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

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

Using Prediction to Improve Patient Flow in a Health Care Delivery Chain

Often, in a health care delivery chain, lack of coordination has been detrimental to timely, high quality care. This paper focuses on the two steps of the hospital health care delivery chain, an emergency department and a hospital’s inpatient units. Past research into this chain has suggested that early prediction of patient need for admission can be used to better align flow between the two departments. This chain and the nature of prediction in health care delivery are discussed as well as a how prediction may be useful in this context. Finally tools for making admission predictions are tested and their possible implications are explored. The results of this exploration show that both expert opinion and a Naïve Bayesian statistical approach have predictive value in this context

Artificial Intelligence Agents and Knowledge Acquisition in Health Information System

This research work highlights the need for AI-powered applications and their usages for theoptimization of information flow processes in the medical sector, from the perspective of howAI-agents can impact human-machine interaction (HCI) for acquiring relevant and necessaryinformation in emergency department (ED). This study investigates how AI-agents can be applied to manage situations of patient related unexpected experiences, such as long waiting times,overcrowding issues, and high number of patients leaving without being diagnosed. For knowledge acquisition, we incorporated modelling workshop techniques for gathering domain information from the domain experts in the context of emergency department in Karolinska Hospi-tal, Solna, Stockholm, Sweden, and for designing the AI-agent utilizing NLP techniques. We dis-cuss how the proposed solution can be used as an assistant to healthcare practitioners and workers to improve medical assistance in various medical procedures to increase flow and to reduce workloads and anxiety levels. The implementation part of this work is based on the natural language processing (NLP) techniques that help to develop the intelligent behavior for information acquisition and itsretriev-al in a natural way to support patients/relatives’ communication with the healthcare organization efficiently and in a natural way

Analyzing Fast-Track Effectiveness at ReadyMED Plus Worcester

Reliant Medical Group is looking to decrease the average patient wait time at its urgent care location, ReadyMED Plus, in Worcester, Massachusetts. ReadyMED Plus management implemented a fast-track system within their urgent care system to streamline patient flow. This project identifies inefficiencies in ReadyMEDs current fast-track system and provides recommendations to reduce patient wait times. The team performed a sensitivity analysis on the current system by developing a simulation model. This model was used to generate recommendations for process flow, and a tool was created to support operational decision making within the urgent care system

Pensamiento Lean en la salud y enfermería: revisión integradora de la literatura

Objetivos: evidenciar o conhecimento científico desenvolvido sobre pensamento Lean na área da saúde, destacando o impacto e as contribuições no cuidado em saúde e enfermagem. Método: revisão integrativa da literatura a partir das bases de dados PubMed, CINAHL, Scopus, Web of Science, Emerald, LILACS e na biblioteca eletrônica SciELO, de 2006 a 2014, com sintaxe de palavras-chaves para cada base, selecionados 47 artigos para análise. Resultados: as categorias foram elaboradas a partir da tríade de qualidade proposta por Donabedian: estrutura, processo e resultado. O pensamento Lean está em ascensão nas pesquisas sobre saúde, principalmente no âmbito internacional, com destaque para os Estados Unidos e Reino Unido, melhorando a estrutura, o processo e o resultado a partir das ações assistenciais e gerenciais. Porém, é uma temática incipiente na enfermagem. Conclusão: por meio desse estudo observou-se que a utilização do pensamento Lean, no contexto da saúde, tem um efeito transformador nos aspectos assistenciais e organizacionais, promovendo vantagens em termos de qualidade, segurança e eficiência dos cuidados de saúde e enfermagem com foco no paciente.Objetivos: evidenciar el conocimiento científico desarrollado sobre el pensamiento Lean en el área de la salud (destacando el impacto y las contribuciones en el cuidado de la salud) y en la enfermería. Método: revisión integradora de la literatura a partir de las bases de datos PubMed, CINAHL, Scopus, Web of Science, Emerald, LILACS y en la biblioteca electrónica SciELO, de 2006 a 2014, con sintaxis de palabras clave para cada base; fueron seleccionados 47 artículos para análisis. Resultados: las categorías fueron elaboradas a partir de la tríade de calidad propuesta por Donabedian: estructura, proceso y resultado. El pensamiento Lean está en ascensión en las investigaciones sobre salud, principalmente en el ámbito internacional, con destaque para los Estados Unidos y Reino Unido, mejorando la estructura, el proceso y el resultado a partir de acciones asistenciales y administrativas. Sin embargo, es una temática incipiente en la enfermería. Conclusión: por medio de ese estudio se observó que la utilización del pensamiento Lean, en el contexto de la salud, tiene un efecto transformador en los aspectos asistenciales y organizacionales, promoviendo ventajas en términos de calidad, seguridad y eficiencia de los cuidados de salud y enfermería con enfoque en el paciente.Objectives: to demonstrate the scientific knowledge developed on lean thinking in health, highlighting the impact and contributions in health care and nursing. Method: an integrative literature review in the PubMed, CINAHL, Scopus, Web of Science, Emerald, LILACS and SciELO electronic library databases, from 2006 to 2014, with syntax keywords for each data base, in which 47 articles were selected for analysis. Results: the categories were developed from the quality triad proposed by Donabedian: structure, process and outcome. Lean thinking is on the rise in health surveys, particularly internationally, especially in the USA and UK, improving the structure, process and outcome of care and management actions. However, it is an emerging theme in nursing. Conclusion: this study showed that the use of lean thinking in the context of health has a transforming effect on care and organizational aspects, promoting advantages in terms of quality, safety and efficiency of health care and nursing focused on the patient

Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

Background The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS). The population are patients that have contacted the ambulance service or turned up at the Emergency Department. The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage. This is in comparison to either an existing decision support tool, clinical opinion or in the absence of these, no comparator. The outcome of this review is the calibration, discrimination and classification statistics. Methods Only derivation studies (with or without internal validation) were included. MEDLINE, CINAHL, PubMed and the grey literature were searched on the 14th December 2019. Risk of bias was assessed using the PROBAST tool and data was extracted using the CHARMS checklist. Discrimination (C-statistic) was a commonly reported model performance measure and therefore these statistics were represented as a range within each machine learning method. The majority of studies had poorly reported outcomes and thus a narrative synthesis of results was performed. Results There was a total of 92 models (from 25 studies) included in the review. There were two main triage outcomes: hospitalisation (56 models), and critical care need (25 models). For hospitalisation, neural networks and tree-based methods both had a median C-statistic of 0.81 (IQR 0.80-0.84, 0.79-0.82). Logistic regression had a median C-statistic of 0.80 (0.74-0.83). For critical care need, neural networks had a median C-statistic of 0.89 (0.86-0.91), tree based 0.85 (0.84-0.88), and logistic regression 0.83 (0.79-0.84). Conclusions Machine-learning methods appear accurate in triaging undifferentiated patients entering the Emergency Care System. There was no clear benefit of using one technique over another; however, models derived by logistic regression were more transparent in reporting model performance. Future studies should adhere to reporting guidelines and use these at the protocol design stage. Registration and funding This systematic review is registered on the International prospective register of systematic reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020168696 This study was funded by the NIHR as part of a Clinical Doctoral Research Fellowship

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. 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Overcrowding analysis in emergency department through indexes: a single center study

Overcrowding in the Emergency Department (ED) is one of the major issues that must be addressed in order to improve the services provided in emergency circumstances and to optimize their quality. As a result, in order to help the patients and professionals engaged, hospital organizations must implement remedial and preventative measures. Overcrowding has a number of consequences, including inadequate treatment and longer hospital stays; as a result, mortality and the average duration of stay in critical care units both rise. In the literature, a number of indicators have been used to measure ED congestion. EDWIN, NEDOCS and READI scales are considered the most efficient ones, each of which is based on different parameters regarding the patient management in the ED

Forecasting weekly emergency department demand in a Portuguese private hospital - generalising to a medium-sized unit

The international Emergency Department (ED) overcrowding crisis affects both private and public Portuguese hospitals, which can be mitigated by an efficient medium-term operational planning. In this light, a Machine Learning multi-step-ahead predictive tool to forecast weekly ED arrivals in the largest unit of a private Portuguese healthcare provider, CUF, was developed. Linear Regression, SARIMAX and LSTM were evaluated and compared. SARIMAX, which obtained the best results, proved to have adequate predictive accuracy to support ED management. Additionally, the question of whether this model could be generalised to a medium-sized CUF ED unit was studied. Keywords: Healthcare, Emergency Department, Machine Learning, Time Series, Multi-step-ahead Forecasting, Model Generalisation