Predictive Analytics for Emergency Department Patient Flow in Regards to Incoming Rate, Admission, and Leaving Behaviour PDF Download
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Author: Harish Kumar Manchukonda
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
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Book Description
In this work, we produce several prediction models for aspects of hospital emergency departments. Firstly, we demonstrate the use of a recurrent neural network to predict the rate of patient arrival at a hospital emergency department. The prediction is made on a per hour basis using date, time, calendar, and weather information. Then, we present our comparison of two prediction systems on the task of replicating the human decisions of patient admittance in a typical American emergency department. Again, a recurrent neural network (RNN) was trained to learn the task of selecting the next patient from the waiting room/queue to be admitted for treatment. Lastly, we present our attempt to produce a regression model that can predict the likelihood that a given patient will leave after waiting a specific amount of time in the emergency department’s waiting-room/queue. Such a model could be used to optimize the patient’s waiting-room/queue of an ED to minimize the likelihood of patients leaving without receiving care..
Author: Harish Kumar Manchukonda
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
Get Book Here
Book Description
In this work, we produce several prediction models for aspects of hospital emergency departments. Firstly, we demonstrate the use of a recurrent neural network to predict the rate of patient arrival at a hospital emergency department. The prediction is made on a per hour basis using date, time, calendar, and weather information. Then, we present our comparison of two prediction systems on the task of replicating the human decisions of patient admittance in a typical American emergency department. Again, a recurrent neural network (RNN) was trained to learn the task of selecting the next patient from the waiting room/queue to be admitted for treatment. Lastly, we present our attempt to produce a regression model that can predict the likelihood that a given patient will leave after waiting a specific amount of time in the emergency department’s waiting-room/queue. Such a model could be used to optimize the patient’s waiting-room/queue of an ED to minimize the likelihood of patients leaving without receiving care..
Author: Shanshan Qiu
Publisher:
ISBN:
Category : Hospitals
Languages : en
Pages : 132
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Book Description
ABSTRACT IMPROVING EMERGENCY DEPARTMENT PATIENT FLOW THROUGH NEAR REAL-TIME ANALYTICS This dissertation research investigates opportunities for developing effective decision support models that exploit near real-time (NRT) information to enhance the "operational intelligence" within hospital Emergency Departments (ED). Approaching from a systems engineering perspective, the study proposes a novel decision support framework for streamlining ED patient flow that employs machine learning, statistical and operations research methods to facilitate its operationalization. ED crowding has become the subject of significant public and academic attention, and it is known to cause a number of adverse outcomes to the patients, ED staff as well as hospital revenues. Despite many efforts to investigate the causes, consequences and interventions for ED overcrowding in the past two decades, scientific knowledge remains limited in regards to strategies and pragmatic approaches that actually improve patient flow in EDs. Motivated by the gaps in research, we develop a near real-time triage decision support system to reduce ED boarding and improve ED patient flow. The proposed system is a novel variant of a newsvendor modeling framework that integrates patient admission probability prediction within a proactive ward-bed reservation system to improve the effectiveness of bed coordination efforts and reduce boarding times for ED patients along with the resulting costs. Specifically, we propose a cost-sensitive bed reservation policy that recommends optimal bed reservation times for patients right during triage. The policy relies on classifiers that estimate the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after.
Author: John M. Shiver
Publisher: CRC Press
ISBN: 1420084976
Category : Business & Economics
Languages : en
Pages : 264
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Book Description
Across the country ambulances are turned away from emergency departments (EDs) and patients are waiting hours and sometimes days to be admitted to a hospital room. Hospitals are finding it hard to get specialist physicians to come to treat emergency patients. Our EDs demand a new way of thinking. They are not at a tipping point; they are at a break
Author: Jody Crane, MD, MBA
Publisher: CRC Press
ISBN: 1498774539
Category : Business & Economics
Languages : en
Pages : 388
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Book Description
This revised and updated book explores the academics behind managing the complex service environment that is the Emergency Department (ED) by combining applied management science and practical experiences to create a model of how to improve operations. This book offers a presentation of Lean tools used in the ED along with basic and advanced flow principles. It then shows how these concepts are applied and why they work, supported by case studies in which Lean principles were used to transform an underperforming ED into a world-class operation. After reviewing best practices, the authors explain how to achieve excellence by discussing the elements of creating a culture of change.
Author: Jody Crane MD MBA
Publisher: CRC Press
ISBN: 1439895384
Category : Business & Economics
Languages : en
Pages : 344
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Book Description
In a unique and integrated approach, The Definitive Guide to Emergency Department Operational Improvement: Employing Lean Principles with Current ED Best Practices to Create the "No Wait" Department exposes you to the academics behind managing the complex service environment that is the ED. The book combines applied management science and ED experi
Author: Azade Tabaie
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 23
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Book Description
Emergency Departments (EDs) in hospitals are experiencing severe crowding and prolonged patient waiting times. The reported crowding in hospitals shows patients in hospital hallways, long waiting times and full occupancy of ED beds. ED crowding has several potential unfavorable effects including patients and staff frustration, lower patient satisfaction and poor health outcomes. The primary motivations behind this study are shortening the patient's waiting time and improving patient satisfaction and level of care. The very initial interaction between clinicians and a patient is recorded on nurse triage notes which contain details of the reason for patient's visit including specific symptoms and incidents. Triage notes and vital signs measured by triage nurse determine the complexity of the patient's condition. If a minor illness or injury occurred, patient would be treated by nurse practitioners under ED physician's supervision. This process called fast track system which allows the main ED area to focus on more severe patient condition. The final decision should be made by physicians so patients have to wait to be seen in order to find out whether they need to be admitted in the hospital or be discharged. In this study, we propose a decision support system based on nurse triage notes and vital signs that can automatically predict ICD9 code assigned to each patient prior to the visit time. We tested the model on 8000 patient records from VA Medical Center in Detroit for ICD9 classification and measured performance in terms of accuracy.
Author: Awung Nkeze Elvis
Publisher: GRIN Verlag
ISBN: 3346929698
Category : Medical
Languages : en
Pages : 21
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Book Description
Academic Paper from the year 2023 in the subject Health - Public Health, , language: English, abstract: The aim of this paper is to pinpoint the root causes, consequences, and remedies of emergency department overpopulation. Hospital and emergency department (ED) overcrowding is a serious problem that has an impact on patient treatment and results. For the purpose of improving hospital capacity and patient flow, it is imperative to comprehend the causes of congestion. An overview of the studies on overcrowding and its effects on healthcare delivery is given in this article. These techniques can ease hospital overcrowding, improve patient flow, and decrease wait times. Additionally, recognizing and controlling hospital overpopulation has showed promise when using data analytics and predictive modeling. Hospitals can proactively allocate resources, change personnel levels, and manage patient flow to avoid congestion by studying previous data and forecasting future demand. This approach has been shown to improve patient outcomes, reduce wait times, and enhance overall hospital efficiency. Understanding overcrowding factors, bed capacity deficits, is crucial for effective strategies. Optimizing bed capacity, patient flow, and data analytics improves hospital care quality and reduces overcrowding.
Author: Jordan Shefer Peck
Publisher:
ISBN:
Category :
Languages : en
Pages : 187
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Book Description
A health care delivery chain is a series of treatment steps through which patients flow. The Emergency Department (ED)/Inpatient Unit (IU) chain is an example chain, common to many hospitals. Recent literature has suggested that predictions of IU admission, when patients enter the ED, could be used to initiate IU bed preparations before the patient has completed emergency treatment and improve flow through the chain. This dissertation explores the merit and implications of this suggestion. Using retrospective data collected at the ED of the Veterans Health Administration Boston Health Care System (VHA BHS), three methods are selected for making admission predictions: expert opinion, naive Bayes conditional probability and linear regression with a logit link function (logit-linear regression). The logit-linear regression is found to perform best. Databases of historic data are collected from four hospitals including VHA BHS. Logit-linear regression prediction models generated for each individual hospital perform well based on multiple measures. The prediction model generated for the VHA BHS hospital continues to perform well when predictive data are collected and coded prospectively by nurses. For two weeks, predictions are made on each patient that enters the VHA BHS ED. This data is then summarized and displayed on the VHA BHS internet homepage. No change was observed in key ED flow measures; however, interviews with hospital staff exposed ways in which the prediction information was valuable: planning individual patient admissions, personal scheduling, resource scheduling, resource alignment, and hospital network coordination. A discrete event simulation of the system shows that if IU staff emphasizes discharge before noon, flow measures improve as compared to a baseline scenario where discharge priority begins at 1pm. Sharing ED crowding or prediction information leads to best patient flow performance when using specific schedules dictating IU response to the information. This dissertation targets the practical and theoretical implications of using prediction to improve flow through the ED/IU health care delivery chain. It is suggested that the results will have impact on many other levels of health care delivery that share the delivery chain structure.
Author: Karen S. Zander
Publisher: HC Pro, Inc.
ISBN: 1601460465
Category : Hospitals
Languages : en
Pages : 195
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Book Description
Eliminate sky-high wait times and increased denials in your ED Hospitals across the country are seeing extreme backup in the emergency department (ED). In recent years, well-structured emergency department case management programs have repeatedly demonstrated their value in: Influencing capacity Assigning patients to appropriate levels of care Targeting complex discharge needs Assisting with proper utilization review Whether you are looking to implement, maintain, or evolve your ED case management program, eliminate confusion surrounding the process with the help of Emergency Department Case Management: Strategies for Creating and Sustaining a Successful Program. Examining all perspectives of ED case management, this new and comprehensive guide will help you define a program that best suits your facility's needs. All the tools you need to get your program up and running From defining goals, clarifying roles, and understanding the necessary knowledge and skill sets required from ED case management staff, Emergency Department Case Management will help to ensure that you have a solid and sustainable foundation in place. After exploring models and reviewing your infrastructure, Emergency Department Case Management will help you outline key partnerships, present multiple options for case finding, tackle observation status, address quality and evaluation issues, and identify ways ED case managers can contribute to care coordination for complex pediatric, psychiatric, homeless, and uninsured populations. Written by Kathleen Walsh, RN, MS, and Karen Zander RN, MS, CMAC, FAAN, from the Center for Case Management, Emergency Department Case Management provides advice and best practices from two of the nations top case management experts. Take a look inside at the table of contents: Chapter 1: ED Case Management: The Heart of Access Chapter 2: The Foundation Chapter 3: Partnerships Chapter 4: The Process Chapter 5: Developing interventional strategies Chapter 6: Observation status determination Chapter 7: Program-level evaluation Chapter 8: Information system support Chapter 9: Quality Chapter 10: Addressing the pediatric population Chapter 11: Responding to the psychiatric population Chapter 12: Strengthening an existing program It's also packed with 15 detailed case studies discussing ED case management strategies, as well as five spotlight accounts detailing the experiences of ED professionals from across the country, including: A case manager A social worker A psychiatric nurse An information systems specialist An ED physician Don't hesitate to jumpstart your ED case management program. From beginning to end, Emergency Department Case Management will serve as the lead architect to help you design, build, and strengthen your ED case management model--order your copy today! Learning objectives: Conceptualize a framework for setting up an ED case management program Develop policies, procedures, and role descriptions Identify structural components, tools, and processes to support an ED case management program Describe potential outcomes of an ED case management program Who should buy this book? Emergency Department Case Management is the perfect resource for case managers, directors of case management, ED nurse managers, social workers, ED directors/administrators, and CFOs. HCPro Inc. has confirmed that none of the faculty presenters or contributors has any relevant financial relationships to disclose related to the content of this educational activity.The HCPro Risk-Free, Money-Back Guarantee If for any reason you're not completely satisfied with your purchase, return it within 30 days and you will receive a prompt, polite, 100% refund--no questions asked.
Author: Davida Kellam MSN
Publisher: Independently Published
ISBN: 9781650305509
Category :
Languages : en
Pages : 37
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Book Description
Over the past several years there has been an overwhelming amount of patients coming to the emergency department for medical care. It is essential for all emergency departments to improve patient flow by identifying barriers that slow the patient flow down and improve and revise these areas. The research study was done to identify multiple areas causing issues with patient flow through emergency departments.
