Optimizing Patient Flow

Optimizing patient flow : advanced strategies for managing variability to enhance access, quality, and safety offers readers innovate techniques for maximizing patient flow and improving operations management while providing clear examples of successful impementation. This all-new book can help health care organizations to reduce and manage variability, thereby increasing the reliablity of systems and processes and improving health care quality and safety.

Provides hospitals with scientifically grounded methods to optimally manage patient flow. This title features advanced tutorials to help you to: understand the problems in patient flow management; assess the quantitative impact of patient flow issues on patients and staff; and, use quantitative methods to enhance patient flow.

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 breaking point. Under current loads and trends they are going to begin to break and these breakdowns will be painful and ultimately dangerous to society. Recognizing that the ideal in health care is presently beyond our immediate grasp, this book instead focuses on providing health care leaders with the tools they can employ to optimize the performance of EDs and thereby improve service to patients, employees, and communities. Written by 20 of the most progressive and successful health care reformers in the country, the approaches described can be utilized to quantify improvements, enhance predictability of workflow, and improve staff scheduling. The data derived using these techniques can serve as powerful evidence in support of change. While a common discussion among ED professionals is the perception that many patients are not really emergency patients and could be treated in another setting at another time, that argument is not germane until we as a nation elect to reform the way we chose to deliver healthcare to the underserviced. In the meantime this book provides invalauable information to help individual hospitals to retool their ED’s. It offers new approaches that think outside of the box for all stakeholders. It also provides the statistical evidence that administrators need to make their cases for changes and added resources. It will help you forecast the demand for services and give your center an approach that will allow the ED to become a source of income rather than one that continues to hemorrhage needed limited health care funding.

Written for a global audience, by an international team, the book provides practical, case-based emergency department leadership skills.

This book is dedicated to improving healthcare through reducing delays experienced by patients. With an interdisciplinary approach, this new edition, divided into five sections, begins by examining healthcare as an integrated system. Chapter 1 provides a hierarchical model of healthcare, rising from departments, to centers, regions and the “macro system.” A new chapter demonstrates how to use simulation to assess the interaction of system components to achieve performance goals, and Chapter 3 provides hands-on methods for developing process models to identify and remove bottlenecks, and for developing facility plans. Section 2 addresses crowding and the consequences of delay. Two new chapters (4 and 5) focus on delays in emergency departments, and Chapter 6 then examines medical outcomes that result from waits for surgeries. Section 3 concentrates on management of demand. Chapter 7 presents breakthrough strategies that use real-time monitoring systems for continuous improvement. Chapter 8 looks at the patient appointment system, particularly through the approach of advanced access. Chapter 9 concentrates on managing waiting lists for surgeries, and Chapter 10 examines triage outside of emergency departments, with a focus on allied health programs Section 4 offers analytical tools and models to support analysis of patient flows. Chapter 11 offers techniques for scheduling staff to match patterns in patient demand. Chapter 12 surveys the literature on simulation modeling, which is widely used for both healthcare design and process improvement. Chapter 13 is new and demonstrates the use of process mapping to represent a complex regional trauma system. Chapter 14 provides methods for forecasting demand for healthcare on a region-wide basis. Chapter 15 presents queueing theory as a method for modeling waits in healthcare, and Chapter 16 focuses on rapid delivery of medication in the event of a catastrophic event. Section 5 focuses on achieving change. Chapter 17 provides a diagnostic for assessing the state of a hospital and using the state assessment to select improvement strategies. Chapter 18 demonstrates the importance of optimizing care as patients transition from one care setting to the next. Chapter 19 is new and shows how to implement programs that improve patient satisfaction while also improving flow. Chapter 20 illustrates how to evaluate the overall portfolio of patient diagnostic groups to guide system changes, and Chapter 21 provides project management tools to guide the execution of patient flow projects.

According to Transforming Health Care Scheduling and Access, long waits for treatment are a function of the disjointed manner in which most health systems have evolved to accommodate the needs and the desires of doctors and administrators, rather than those of patients. The result is a health care system that deploys its most valuable resource--highly trained personnel--inefficiently, leading to an unnecessary imbalance between the demand for appointments and the supply of open appointments. This study makes the case that by using the techniques of systems engineering, new approaches to management, and increased patient and family involvement, the current health care system can move forward to one with greater focus on the preferences of patients to provide convenient, efficient, and excellent health care without the need for costly investment. Transforming Health Care Scheduling and Access identifies best practices for making significant improvements in access and system-level change. This report makes recommendations for principles and practices to improve access by promoting efficient scheduling. This study will be a valuable resource for practitioners to progress toward a more patient-focused "How can we help you today?" culture.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Diagnosis-related groups (DRGs) are used in hospitals for the reimbursement of inpatient services. The assignment of a patient to a DRG can be distinguished into billing- and operations-driven DRG classification. The topic of this monograph is operations-driven DRG classification, in which DRGs of inpatients are employed to improve contribution margin-based patient scheduling decisions. In the first part, attribute selection and classification techniques are evaluated in order to increase early DRG classification accuracy. Employing mathematical programming, the hospital-wide flow of elective patients is modelled taking into account DRGs, clinical pathways and scarce hospital resources. The results of the early DRG classification part reveal that a small set of attributes is sufficient in order to substantially improve DRG classification accuracy as compared to the current approach of many hospitals. Moreover, the results of the patient scheduling part reveal that the contribution margin can be increased as compared to current practice.