A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Methodological approaches to support process improvement in emergency departments: a systematic review

The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rate

Operating room planning and scheduling: A literature review.

This paper provides a review of recent research on operating room planning and scheduling. We evaluate the literature on multiple fields that are related to either the problem setting (e.g. performance measures or patient classes) or the technical features (e.g. solution technique or uncertainty incorporation). Since papers are pooled and evaluated in various ways, a diversified and detailed overview is obtained that facilitates the identification of manuscripts related to the reader's specific interests. Throughout the literature review, we summarize the significant trends in research on operating room planning and scheduling and we identify areas that need to be addressed in the future.Health care; Operating room; Scheduling; Planning; Literature review;

An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector

[EN] Emergency Care Networks (ECNs) were created as a response to the increased demand for emergency services and the ever-increasing waiting times experienced by patients in emergency rooms. In this sense, ECNs are called to provide a rapid diagnosis and early intervention so that poor patient outcomes, patient dissatisfaction, and cost overruns can be avoided. Nevertheless, ECNs, as nodal systems, are often inefficient due to the lack of coordination between emergency departments (EDs) and the presence of non-value added activities within each ED. This situation is even more complex in the public healthcare sector of low-income countries where emergency care is provided under constraint resources and limited innovation. Notwithstanding the tremendous efforts made by healthcare clusters and government agencies to tackle this problem, most of ECNs do not yet provide nimble and efficient care to patients. Additionally, little progress has been evidenced regarding the creation of methodological approaches that assist policymakers in solving this problem. In an attempt to address these shortcomings, this paper presents a three-phase methodology based on Discrete-event simulation, payment collateral models, and lean six sigma to support the design of in-time and economically sustainable ECNs. The proposed approach is validated in a public ECN consisting of 2 hospitals and 8 POCs (Point of Care). The results of this study evidenced that the average waiting time in an ECN can be substantially diminished by optimizing the cooperation flows between EDs.The authors would like to express his gratitude to Giselle Polifroni Avendaño for supporting this research.Ortiz-Barrios, MA.; Alfaro Saiz, JJ. (2020). An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector. PLoS ONE. 15(6):1-28. https://doi.org/10.1371/journal.pone.0234984S128156Sheard, S. (2018). Space, place and (waiting) time: reflections on health policy and politics. Health Economics, Policy and Law, 13(3-4), 226-250. doi:10.1017/s1744133117000366Morley, C., Stankovich, J., Peterson, G., & Kinsman, L. (2018). Planning for the future: Emergency department presentation patterns in Tasmania, Australia. International Emergency Nursing, 38, 34-40. doi:10.1016/j.ienj.2017.09.001Baier, N., Geissler, A., Bech, M., Bernstein, D., Cowling, T. E., Jackson, T., … Quentin, W. (2019). Emergency and urgent care systems in Australia, Denmark, England, France, Germany and the Netherlands – Analyzing organization, payment and reforms. Health Policy, 123(1), 1-10. doi:10.1016/j.healthpol.2018.11.001Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018). Emergency department crowding: A systematic review of causes, consequences and solutions. PLOS ONE, 13(8), e0203316. doi:10.1371/journal.pone.0203316Turner, J., Coster, J., Chambers, D., Cantrell, A., Phung, V.-H., Knowles, E., … Goyder, E. (2015). What evidence is there on the effectiveness of different models of delivering urgent care? A rapid review. Health Services and Delivery Research, 3(43), 1-134. doi:10.3310/hsdr03430Porter, M. E., & Kramer, M. R. (2018). Creating Shared Value. Managing Sustainable Business, 323-346. doi:10.1007/978-94-024-1144-7_16Wilson, K. J. (2013). Pay-for-Performance in Health Care. Quality Management in Health Care, 22(1), 2-15. doi:10.1097/qmh.0b013e31827dea50Ortiz Barrios, M. A., Escorcia Caballero, J., & Sánchez Sánchez, F. (2015). A Methodology for the Creation of Integrated Service Networks in Outpatient Internal Medicine. Ambient Intelligence for Health, 247-257. doi:10.1007/978-3-319-26508-7_24Glickman, S. W., Kit Delgado, M., Hirshon, J. M., Hollander, J. E., Iwashyna, T. J., Jacobs, A. K., … Branas, C. C. (2010). Defining and Measuring Successful Emergency Care Networks: A Research Agenda. Academic Emergency Medicine, 17(12), 1297-1305. doi:10.1111/j.1553-2712.2010.00930.xCalvello, E. J. B., Broccoli, M., Risko, N., Theodosis, C., Totten, V. Y., Radeos, M. S., … Wallis, L. (2013). Emergency Care and Health Systems: Consensus-based Recommendations and Future Research Priorities. Academic Emergency Medicine, 20(12), 1278-1288. doi:10.1111/acem.12266Stoner, M. J., Mahajan, P., Bressan, S., Lam, S. H. F., Chumpitazi, C. E., Kornblith, A. E., … Kuppermann, N. (2018). Pediatric Emergency Care Research Networks: A Research Agenda. Academic Emergency Medicine, 25(12), 1336-1344. doi:10.1111/acem.13656Navein, J. (2003). The Surrey Emergency Care System: a countywide initiative for change. Emergency Medicine Journal, 20(2), 192-195. doi:10.1136/emj.20.2.192Martinez, R. (2010). Keynote Address-Redefining Regionalization: Merging Systems to Create Networks. Academic Emergency Medicine, 17(12), 1346-1348. doi:10.1111/j.1553-2712.2010.00945.xA discrete event simulation model of an emergency department network for earthquake conditions. 6th International Conference on Modeling, Simulation, and Applied Optimization, ICMSAO 2015—Dedicated to the Memory of Late Ibrahim El-Sadek; 2015.Preparedness of an emergency department network for a major earthquake: A discrete event simulation-based design of experiments study. Uncertainty Modelling in Knowledge Engineering and Decision Making—Proceedings of the 12th International FLINS Conference, FLINS 2016; 2016.Salisbury, C., & Bell, D. (2010). Access to urgent health care. Emergency Medicine Journal, 27(3), 186-188. doi:10.1136/emj.2009.073056Mousavi Isfahani, H., Tourani, S., & Seyedin, H. (2019). Lean management approach in hospitals: a systematic review. International Journal of Lean Six Sigma, 10(1), 161-188. doi:10.1108/ijlss-05-2017-0051Ahmed, S., Manaf, N. H. A., & Islam, R. (2013). Effects of Lean Six Sigma application in healthcare services: a literature review. Reviews on Environmental Health, 28(4). doi:10.1515/reveh-2013-0015Furterer, S. L. (2018). Applying Lean Six Sigma methods to reduce length of stay in a hospital’s emergency department. Quality Engineering, 30(3), 389-404. doi:10.1080/08982112.2018.1464657Romero-Conrado, A. R., Castro-Bolaño, L. J., Montoya-Torres, J. R., & Jiménez Barros, M. Á. (2017). Operations research as a decision-making tool in the health sector: A state of the art. DYNA, 84(201), 129. doi:10.15446/dyna.v84n201.57504Modeling the Healthcare Services in Hilla Emergency Department. ICOASE 2018—International Conference on Advanced Science and Engineering; 2018.Ibrahim, I. M., Liong, C.-Y., Bakar, S. A., Ahmad, N., & Najmuddin, A. F. (2018). Estimating optimal resource capacities in emergency department. Indian Journal of Public Health Research & Development, 9(11), 1558. doi:10.5958/0976-5506.2018.01670.4Bedoya-Valencia, L., & Kirac, E. (2016). Evaluating alternative resource allocation in an emergency department using discrete event simulation. SIMULATION, 92(12), 1041-1051. doi:10.1177/0037549716673150Baril, C., Gascon, V., & Vadeboncoeur, D. (2019). Discrete-event simulation and design of experiments to study ambulatory patient waiting time in an emergency department. Journal of the Operational Research Society, 70(12), 2019-2038. doi:10.1080/01605682.2018.1510805Combined forecasting of patient arrivals and doctor rostering simulation modelling for hospital emergency department. IEEE International Conference on Industrial Engineering and Engineering Management; 2018.Hussein, N. A., Abdelmaguid, T. F., Tawfik, B. S., & Ahmed, N. G. S. (2017). Mitigating overcrowding in emergency departments using Six Sigma and simulation: A case study in Egypt. Operations Research for Health Care, 15, 1-12. doi:10.1016/j.orhc.2017.06.003Integrated simulation and data envelopment analysis models in emergency department. AIP Conference Proceedings; 2016.Ortiz Barrios, M., Felizzola Jiménez, H., & Nieto Isaza, S. (2014). Comparative Analysis between ANP and ANP- DEMATEL for Six Sigma Project Selection Process in a Healthcare Provider. Lecture Notes in Computer Science, 413-416. doi:10.1007/978-3-319-13105-4_62Ortiz Barrios, M. A., & Felizzola Jiménez, H. (2016). Use of Six Sigma Methodology to Reduce Appointment Lead-Time in Obstetrics Outpatient Department. Journal of Medical Systems, 40(10). doi:10.1007/s10916-016-0577-3Ortiz-Barrios, M. A., Herrera-Fontalvo, Z., Rúa-Muñoz, J., Ojeda-Gutiérrez, S., De Felice, F., & Petrillo, A. (2018). An integrated approach to evaluate the risk of adverse events in hospital sector. Management Decision, 56(10), 2187-2224. doi:10.1108/md-09-2017-0917Karnon, J., Stahl, J., Brennan, A., Caro, J. J., Mar, J., & Möller, J. (2012). Modeling using Discrete Event Simulation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Value in Health, 15(6), 821-827. doi:10.1016/j.jval.2012.04.013Gillespie, J., McClean, S., Garg, L., Barton, M., Scotney, B., & Fullerton, K. (2016). A multi-phase DES modelling framework for patient-centred care. Journal of the Operational Research Society, 67(10), 1239-1249. doi:10.1057/jors.2015.114Becker, J. B., Lopes, M. C. B. T., Pinto, M. F., Campanharo, C. R. V., Barbosa, D. A., & Batista, R. E. A. (2015). Triage at the Emergency Department: association between triage levels and patient outcome. Revista da Escola de Enfermagem da USP, 49(5), 783-789. doi:10.1590/s0080-623420150000500011Kaushal, A., Zhao, Y., Peng, Q., Strome, T., Weldon, E., Zhang, M., & Chochinov, A. (2015). Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department. Socio-Economic Planning Sciences, 50, 18-31. doi:10.1016/j.seps.2015.02.002Kuo, Y.-H., Rado, O., Lupia, B., Leung, J. M. Y., & Graham, C. A. (2014). Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions. Flexible Services and Manufacturing Journal, 28(1-2), 120-147. doi:10.1007/s10696-014-9198-7Ortíz-Barrios, M. A., & Alfaro-Saíz, J.-J. (2020). Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review. International Journal of Environmental Research and Public Health, 17(8), 2664. doi:10.3390/ijerph1708266

Implementation of an Emergency Department Split-Flow Process at Saint Vincent Hospital

Emergency Departments (ED) are faced with the challenges of increasing demand and constrained capacity, resulting in the need for hospitals to improve efficiency and patient care. In response, some have implemented a concept known as a split-flow process in their ED. The purpose of our project was to develop recommendations for the implementation of a split-flow process at Saint Vincent’s ED in Worcester, MA. We analyzed current ED processes and developed a discrete-event simulation to project the effect of split-flow on ED performance metrics. We present recommendations for staffing assignments, physical layouts, and resources required for a successful implementation. To our knowledge, this is the first simulation model used to guide the implementation of a split-flow process in an ED

Redesigning the Barranquilla's public emergency care network to improve the patient waiting time

Tesis por compendio[ES] La oportunidad en la atención es uno de los críticos de mayor relevancia en la satisfacción de los pacientes que acuden a los servicios de Urgencias. Por tal motivo, las instituciones prestadoras de servicio y las organizaciones gubernamentales deben propender conjuntamente por una atención cada vez más oportuna a costos operacionales razonables. En el caso de la Red Pública en Servicios de Urgencias de Barrannquilla, compuesta por 8 puntos de atención y 2 hospitales, la tendencia marca un continuo crecimiento de la oportunidad en la atención con una tasa de 3,08 minutos/semestre y una probabilidad del 93,13% de atender a los pacientes después de una espera mayor a 30 minutos. Lo anterior se constituye en un síntoma inequívoco de la incapacidad de la Red para satisfacer los estándares de oportunidad establecidos por el Ministerio de Salud, hecho que podría desencadenar el desarrollo de sintomatologías de mayor complejidad, el incremento de la probabilidad de mortalidad, el requerimiento de servicios clínicos más complejos (hospitalización y cuidados intensivos) y el aumento de los costos asociados al servicio. En consecuencia, la presente tesis doctoral presenta el rediseño de la Red Pública en Servicios de Urgencias anteriormente mencionada a fin de otorgar a la población diana un servicio eficiente y altamente oportuno donde tanto las instituciones prestadoras del servicio como los organismos gubernamentales converjan efectivamente. Para ello, fue necesaria la ejecución de 4 grandes fases a través de las cuales se consolidó una propuesta orientada al desarrollo efectivo y sostenible de las operaciones de la Red. Primero, se caracterizó la Red Pública de Servicios de Urgencias en Salud considerando su comportamiento actual en términos de demanda y oportunidad de la atención. Luego, a través de una revisión sistemática de la literatura, se identificaron los enfoques metodológicos que se han implementado para la mejora de la oportunidad y otros indicadores de rendimiento asociados al servicio de Urgencias. Posteriormente, se diseñó una metodología para la creación de redes de Urgencias eficientes y sostenibles la cual luego se validó en la Red Pública sudamericana a fin de disminuir la oportunidad de atención promedio en Urgencias y garantizar la distribución equitativa de los beneficios financieros derivados de la colaboración. Finalmente, se construyó un modelo multicriterio que permitió evaluar el rendimiento de los departamentos de Urgencia e impulsó la creación de estrategias de mejora focalizadas en incrementar su respuesta ante la demanda cambiante, los críticos de satisfacción y las condiciones de operación estipuladas en la ley. Los resultados de esta aplicación evidenciaron que los pacientes que acceden a la Red tienden a esperar en promedio 201,6 min con desviación de estándar de 81,6 min antes de ser atendidos por urgencia. Por otro lado, de acuerdo con la revisión de literatura, la combinación de técnicas de investigación de operaciones, ingeniería de la calidad y analítica de datos es ampliamente recomendada para abordar este problema. En ese sentido, una metodología basada en modelos colaterales de pago, simulación de procesos y lean seis sigma fue propuesta y validada generando un rediseño de Red cuya oportunidad de atención promedio podría disminuir entre 6,71 min y 9,08 min con beneficios financieros promedio de US$29,980/nodo. En último lugar, un modelo compuesto por 8 criterios y 35 sub-criterios fue diseñado para evaluar el rendimiento general de los departamentos de Urgencias. Los resultados del modelo evidenciaron el rol crítico de la infraestructura (Peso global = 21,5i garantir la distribució equitativa dels beneficis financers derivats de la col´laboració. Finalment, es va construir un model multicriteri que va permetre avaluar el rendiment dels departaments d'Urgència i va impulsar la creació d'estratègies de millora focalitzades en incrementar la seva resposta davant la demanda canviant, els crítics de satisfacció i les condicions d'operació estipulades en la llei. Els resultats d'aquesta aplicació van evidenciar que els pacients que accedeixen a la Xarxa tendeixen a esperar de mitjana 201,6 min amb desviació d'estàndard de 81,6 min abans de ser atesos per urgència. D'altra banda, d'acord amb la revisió de literatura, la combinació de tècniques d'investigació d'operacions, enginyeria de la qualitat i analítica de dades és àmpliament recomanada per abordar aquest problema. En aquest sentit, una metodologia basada en models col´laterals de pagament, simulació de processos i llegeixin 6 sigma va ser proposada i validada generant un redisseny de Xarxa la oportunitat d'atenció mitjana podria disminuir entre 6,71 min i 9,08 min amb beneficis financers mitjana d'US$ 29,980 / node. En darrer lloc, un model compost per 8 criteris i 35 sub-criteris va ser dissenyat per avaluar el rendiment general dels departaments d'Urgències. Els resultats de el model evidenciar el paper crític de la infraestructura (Pes global = 21,5%) en el rendiment dels departaments d'Urgència i la naturalesa interactiva de la Seguretat de l'Pacient (C + R = 12,771).[EN] Waiting time is one of the most critical measures in the satisfaction of patients admitted within emergency departments. Therefore, hospitals and governmental organizations should jointly aim to provide timely attention at reasonable costs. In the case of Barranquilla's Pubic Emergency Service Network, composed by 8 Points of care (POCs) and 2 hospitals, the trend evidences a continuous growing of the waiting time with a rate of 3,08 min/semester and a 93,13% likelihood of serving patients after waiting for more than 30 minutes. This is an unmistakable symptom of the network inability for satisfying the standards established by the Ministry of Health, which may trigger the development of more complex symptoms, increase in the death rate, requirement for more complex clinical services (hospitalization and intensive care unit) and increased service costs. This doctoral dissertation then illustrates the redesign of the aforementioned Public Emergency Service Network aiming at providing the target population with an efficient and highly timely service where both hospitals and governmental institutions effectively converge. It was then necessary to implement a 4-phase methodology consolidating a proposal oriented to the effective and sustainable development of network operations. First, the Public Emergency Service Network was characterized considering its current behavior in terms of demand and waiting time. A systematic literature review was then undertaken for identifying the methodological approaches that have been implementing for improving the waiting time and other performance indicators associated with the emergency care service. Following this, a methodology for the creation of efficient and sustainable emergency care networks was designed and later validated in the Southamerican Public network for lessening the average waiting time and ensuring the equitable distribution of profits derived from the collaboration. Ultimately, a multicriteria decision-making model was created for assessing the performance of the emergency departments and propelling the design of improvement strategies focused on bettering the response against the changing demand conditions, critical to satisfaction and operational conditions. The results evidenced that the patients accessing to the network tend to wait 201,6 min on average with a standard deviation of 81,6 min before being served by the emergency care unit. On the other hand, based on the reported literature, it is highly suggested to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for addressing this problem. In this sense, a methodology based on collateral payment models, Discrete-event simulation, and Lean Six Sigma was proposed and validated resulting in a redesigned network whose average waiting time may diminish between 6,71 min and 9,08 min with an average profit US$29,980/node. Lately, a model comprising of 8 criteria and 35 sub-criteria was designed for evaluating the overall performance of emergency departments. The model outcomes revealed the critical role of Infrastructure (Global weight = 21,5%) in ED performance and the interactive nature of Patient Safety (C + R = 12,771).Ortíz Barrios, MÁ. (2020). Redesigning the Barranquilla's public emergency care network to improve the patient waiting time [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156215TESISCompendi

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Emergency department design evaluation and optimization using discrete event simulation

The proposed research would help any architect/owner decide the number of rooms/ cubicles for each sub-department of the ED, as well as have an estimated price for the ED, in order to optimally serve patients entering the ED with a known arrival rate. A thorough literature review was undertaken to collect data concerning the application of decision support tools for minimizing patient waiting times and maximizing the utilization rate in health care systems. Interviews were made with hospital managers in order to verify process flow, waiting times, activity durations, and resources. In addition, several floor plans of EDs have been studied in order to assure the logical flow of the process. Based on the data collected and the several verifications, a discrete event simulation model was developed using ARENA software. This simulation model was then verified by building a similar model on different software, which was AnyLogic. The results proved the accuracy of the model. Twenty additional simulation runs were performed to be used for the regression analysis. The equations resulted from the regression analysis were used for the optimization model. A genetic algorithm was used for the purpose of obtaining optimized resource allocation for different arrival rates within a constrained budget, area, and patient waiting time in the system