Multi-Agent Based Information Systems For Patient Coordination in Hospitals

The health sector is a central domain in every economy. It is challenged by progressing costs and funding issues. Hospitals play a major role for the examination and treatment of patients. The sequence how patients are assigned to hospital units determines the quality of treatment, the resource utilization, as well as the patients’ overall treatment time. Thus, efficient scheduling of patients in hospitals is crucial. Current approaches disregard the decentral organization in hospitals and neglect the varying pathway of patients since they often focus on one single unit solely. We propose an agent-based coordination mechanism that overcomes these limitations. Patients and hospital resources are modeled as autonomous software agents which follow their own objectives. This reflects the decentralized structure in hospitals. Agents are coordinated by a distributed mechanism where software agents improve their situation through negotiations which moves towards an overall pareto-optimum. We show promising evaluations based on experiments

Zeitplanung für Patientenpfade unter Berücksichtigung von Betten-, Behandlungskapazitäten und Fairnesskriterien

The costs of patient care reached a new height. Poor management of patient flows in hospitals lead to unnecessary waiting time, a low degree of capacity utilization and expensive needless treatments. In the beginning of this paper a shortly overview of health care optimization research is shown, which leads to the implementation of interdisciplinary clinical pathways to improve the patient flow. Based on this the structure of scheduling focused clinical pathways is described. After that, a mixed integer linear programming model is shown, which is able to schedule these pathways. In the end the model is verified by an instance of a clinical pathway

Improving hospital bed utilisation through simulation and optimisation in South African Public Hospitals

South African public hospitals have a shortage of beds and struggle to allocate patients to beds and keep track thereof. This causes inefficient utilisation of limited bed capacity. This report addresses this problem by first testing individual bed allocation rules using an agent based simulation to establish what the influence of individual allocation rules are on the system. The results of the rule based models are then used to create three rule sets that are tested using agent based simulation modelling. The results are compared to the current system used within Mamelodi Hospital. All rule sets perform better than the current system. By simply tracking the available beds an additional 20% capacity utilisation is achieved.Thesis (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2012

15. Interuniversitäres Doktorandenseminar Wirtschaftsinformatik der Universitäten Chemnitz, Dresden, Freiberg, Halle-Wittenberg, Jena und Leipzig

Das zum dreizehnten Male durchgeführte interuniversitäre Doktorandenseminar der Universitäten Chemnitz, Dresden, Freiberg, Halle-Wittenberg, Jena und Leipzig repräsentiert eine Kooperation mehrerer Wirtschaftsinformatik-Professoren. Es hat sich als Forum des fruchtbaren Austausches zu Forschungsthemen etabliert, die gemäß der Brückenfünktion der Wirtschaftsinformatik ein breites Spektrum zwischen Betriebswirtschaft und Technik aufspannen.:1. Model Driven Logistics Integration Engineering 2. Using Semantic Web Technologies for Classification Analysis in Social Networks 3. RealTime and Anytime Supply Chain Planning 4. Zeitplanung für Patientenpfade unter Berücksichtigung von Betten-, Behandlungskapazitäten und Fairnesskriterien 5. Automatic Editing Rights Management in Wikipedia 6. Konzeption eines Auswahlverfahrens zur Datenanalyse im Einzelhandel am Beispiel einer Einkaufsverhaltensanalyse im Lebensmitteleinzelhandel 7. Generating Graphical User Interfaces for Software Product Lines: A Constraint-based Approac

A Computational Approach to Patient Flow Logistics in Hospitals

Scheduling decisions in hospitals are often taken in a decentralized way. This means that different specialized hospital units decide autonomously on e.g. patient admissions and schedules of shared resources. Decision support in such a setting requires methods and techniques that are different from the majority of existing literature in which centralized models are assumed. The design and analysis of such methods and techniques is the focus of this thesis. Specifically, we develop computational models to provide dynamic decision support for hospital resource management, the prediction of future resource occupancy and the application thereof. Hospital resource management targets the efficient deployment of resources like operating rooms and beds. Allocating resources to hospital units is a major managerial issue as the relationship between resources, utilization and patient flow of different patient groups is complex. The issues are further complicated by the fact that patient arrivals are dynamic and treatment processes are stochastic. Our approach to providing decision support combines techniques from multi-agent systems and computational intelligence (CI). This combination of techniques allows to properly consider the dynamics of the problem while reflecting the distributed decision making practice in hospitals. Multi-agent techniques are used to model multiple hospital care units and their decision policies, multiple patient groups with stochastic treatment processes and uncertain resource availability due to overlapping patient treatment processes. The agent-based model closely resembles the real-world situation. Optimization and learning techniques from CI allow for designing and evaluating improved (adaptive) decision policies for the agent-based model, which can then be implemented easily in hospital practice. In order to gain insight into the functioning of this complex and dynamic problem setting, we developed an agent-based model for the hospital care units with their patients. To assess the applicability of this agent-based model, we developed an extensive simulation. Several experiments demonstrate the functionality of the simulation and show that it is an accurate representation of the real world. The simulation is used to study decision support in resource management and patient admission control. To further improve the quality of decision support, we study the prediction of future hospital resource usage. Using prediction, the future impact of taking a certain decision can be taken into account. In the problem setting at hand for instance, predicting the resource utilization resulting from an admission decision is important to prevent future bottlenecks that may cause the blocking of patient flow and increase patient waiting times. The methods we investigate for the task of prediction are forward simulation and supervised learning using neural networks. In an extensive analysis we study the underlying probability distributions of resource occupancy and investigate, by stochastic techniques, how to obtain accurate and precise prediction outcomes. To optimize resource allocation decisions we consider multiple criteria that are important in the hospital problem setting. We use three conflicting objectives in the optimization: maximal patient throughput, minimal resource costs and minimal usage of back-up capacity. All criteria can be taken into account by finding decision policies that have the best trade-off between the criteria. We derived various decision policies that partly allow for adaptive resource allocations. The design of the policies allows the policies to be easily understandable for hospital experts. Moreover, we present a bed exchange mechanism that enables a realistic implementation of these adaptive policies in practice. In our optimization approach, the parameters of the different decision policies are determined using a multiobjective evolutionary algorithm (MOEA). Specifically, the MOEA optimizes the output of the simulation (i.e. the three optimization criteria) as a function of the policy parameters. Our results on resource management show that the benchmark allocations obtained from a case study are considerably improved by the optimized decision policies. Furthermore, our results show that using adaptive policies can lead to better results and that further improvements may be obtained by integrating prediction into a decision policy

Serviço de emergência médica angolano : optimização utilizando sistemas multi-agente

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015A temática da saúde é uma das que apresenta mais desafios em Angola. Os desafios são não só intrínsecos à própria área, mas resultam também de condicionantes externas. Uma das áreas mais problemáticas, dada a sua natureza complexa e multidisciplinar, é a dos serviços de emergências hospitalares. Visando um aumento de eficiência desses serviços, podem estudar e ensaiar-se várias políticas públicas, mas que, frequentemente, apenas podem ser avaliadas quando já se encontram implementadas. A simulação à priori dessas políticas apresenta vários benefícios: o design pode ser ajustado aos objectivos dos decisores políticos de forma mais exacta; as políticas podem reflectir melhor as motivações dos indivíduos envolvidos em diversos papéis (utilizadores, médicos, enfermeiros, funcionários públicos, auditores, decisores políticos); as ligações micro-macro e as mediações são representadas explicitamente; a simulação permite a melhoria sucessiva das políticas, de tal forma que as mesmas aquando da sua implementação estejam aperfeiçoadas; os decisores e intervenientes podem conhecer melhor o território de decisão tendo em vista uma economia de custos, um aumento da eficiência dos serviços, uma maior satisfação dos utentes e uma acção mais adequada em situações de contingência. Defendemos a simulação baseada em multi-agente como forma de orientar a especificação de políticas. Os sistemas multi-agente (SMA) permitem a representação de agentes racionais heterogéneos e fornecem uma abordagem para criar modelos dinâmicos complexos de fenómenos sociais. Ao longo dos últimos anos assistiu-se a um crescente interesse pela utilização dos SMA na área da prestação de cuidados de saúde. O potencial de flexibilidade, adaptabilidade e robustez dos SMA é amplamente considerado como uma mais-valia para a área da saúde em tópicos como o apoio à decisão médica, diagnóstico e monitorização de pacientes, prestação de cuidados remotos, gestão e coordenação de recursos ou aprendizagem e treino médicos. Nesta dissertação descreve-se como podemos atacar o problema de optimização das políticas de serviços de emergência médica, quando há uma diferença clara entre a concepção dessas políticas e o uso que as pessoas lhes dão. Apresenta-se o cenário e um modelo para a simulação, identificando os actores envolvidos, as medidas necessárias para avaliar os resultados multidimensionais da simulação e como se podem afinar as políticas e simulá-las antes da sua implementação no mundo real. Motivado pelo cenário mais eficiente resultante da simulação e por forma a validá-lo, implementou-se o protótipo SIEMA (Sistema Integrado de Emergências Médicas Angolanas) com a finalidade de apoiar a gestão de emergências médicas em Angola.Healthcare presents major challenges in Angola. These challenges are not only intrinsic to the area itself, but are also a consequence of external constraints. Medical emergency services, on account of their complex and multidisciplinary nature, are one of the most problematic areas. Aiming at an increase of efficiency of these services, various public policies can be studied and tested, but their results often can only be assessed when policies are already implemented. The simulation of these policies has several benefits: the design can be adjusted to the objectives of policy makers more accurately; policies can better reflect the motivations of the individuals involved in various roles (patients, doctors, nurses, hospital staff, auditors, policy makers); micro-macro links and mediations are represented explicitly. Simulation allows successive improvement of policies before their implementation; decision-makers and stakeholders can better understand the decision territory, namely concerning cost savings, increased service efficiency, greater user satisfaction and a more adequate action in contingency situations. We defend multi-agent based simulation as a way to guide the policy specification. Multi-agent systems (MAS) allow the representation of heterogeneous agents and provide a rational approach to create complex social phenomena dynamic models. The past few years have witnessed a growing interest in the use of MAS in health. The potential for flexibility, adaptability and robustness of MAS is widely regarded as an asset for healthcare on topics such as medical decision support, diagnosis and monitoring of patients, remote care, management and coordination of resources or learning and medical training. This thesis describes how we tackle the optimization of medical emergency services policies when there is a clear distance between the conception of policies and the use that people give them. We present the scenario and a model for the simulation, identify involved actors and fine-tuned and simulate policies before implementation in the real world. Motivated by the most efficient scenario resulting from the simulation and in order to validate it, we implemented a prototype (SIEMA) to support the management of medical emergencies in Angola

Multikonferenz Wirtschaftsinformatik 2010 : Göttingen, 23. - 25. Februar 2010 ; Kurzfassungen der Beiträge

Dieser Band enthält Kurzfassungen der Beiträge zur MKWI 2010. Die Vollversionen der Beiträge sind auf dem wissenschaftlichen Publikationenserver (GoeScholar) der Georg-August-Universität Göttingen und über die Webseite des Universitätsverlags unter http://webdoc.sub.gwdg.de/univerlag/2010/mkwi/ online verfügbar und in die Literaturnachweissysteme eingebunden

Abteilungsübergreifende Termin- und Reihenfolgeplanung in Krankenhäusern mittels multichromosomaler, künstlicher Evolution

Seit der Umstellung des Vergütungssystems auf diagnosebezogene Fallpauschalen sind Krankenhäuser gezwungen, effizient zu arbeiten um kostendeckend zu wirtschaften. Unter diesem Gesichtspunkt steigt die Notwendigkeit zur Planung und Optimierung der Abläufe innerhalb dieser. Gegenstand der vorliegenden Arbeit ist eine abteilungsübergreifende Termin- und Reihenfolgeplanung der Patienten, mit dem Ziel, die Krankenhausressourcen möglichst effizient einzusetzen und die Wartezeiten der Patienten zu minimieren. Bis heute werden Lösungsansätze nach ambulanter Aufnahmeplanung, stationärer Aufnahmeplanung sowie OP-Planung differenziert und überwiegend losgelöst voneinander betrachtet. Die bisherigen Lösungsansätze verkennen weitestgehend, dass die stationär und ambulant aufgenommenen Patienten im weiteren Ablauf teils dieselben Ressourcen beanspruchen. Auch lässt sich eine OP-Planung nicht verlässlich durchführen, ohne die Aufnahmeplanung und ohne die vorhandenen Krankenhausressourcen (z.B. Betten, Personal) mit deren jeweiligen Kapazitäten in der Planung zu berücksichtigen. Daher erfolgt erstmalig in der hier entwickelten Planungsmethode eine gesamtheitliche Betrachtung der Problemfelder der stationären Aufnahmeplanung, der ambulanten Aufnahmeplanung und der OP-Planung, unter Berücksichtigung erforderlicher vor- und nachgelagerter Ressourcen, insbesondere der Notaufnahme. Es wird auf den Untersuchungsgegenstand bezogen aufgezeigt, wie die vorliegenden fachübergreifenden und dynamischen Gegebenheiten (fachübergreifende und dynamische Komplexität) berücksichtigt werden können, ohne im Detaillierungsgrad mit vielen stark vereinfachenden Annahmen zu arbeiten (Detailkomplexität), wie es bisherige Arbeiten tun. Um der dynamischen Eigenschaft der zugrundeliegenden Prozesse zu entsprechen (dynamische Komplexität), wurde ein dynamisches Simulationsmodell (ausführbares Modell) entwickelt, welches unter Einsatz einer hier entwickelten Methode zur automatisierten Transformation aus eEPK Prozessbeschreibungen aufgebaut und an Realdaten validiert wurde. Der Arbeit liegen Prozesse und Daten aus drei Kliniken der Maximalversorgung zugrunde (Referenzklinik). Um der Detailkomplexität gerecht zu werden, sind im Modell auf die Planung einwirkende stochastische Einflüsse berücksichtigt, wie u.a. Notfälle, nicht geplantes Patientenaufkommen (nicht-elektiv, walk-ins), Unpünktlichkeit von Patienten, Ausbleiben von Patienten (no-show), Varianzen im Behandlungsverlauf, Varianzen in den Bearbeitungszeiten oder Störungen resp. Ausfälle technischer Ressourcen. Das entwickelte Planungskonzept wird in einer multichromosomalen Repräsentation kodiert. Die Planung und Optimierung erfolgt mit einem hybriden Genetischen Algorithmus (GA), welcher eine hier entwickelte Methode der selbstadaptiven Mutation einsetzt. Im Weiteren werden die Ergebnisse der optimierten Termin- und Reihenfolgeplanung dargelegt und analysiert. Abschließend wird ein konkreter Vorschlag zur Umsetzung im Krankenhaus unterbreitet.Since the compensation system was switched to diagnosis-related payments, hospitals have been forced to work efficiently in order to economize and cover costs. To achieve these objectives, processes have to be planned and optimized. In this work an inter-departmental plan for appointment and patient sequencing is developed, that focus on using hospital resources efficiently and minimize patients waiting time. Up to now the approach has been to find dedicated and independent solutions for the outpatient admission, inpatient admission and operating room (surgery) planning, even so they are dynamically coupled. The current solutions for the most part do not take into consideration that inpatients and outpatients lay claim to many of the same resources in subsequent procedures. Surgery planning cannot be carried out reliably without planning admission or taking the available resources of the hospital (e.g. beds, staff) and their respective capacities into account. The planning method developed here, hence for the first time combines the problem areas in planning admissions for in- and outpatients, and surgeries, taking into account the required upstream and downstream resources, in particular from the emergency department. It is shown, how the inter-departmental, dynamic conditions (inter-departmental and dynamic complexity) can be taken into account without the need to work at a level of detail with numerous grossly simplifying assumptions (detail complexity) as in previous research. In order to consider the dynamics of the underlying processes (dynamic complexity), a dynamic simulation model (executable model) has been developed. An automated transformation method was developed and used to transform an eEPC description of the underlying processes into an executable model. The model was validated for recorded data from a hospital. The research was based on processes and data from three maximum care clinics (reference clinics). To do justice to the detail complexity, stochastic variations which affect planning have been taken into consideration, such as emergencies, unplanned patient volumes (non-electives, walk-ins), patients' lack of punctuality, patient absence (no-show), variances in the course of treatment, variances in processing time or faults and failures of technical resources. The developed planning concept is coded by a multichromosomal representation. For planning and optimization a hybrid genetic algorithm (GA) is used, that employs a method for self-adapting mutation developed here. GA performance for self-adapting mutation rate and several static mutation rates are compared. The results of the optimization are presented and analyzed. It is shown how the developed planning concept may be integrated into an existing hospital IT-system (SAP IS-H*med)