routing policy that assigns customers to the the fastest fastestoptimal, available pool. It is been shown that FSF is asymptotically optimal, pool. It is been shown that FSF is asymptotically optimal, in in
Copyright © 2015 IFAC 743 2405-8963 © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright © 2015 IFAC 743 Copyright 2015 responsibility IFAC 743Control. Peer review©under of International Federation of Automatic 10.1016/j.ifacol.2015.06.166
INCOM 2015 May 11-13, 2015. Ottawa, Canada
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
the sense that it stochastically minimizes the stationary queue length and waiting time, as the arrival rate and number of servers grow large. Markov model assumes a probabilistic behavior of patients moving around the system and therefore gives a realistic representation of the actual system. Irvine, et al. (1994) describe the development of a continuous time stochastic model of patient flow. Essentially, it is a twostage continuous-time Markov model that describes the movement of patients through geriatric hospitals. The behavior in the model can be regarded as states and the probabilities of patients moving within those states can be calculated. Patients are initially admitted to the hospital in the acute state from which they transfer to the long-stay state or leave the hospital completely through discharge or death state. Discrete event simulation Discrete event simulation describes the activities of individuals as time progresses. Events can occur at any time and are not limited to particular time intervals. In a hospital simulation, the individuals are usually patients who progress from one ward or hospital facility to another. Marcon and Dexter (2006), for instance, use discrete-event simulation to examine how standard sequencing rules, such as longest case first or shortest case first, may assist in reducing the peak number of patients in both the holding area and the post anesthesia care unit.
711
1. Usually, the knowledge is spatially distributed in different locations. 2. The coordination between different individuals with several skills and functions is frequently required. 3. It is difficult to find standard software engineering solutions for health care problems. Applications in health care The multi-agent paradigm has been proposed to deal with many different kinds of problems in the health care domain (Müller et al., 2014): patient scheduling (Paulussen, 2006), organ and tissue transplant management (Moreno, 2001), community care (M. Beer, 2003), resource allocation (Hosseini et al., 2014), and decision support system (Heine, 2003) (Kirn, 2003). Beside those different application’s sectors, these approaches are linked by a common objective, which is also ours: create cooperation between agents in systems where decisions and information are highly distributed. Among the several coordination/negotiation techniques existing techniques, we can firstly note a decentralized auction mechanism in which each agent makes a bids based on its utility function to gain a specific time slot. Then, a novel health-state dependent utility function is introduced, with which patient-agent generate bids for the time slot auctions at the resource-agents (Paulussen, 2006). Secondly, coordination could be achieved through agents that keep track of personnel schedules and the availability of the facilities (both described as time-tables divided into slots of thirty minutes) (Moreno, 2001). And lastly, using a specific agent as coordinator could be considered, like in the INCA (Integrated Community Care) system (M. Beer, 2003) where the coordinator is responsible for preparing the plan and different care providers’ supply various services. Another research way is based on open framework design allowing the hospital legacy system considering and new behaviors or innovative solutions application simulating. Agent Hospital (Kirn, 2003) is such an open agent-based framework for highly distributed health care systems. As an application of this framework, the ADAPT project (Heine, 2003) tried to optimize the processes as well as the information flow. As a result of this, resource allocation, time scheduling and tactical planning should improve with respect to efficiency and control. The main motives and goals of this research are: improvement of distributed appointment scheduling, support for decisions about participation in clinical trials and operationalization of study protocols. The works mentioned above are domain specific, e.g. patient scheduling, organ and tissue transplant management, decision support system, etc. These systems and approaches are therefore not capable of handling the problem as presented below. Almost all systems and approaches, as far as we know, treat specific departments (emergency department (Daknou, 2010), oncology department (Moreno, 2001), etc.) or specific pathway. More precisely, the efficacy of agents in that specific case has still not been explored. We thereby describe the problem in the next section.
1.2 Multi-agent systems applied in Health Care Multi-agent paradigm has been used to develop complex systems in various fields. Therefore, multi-agent paradigm can be considered as an effective approach to design and build a health care system. The purpose of this literature review is to explore the use of intelligent multi-agent approach for developing such a system. Isern et al. (Isern, 2010) reviewed several agent-based solutions for health care are concluded that such an approach has positive affects in terms of modularity, efficiency, decentralization, flexibility, personalization, distributed planning, monitoring, pro activity and security. Intelligent agent For all we know, there is still no unified definition about Agent. An agent can be defined as a software system that is situated in an environment, which it can perceive, and that is capable of autonomous action in this environment to meet its design objectives (Ferber, 1999). Other features of an agent are its sociability and pro activity. In fact, agents are able to run without the direct intervention of person or other agent, communicate with each other in the form of negotiations and coordinations, and perform beneficial tasks to users. They can take initiatives and perform actions proactively that may help them to reach their goals. Multi-agent system A MAS is a distributed autonomous system composed of multiple agents able to complete their tasks. In the system, each agent is an autonomous computing entity which has objective(s), knowledge and abilities. The agents work together to solve problems. Therefore, multi-agent approach can be considered as an effective approach to design and implement health care systems, for the reasons set out below:
3. PROBLEM DESCRIPTION Nowadays, there are several continuing challenges in health care field. On one hand, there is a growing demand for a high 744
INCOM 2015 May 11-13, 2015. Ottawa, Canada
712
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
quality and efficient health care services provision. On the other hand, the cost of health care services has become important. In this regard, in France, Haute Autorité de Santé (HAS “High Health Authority”) requires certifications from hospitals to evaluate their functioning such as V2012 and V2014 accreditations. Our collaborator CH de Sarreguemines – Robert Pax hospital obtained from HAS the V2010 accreditation in October 2013. The aim is to be certified with V2014 accreditation. Five strategic guidelines were defined for this accreditation: 1. reinforce the capacity to manage risk. 2. Strengthen the impact on the management of the institutions 3. Continually improve the institution’s quality. 4. Develop patient-centered approaches. 5. Recognize the value of institutions. Our focus is on the fourth guideline. By improving patient flow, hospital can save money on staffing, decrease wait times and boost patient and provider satisfaction. We propose a patientcentered architecture based on a multi-agent paradigm able to manage and control the patient flow in order to increase the effectiveness. The aim of this labor is to model a system able to support both the medical staff throughout the health care process and the hospital managers by providing management staff dynamic decision-making scenarios with more optimized distribution of resources. The optimized distribution of resources emerges from the interaction between agents. Based on a detailed analysis of discussions with Robert Pax, the hospital’s director of care and the health care managers, we identified their processes and validate established models as work has progressed.
In order to model the patient flow, realistic representation of the patient flow is required. Based on detailed analysis and discussions with Robert Pax hospital’s director of care and health care managers, we identified their processes and patient flow and validate established models as work has progressed. The hospital flow is detailed in Figure 1.
4. SYSTEM DESCRIPTION Figure 1: Hospital diagram flow
Management and control of patient flow is a complex problem where coordination of many hospital personnel working in different areas is required. The properties of intelligent agents, including autonomy, pro-activity, social capacity and multi-agent systems’ characteristics, such as distribution, communication, coordination and negotiation seem appropriate to model the described system. To provide adequate decision support, we propose a centeredpatient multi-agent system based on eight types of agents: the “Patient Agent”, “Physician Agent”, “Nurse Agent”, “Secretary Agent”, “Stretcher Agent”, “Imaging Agent”, “Laboratory Agent”, and “Health Manager Agent”. Before presenting the architecture of the proposed system, it is crucial to stress the difference between the “Patient Agent”, which is a cognitive agent and the patient itself, who is the recipient of health care. Indeed, the “Patient Agent” is a software program which represents the physical patient, its beliefs, goals and behaviors. The patient is not involved in the management of its own pathway. Nevertheless the “Patient Agent” manages and controls the pathway of the associated patient in collaboration with the other agents.
The graph below (figure 2) illustrates the architecture of the centered-patient multi-agent system that has been designed on the basis of the diagram flow exposed above. In this figure, “Patient Agents” are represented by circles. The eleven large boxes represent the shared resources between patients, which belong to the same hierarchy level. We represent with parallel squares the fact that some resources exist in different instances. The dotted line between the “Patient Agents” and the resource agents indicates the information flow, while the physical flow (i.e. everything which is physic except the patients themselves: medical sample, meals, etc.) is represented by the solid line. It must be emphasized that we don't treat the pharmaceutical flow which is widely treated in the literature. We can see from this graph that the proposed system is heterarchical. Two levels are distinguished. The upper level, which is composed of “Patient Agents” and the lower level, which is composed of share resources agents. We have adopted heterarchical architecture for the following reasons: 1. According to the activity to be coordinated, the level of control can change. 2. The communication can be achieved by cooperation, coordination and negotiation. 3. New entities can be introduced or removed without significant structural changes. The main idea of our reflection is that the “Patient Agent” manages its own care path, according to the doctor's recommendations obviously. In other words, the “Patient
4.1. Architecture of the proposed system To preserve relative simplicity and flexibility, we have chosen an agent-based modular and heterarchical architecture by placing around a central agent, “Patient Agent” (which represent the physical patient), seven peripheral agents (which represent the shared hospital resources). 745
INCOM 2015 May 11-13, 2015. Ottawa, Canada
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
Agent” schedules its own needed medical services, such as, treatments, examinations or surgeries. For this propose,
713
“Patient Agent” interact with resource agents to reserve slots.
Figure 2: Centered-Patient Multi-Agent System simplified architecture
There may, however, be cases where “Patient Agents” or even resource agents are in conflict. To remedy this situation, we propose a negotiation process. Two conflicts were differenced: Conflicts between resource agents and conflicts between “Patient Agents” and resource agents. “Patient Agents” negotiate with the resource agents to reach a consensus about the shared resources and overcome the allocation conflicts. If the horizontal negotiation failed, we propose a higher level of negotiation. Indeed, “Patient Agents” lunch a negotiation process with other(s) “Patient Agent(s)”. Figure 3 illustrates the interactions between resource agents in consultation service. Resource agents interact in order to overcome the allocation conflicts and satisfy the whole agent’s preferences. Assuming that each consultation service is composed from nm multipurpose rooms and nd dedicated rooms. Multi-purpose rooms can be occupied by n doctors who are assisted by nn nurses. However, careful consultations are programmed in dedicated rooms where advance practice nurses manipulate delicate instruments. As illustrated in the figure, rooms, nurses or doctors (i.e. their representing agents in the system, not the real persons themselves) interact with one another to reach a desired scheduling. For instance, if a doctor has to change his schedule for any reason, he can communicate with other doctors to exchange their time slot. This shall be also applied to solve the conflicts of interest between nurses. Thus, the scheduling is adjusted locally according to the preferences of the agent resources.
We will now provide a brief description of each of the agents of the system.
Figure 3: Interaction between resource agents (consultation services)
Patient Agent The “Patient Agent” is the central agent of the system. This agent requests for needed services from the other agents (appointment request, consulting request, etc.). It interacts and coordinates with the other “Patient Agents” to reach an
4.2. Agents’ behaviors
746
INCOM 2015 May 11-13, 2015. Ottawa, Canada
714
N. Benhajji et al. / IFAC-PapersOnLine 48-3 (2015) 710–714
agreement according to their constraints and preferences. It is then a mixed agent. Physician Agent The “Physician Agent” provides an intuitive Human-Machine Interface (HMI) to the physical physician. This agent expresses its availabilities through an HMI. It transmits also its decisions/propositions via the HMI. The physician agent is reactive. The Physician Agent is the meta-agent of the whole patricians and physician in hospital. Such as: Doctor Agent, Surgeon Agent, Radiologist Agent and Anesthetist Agent. Nurse Agent The “Nurse Agent” fulfills the availabilities data board by using HMI. It is a reactive agent. Secretary Agent The main functions of this reactive agent are to create patient record, follow up billings and manage appointments. Stretcher Agent The task of the “Stretcher Agent” is to planning the daily stretcher's routes to ensure patient's transport into the hospital. The agent's cognition lies in elaborate routing algorithms. Imaging Agent This agent is responsible to make a planning for the imaging tests. It should likewise transfer the result to the concerned agent. The “Imaging Agent” is a cognitive agent because it treats two main problems; queuing problems and resources allocation problems. Laboratory Agent The behavior of this agent is similar to the previous agent. Health manager Agent The role of this agent is to maintain the planning and manage hazards. It can also create new agents in need.
motivated by our study in collaboration with centre hospitalier de Sarreguemines-hôpital Robert Pax. REFERENCES Armony, M., Israelit, S., Mandelbaum, A., Marmor, Y. N., Tseytlin, Y., & Yom-Tov, G. B. (2011). Patient flow in hospitals: A data-based queueing-science perspective. Submitted to Stochastic Systems, 20. Beer, M., Huang, W., & Hill, R. (2003). Designing community care systems with AUML. Mexico: ehealth - Application of Computing Science in Medicine and Health Care. Daknou, A., Zgaya, H., Hammadi, S., & Hubert, H. (2010, February). A dynamic patient scheduling at the emergency department in hospitals. In Health Care Management (WHCM), 2010 IEEE Workshop on (pp. 1-6). IEEE. Ferber, J. (1999). Multi-agent systems: an introduction to distributed artificial intelligence (Vol. 1). Reading: Addison-Wesley. Heine, C., Herrler, R., Petsch, M., & Anhalt, C. (2003). ADAPT: Adaptive Multi-Agent Process Planning and Coordination of Clinical Trials. AMCIS 2003 Proceedings, 235. Hosseini, H., Hoey, J., & Cohen, R. (2014). A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare. arXiv preprint arXiv:1407.1584. IRVINE, V., MCCLEAN, S., & MILLARD, P. (1994). Stochastic models for geriatric in-patient behaviour. Mathematical Medicine and Biology, 11(3), 207-216. Isern, D., Sánchez, D., & Moreno, A. (2010). Agents applied in health care: A review. International journal of medical informatics, 79(3), 145-166. Kirn, S., Herrler, R., Heine, C., & Krempels, K. H. (2003, June). Agent. Hospital-agent-based open framework for clinical applications. In Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003. WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on (pp. 36-41). IEEE. Marcon, E., & Dexter, F. (2006). Impact of surgical sequencing on post anesthesia care unit staffing. Health Care Management Science, 9(1), 87-98. Moreno, A., Valls, A., & Bocio, J. (2001). Management of hospital teams for organ transplants using multiagent systems. In Artificial Intelligence in Medicine(pp. 374-383). Springer Berlin Heidelberg. Müller, J. P., & Fischer, K. (2014, January). Application Impact of Multi-Agent Systems and Technologies: A Survey. In Agent-Oriented Software Engineering(pp. 27-53). Springer Berlin Heidelberg. Paulussen, T. O., Zöller, A., Rothlauf, F., Heinzl, A., Braubach, L., Pokahr, A., & Lamersdorf, W. (2006). Agent-based patient scheduling in hospitals (pp. 255-275). Springer Berlin Heidelberg.
5. CONCLUSIONS A patient-centered multi agent system dedicated to manage and control the patient flow in medical care facilities was presented in this paper. This approach models the patients as well as the hospital resources as autonomous agents. We described in this paper a heterarchical architecture with two levels. The patients form the higher one and the different shared resources the lower one. The main purpose of this architecture is to make the (agent) patient the main important actor in its own path in healthcare system. Then, the “Patient Agent” can negotiate in two ways to solve schedule problems it could encounter along his medical path. The originality of our model consists on a patient centered view of the problem and on the two level of negotiation proposed to ensure that all agents (patients and shared resources) are satisfied of the proposed allocation. Future work will focus on the negotiation mechanism and the implementation of the patient-centered multi agent system. Our ultimate objective is the deployment of the system into Robert Pax hospital. ACKNOWLEDGEMENTS This work has been supported by the urban community of Sarreguemines-France and the region of Lorraine-France and 747