Patient Streaming as a Mechanism for Improving Responsiveness in Emergency Departments

Crisis level overcrowding conditions in Emergency Departments (ED's) have led hospitals to seek out new patient flow designs to improve both responsiveness and safety. One approach that has attracted attention and experimentation in the emergency medicine community is a system in which ED beds and care teams are segregated and patients are "streamed" based on predictions of whether they will be discharged or admitted to the hospital. In this paper, we use a combination of analytic and simulation models to determine whether such a streaming policy can improve ED performance, where it is most likely to be effective, and how it should be implemented for maximum performance. Our results suggest that the concept of streaming can indeed improve patient flow, but only in some situations. First, ED resources must be shared across streams rather than physically separated. This leads us to propose a new "virtual-streaming" patient flow design for ED's. Second, this type of streaming is most effective in ED's with (1) a high percentage of admitted patients, (2) longer care times for admitted patients than discharged patients, (3) a high day-to-day variation in the percentage of admitted patients, (4) long patient boarding times (e.g., caused by hospital "bed-block"), and (5) high average physician utilization. Finally, to take full advantage of streaming, physicians assigned to admit patients should prioritize upstream (new) patients, while physicians assigned to discharge patients should prioritize downstream (old) patients.http://deepblue.lib.umich.edu/bitstream/2027.42/85792/1/1162_Hopp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/85792/4/2012Jan18WHopp#1162.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/85792/6/1162_Hopp_mar12.pd

Investigation of Frequency of the Lethal Triad and Its 24 Hours Prognostic Value among Patients with Multiple Traumas

BACKGROUND: Death in multiple trauma (MT) patients is one of the serious concerns of the medical service provider. Any prediction of the likelihood of death on the assessment of the patient's condition is performed using different variables, one of the tools in the triage of patients to determine their condition. AIM: We aimed to investigate the frequency and the predictive value of death in 24 hours triad of death in patients qualified with multiple traumas admitted to Imam Khomeini hospital. METHODS: This was a prospective cross-sectional study to determine the prevalence and predictive value of 24-hour triad of death among patients with MT referred to an emergency department. Three factors including acidosis, hypothermia and coagulopathy and predictive value of 24-hour death were evaluated. Arterial blood gas, oral temperature and blood samples for coagulation factors were analysed. Data were analysed using SPSS version 19. Multivariate analysis (logistic regression) was used to determine the predictive value of the triad of death. RESULTS: A group of 199 MT patients referring to Imam Khomeini hospital during the first 6 months of 2015 were evaluated for the first 24 hours of admission. Logistic regression analysis showed that using the following formula based on the triad of death can predict death in 96% of cases can be based on the triad of a death foretold death upon admission to the emergency room. It should be noted that this prediction tool as 173 people left alive after 24 hours as live predicts (100% correct). CONCLUSION: The triad of death is one of the tools in the triage of patients to determine their condition and care plan to be used, provided valuable information to predict the prognosis of patients with a medical team

Model sistem dinamik untuk meramalkan bilangan pesakit dan keperluan sumber tenaga di Zon Kuning Jabatan Kecemasan

Jabatan Kecemasan Hospital Universiti Sains Malaysia (JKHUSM) telah menunjukkan perubahan yang pesat dan menghadapi transformasi drastik berhubung dengan kepentingan dalam sistem penjagaan kesihatan. Peramalan dan perancangan sumber yang munasabah perlu dilakukan bagi memenuhi permintaan pesakit yang kian bertambah. Justeru, kajian ini memfokuskan kepada Zon Kuning di JKHUSM yang memerlukan perancangan sewajarnya atas keperluan sumber yang sepatutnya disediakan pada masa kini dan akan datang untuk membantu pihak pengurusan dalam perancangan strategik jabatan serta menambah baik aliran pesakit dan perkhidmatan di zon tersebut. Pemodelan Sistem Dinamik telah dibangunkan untuk meramalkan bilangan pesakit yang akan berkunjung serta jumlah sumber yang diperlukan untuk memenuhi permintaan perkhidmatan di Zon Kuning JKHUSM pada masa sekarang (2014) dan masa hadapan bagi tempoh lima (2019) dan sepuluh tahun akan datang (2024). Hasil kajian meramalkan sumber yang diperlukan bagi memenuhi permintaan pesakit yang berkunjung di Zon Kuning pada masa sekarang adalah seramai 11 orang doktor, 12 orang jururawat dan 18 buah katil berbanding dengan sembilan orang doktor, sembilan orang jururawat dan 16 buah katil sedia ada. Seterusnya penambahan dua buah katil diramalkan untuk memenuhi keperluan pesakit bagi tempoh lima dan sepuluh tahun akan datang. Manakala tiada penambahan doktor dan jururawat diperlukan bagi memenuhi permintaan pesakit bagi tempoh lima tahun akan datang. Namun begitu dijangkakan penambahan seorang doktor dan seorang jururawat diperlukan bagi memenuhi permintaan 10 tahun akan datang. Oleh itu, peramalan penambahan sumber ini adalah sangat penting untuk menambah baik aliran pesakit di Zon Kuning JKHUSM serta membantu dalam mencapai Penunjuk Prestasi Utama jabatan ini. Hasil kajian yang diperoleh akan membantu pihak pengurusan membuat keputusan yang wajar dengan belanjawan yang telah ditetapkan demi meningkatkan kualiti perkhidmatan yang ditawarkan di samping meningkatkan tahap prestasi Zon Kuning JKHUSM

Predictive Analytics in Practice: A Novel Simulation Application for Addressing Patient Flow Challenges in Today's Emergency Departments

Abstract Objectives: To develop a flexible software application that uses predictive analytics to enable emergency department (ED) decision-makers in virtually any environment to predict the effects of operational interventions and enhance continual process improvement efforts. To demonstrate the ability of the application's core simulation model to recreate and predict sitespecific patient flow in two very different EDs: a large academic center and a freestanding ED. To describe how the application was used by a freestanding ED medical director to match ED resources to patient demand. Methods: The application was developed through a public-private partnership between University of Florida Health and Roundtable Analytics, Inc., supported by a National Science Foundation Small Business Technology Transfer (STTR) grant. The core simulation technology was designed to be quickly adaptable to any ED using data routinely collected by most electronic health record systems. To demonstrate model accuracy, Monte Carlo studies were performed to predict the effects of management interventions in two distinct ED settings. At one ED, the medical director conducted simulation studies to evaluate the sustainability of the current staffing strategy and inform his decision to implement specific interventions that better match ED resources to patient demand. After implementation of one intervention, the fidelity of the model's predictions was evaluated. Results: A flexible, cloud-based software application enabling ED decision-makers to predict the effects of operational decisions was developed and deployed at two qualitatively distinct EDs. The application accurately recreated each ED's throughput and faithfully predicted the effects of specific management interventions. At one site, the application was used to identify when increasing arrivals will dictate that the current staffing strategy will be less effective than an alternative strategy. As actual arrivals approached this point, decision-makers used the application to simulate a variety different interventions; this directly informed their decision to implement a new strategy. The observed outcomes resulting from this intervention fell within the range of predictions from the model. Conclusion: This application overcomes technical barriers that have made simulation modeling inaccessible to key decision-makers in emergency departments. Using this technology, ED managers with no programming experience can conduct customized simulation studies regardless of their ED's volume and complexity. In two very different case studies, the fidelity of the application was established and the application was shown to have a direct positive effect on patient flow. The effective use of simulation modeling promises to replace inefficient trial-anderror approaches and become a useful and accessible tool for healthcare managers challenged to make operational decisions in environments of increasingly scarce resources

Improving Patient Flow through Early Bed Requests at UNC Hospital ED: A Discrete-Event Simulation Study

Emergency department (ED) overcrowding is a widely recognized problem in the United States due to numerous legal, economic, and operational factors. This difficulty has motivated a significant body of clinical and academic interest in applying operations research (OR) techniques to improve patient flow in EDs. In particular, discrete event simulation has been extensively utilized to study each of the three steps of ED patient flow (into, within, and out of the ED). The flexibility of simulation makes it particularly useful for examining each stage and comparing alternatives to reduce overcrowding. Many approaches to addressing ED overcrowding focus on ways to increase bed capacity for patients requiring service since beds are the bottleneck resource in many EDs. The problem of bed capacity can be addressed at each stage of patient flow. For example, many works have considered the effect of physician-at-triage (PT) altering the flow within the ED by treating low acuity patients with low resource requirements in a separate clinic to preserve more beds for severe patients. In contrast, early bed request attempts to improve flow out of the ED by reducing “bed-block”, the utilization of ED beds by patients who have completed service but board in the ED until an in-patient bed is available. Early boarding is the process of identifying at the time of triage patients who will later be admitted as in-patients. The ED “calls ahead” to request a bed from the appropriate ward so that the in-patient bed is ready when, or soon after, the patient completes service in the ED. In theory, such a policy has great potential to deliver system-wide improvements since many studies recognize bed-block and patient flow out of the ED as major drivers in long LOS and wait durations and “one of the most well-known operational problems to afflict an ED”. This paper describes the development and application of a simulation model for the UNC Hospitals ED to examine the effect of implementing an early bed request policy. Section 2 describes the patient flow at UNC Hospitals ED, the simulation model, the proposed early bed request policy, and how this policy is incorporated into the simulation model. Section 3 provides details on the data used, the estimation of input parameters, and validation of the model. Section 4 reports the results of simulation experiments on early bed request policies.Bachelor of Scienc

Reducing Wait Time Prediction In Hospital Emergency Room: Lean Analysis Using a Random Forest Model

Most of the patients visiting emergency departments face long waiting times due to overcrowding which is a major concern across the hospital in the United States. Emergency Department (ED) overcrowding is a common phenomenon across hospitals, which leads to issues for the hospital management, such as increased patient s dissatisfaction and an increase in the number of patients choosing to terminate their ED visit without being attended to by a medical healthcare professional. Patients who have to Leave Without Being Seen (LWBS) by doctors often leads to loss of revenue to hospitals encouraging healthcare professionals to analyze ways to improve operational efficiency and reduce the operational expenses of an emergency department. To keep patients informed of the conditions in the emergency room, recently hospitals have started publishing wait times online. Posted wait times help patients to choose the ED which is least overcrowded thus benefiting patients with shortest waiting time and allowing hospitals to allocate and plan resources appropriately. This requires an accurate and efficient method to model the experienced waiting time for patients visiting an emergency medical services unit. In this thesis, the author seeks to estimate the waiting time for low acuity patients within an ED setting; using regularized regression methods such as Lasso, Ridge, Elastic Net, SCAD and MCP; along with tree-based regression (Random Forest). For accurately capturing the dynamic state of emergency rooms, queues of patients at various stage of ED is used as candidate predictor variables along with time patient s arrival time to account for diurnal variation. Best waiting time prediction model is selected based on the analysis of historical data from the hospital. Tree-based regression model predicts wait time of low acuity patients in ED with more accuracy when compared with regularized regression, conventional rolling average, and quantile regression methods. Finally, most influential predictors for predictability of patient wait time are identified for the best performing model

Improve primary care performance through operations management: An application to emergency care and preventive care

El propósito principal de esta tesis es aplicar el método de gestión de operaciones para mejorar el rendimiento de los responsables de proporcionar atención sanitaria en relación con dos componentes principales de la atención primaria: atención de urgencia y atención primaria. Durante muchos años, en la atención sanitaria se han aplicado los sistemas de gestión de operaciones (OM) y de investigación de operaciones (OR) con la finalidad de mejorar la eficiencia en la prestación de los servicios sanitarios. El núcleo del sistema de atención médica es la atención sanitaria, cuyas funciones principales incluyen el suministro de un punto de entrada, la prestación de atención médica y preventiva fundamental y ayudar a los pacientes a coordinar y a integrar la atención, aspectos que son fundamentales de cara a mejorar no solo el resultado sanitario de los pacientes, sino también el rendimiento en términos de coste de todo el sistema sanitario (Starfield 1998). En un estudio sobre el rendimiento de la atención primaria y del sistema de salud (Schoen et al., 2004), en EE. UU. se registró un índice de utilización del departamento de urgencias (ED) muy superior al de otros tres países, el cual venía acompañado de un menor porcentaje de adultos que dispusieran de un doctor, un lugar o una clínica habitual donde acudir al caer enfermos. Por este motivo, el capítulo 2 de esta disertación aborda la mejora del departamento de salas de urgencia a través del rediseño del proceso. Otro hallazgo fundamental de la encuesta es que Canadá cuenta con el menor índice de chequeos en términos de prueba de Papanicolaou y mamografías. Debido a la importancia de la atención preventiva para salvar vidas y reducir costes, el capítulo 3 de esta disertación analiza cómo mejorar el programa de atención preventiva financiado por el gobierno a través del diseño de la red. El capítulo 2 establece el contexto de un departamento de urgencias (ED) en un hospital terciario con un censo anual de 55 000 pacientes, y analiza la forma en la que el proceso de rediseño de una prueba sanguínea específica tiene un determinado impacto sobre la congestión del ED. De forma más específica, analizamos en cambio en tres magnitudes de rendimiento después de que el análisis de la muestra de sangre del paciente para determinar los niveles de troponina fuera trasladada del laboratorio central del laboratorio al interior del ED. Mediante la teoría de la asignación de colas de prioridad, generamos hipótesis sobre las siguientes medidas de rendimiento: tiempo de espera (definido como la diferencia de tiempo entre el registro de entrada del paciente y la asignación de cama), tiempo de servicio (definido como la diferencia de tiempo entre la asignación de cama y la distribución, el metabolismo y la eliminación de un fármaco) y calidad del servicio (definido como el índice de revisión de los pacientes tras 72 horas). Mediante un modelo de diferencias en diferencias, determinamos que el rediseño del proceso está asociado con unas mejoras estadísticamente significativas en casi todas las mediciones de rendimiento operativo. Concretamente, encontramos que la adopción de POCT está asociada a una reducción del 21,6 % en el tiempo de servicio entre los pacientes objeto de la prueba durante las horas punta, y en una reducción de entre el 5,9 % y el 35,5 % en el tiempo de espera en función de la categoría de prioridad del paciente durante esas mismas horas punta. Además, encontramos que la adopción de un POCT estaba asociada con una mejora de la calidad del servicio, puesto que la probabilidad de recaída pronosticada se redujo en un 0,64 % durante su uso. También descubrimos importantes efectos indirectos a través de todo el sistema en pacientes que no habían sido objeto de un POCT (pacientes que no son objeto de prueba). En otras palabras, la adopción de un POCT está asociada con una reducción del tiempo de espera entre estos pacientes que no son objeto de prueba de un 4,73 % y a una reducción del 11,6 % en el tiempo de espera en función de la categoría de prioridad de los pacientes durante las horas punta. Al examinar el impacto del POCT entre ambas poblaciones de pacientes, tanto los que fueron sometidos a la prueba como los que no, se pudo determinar que esta investigación es única a la hora de identificar los grandes beneficios en el sistema que pueden lograrse a través del rediseño del proceso asociado al ED. El tercer capítulo de esta tesis emplea un modelo de elección de preferencias para analizar las prioridades del cliente en la atención preventiva desde la perspectiva de la configuración del servicio. Aplicamos el modelo en el contexto de un programa de chequeos asociados con el cáncer de mama financiado por el gobierno en Montreal (Canadá), con el fin de identificar las contrapartidas que reciben los participantes del programa a la hora de acceder a un conjunto de instalaciones con diferentes configuraciones de servicio basadas en sus auténticas preferencias. De forma más concreta, analizamos estas preferencias en relación con el tiempo de espera para obtener cita, el tiempo de desplazamiento a la clínica en la que se vaya a practicar el chequeo, la disponibilidad del aparcamiento de la clínica, el horario de apertura de la clínica, el tiempo de espera dentro de la clínica el día del chequeo, la preparación del personal de enfermería, el proceso de chequeo y el tiempo de espera para recibir el resultado. Pudimos comprobar que la preparación del personal de enfermería (es decir, si son capaces de responder preguntas relacionadas con el chequeo o con el cáncer de mama) y el tiempo de espera para obtener una cita eran los factores más determinantes a la hora de elegir una clínica, seguidos de cerca por la disponibilidad de aparcamiento. Mediante el análisis de clases latentes también podemos confirmar que, al contrario de lo apuntado por otras investigaciones, no existe una heterogeneidad clara entre los participantes del programa. Nuestro modelo Arena de simulación muestra que tener en cuenta las preferencias del cliente en el diseño de las configuraciones del servicio mejorará notablemente tanto el nivel de congestión como el índice de participación en las nuevas pruebas. Como conclusión de ambos capítulos, esta tesis trata de generar implicaciones en términos de gestión en lo que respecta a la configuración de la atención sanitaria que puedan ayudar a mejorar la calidad del servicio mediante el uso de un enfoque de metodología empírica. Vemos que pueden acometerse importantes mejoras en los servicios existentes a través del rediseño del proceso de servicio y de la comprensión de las preferencias del cliente, sin necesidad de revisar todo el sistema de atención sanitaria

Studies on using data-driven decision support systems to improve personalized medicine processes

This dissertation looks at how new sources of information should be incorporated into medical decision-making processes to improve patient outcomes and reduce costs. There are three fundamental challenges that must be overcome to effectively use personalized medicine, we need to understand: 1) how best to appropriately designate which patients will receive the greatest value from these processes; 2) how physicians and caregivers interpret additional patient-specific information and how that affects their decision-making processes; and finally, (3) how to account for a patient’s ability to engage in their own healthcare decisions. The first study looks at how we can infer which patients will receive the most value from genomic testing. The difficult statistical problem is how to separate the distribution of patients, based on ex-ante factors, to identify the best candidates for personalized testing. A model was constructed to infer a healthcare provider’s decision on whether this test would provide beneficial information in selecting a patient’s medication. Model analysis shows that healthcare providers’ primary focus is to maximize patient health outcomes while considering the impact the patient’s economic welfare. The second study focuses on understanding how technology-enabled continuity of care (TECC) for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) patients can be utilized to improve patient engagement, measured in terms of patient activation. We shed light on the fact that different types of patients garnered different levels of value from the use of TECC. The third study looks at how data-driven decision support systems can allow physicians to more accurately understand which patients are at high-risk of readmission. We look at how we can use available patient-specific information for patients admitted with CHF to more accurately identify which patients are most likely to be readmitted, and also why – whether for condition-related reasons versus for non- related reasons, allowing physicians to suggest different patient-specific readmission prevention strategies. Taken together, these three studies allow us to build a robust theory to tackle these challenges, both operational and policy-related, that need to be addressed for physicians to take advantage of the growing availability of patient-specific information to improve personalized medication processes

Early Information Access to Alleviate Emergency Department Congestion

Alleviating Emergency Department (ED) congestion results in shorter hospital stay which not only reduces the cost of medical procedure but also increase the hospital performance. Length of patient stay is used to determine the hospital performance. Organization Information Processing (OIPT) Theory is used to explain the impact of information access and availability on the information processing need and ability of a hospital. Technical devices such as RFID that works as “Auto Identification tags” is suggested to increase the information availability as well as the information processing capability of the hospitals. This study suggests that the OIPT needs to be further broken down into its entity form and then the impact of these entities is measured separately. On the other hand, institutional factors such as employee behavior towards the new technology is studied to analyze the impact of human factors in the implementation of these technical devices in the ED procedures. It can be implied from this study that early information access does increase the use of supporting EMR implementation. However, the importance of the use of EMR decreases with time on hospital performance. Moreover, other factors such as management policies related to IT positively moderates the relationship between information availability and the processing capability of a hospital ED