Multi-objective Optimization of Hospital Inpatient Bed Assignment

Choosing which bed to assign an admitted patient to in a hospital is a complex problem. There are numerous factors to consider including the patient’s gender and isolation requirements, current bed availability, and unit configurations. This problem must be solved each time a new patient seeks admission resulting in rearrangement of already admitted patients. Each movement of an already admitted patient increases the workload for hospital staff and also increases the risk of nosocomial infections for the patient. In order to alleviate these problems we propose optimizing the patient admission process through a multi-objective model which first maximizes the overall criticality of patients admitted, then minimizes movements of previously admitted patients while creating space for incoming patients. Using this model we perform three sets of experiments. The first experiments seek to determine the ideal number of private and semi-private rooms in a multi-occupancy unit with a fixed number of total rooms. This results in a tool to enable the unit to manage the tradeoffs between moving previously admitted patients and bed utilization. The second experiments seek to determine the ideal timeframe over which to batch patient admissions. These results suggest more frequent admissions have minimal impact on inpatient rearrangement. The third experiments seek to determine the potential benefit of using a centralized admitting entity and finds managing bed assignment from a central perspective far out performs individual units managing their bed assignments

Flexible nurse staffing based on hourly bed census predictions

Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which are both inherently variable. Predicting this workload and staffing nurses accordingly is essential for guaranteeing quality of care in a cost effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies which allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center (AMC) Amsterdam. This case study demonstrates the method's potential to study the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, and surgical block planning. Inspired by the numerical results, the AMC decided that this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations during the upcoming years

A Semi-Private Revival: Is There a Place for the Semi-Private Patient Room in American Hospitals?

Is there a place for the semi-private patient room in contemporary American hospitals? This thesis purposes that the semi-private room remains a viable option for some patient populations and care scenarios and merits reconsideration and redesign. This inquiry is among few in the literature devoted to advancing semi-private patient room design and application within contemporary healthcare. The semi-private room may yet offer increased opportunities to address pressing issues of increasing healthcare costs, improving operational efficiencies, and reducing environmental footprint while simultaneously attempting to address both longstanding and recent industry concerns with traditional semi-private patient rooms. Further, it will serve as an opportunity to better understand the benefits and limitations for both the private and semi-private patient room models as there remains limited empirical evidence in support of all private rooms in all circumstances. This study aims to uncover the possible advantages and disadvantages of the semi-private patient room in relation to the private model in order to inform and establish criteria for evaluating and executing applicable and successful semi-private patient room design. Private patient rooms prevail as the preferred model of care in new hospital construction in the United States today. The 2006 AIA Guidelines for the Design and Construction of Health Care Facilities mandates the construction of all private rooms \u27unless the functional program demonstrates the necessity of a two-bed arrangement\u27 (Burnette, 2006). As a result, the semi-private patient room has become nearly obsolete, neglecting its potential benefits on the basis of limited empirical evidence in support of the private patient room. This re-evaluation of the semi-private patient room seeks to assess its remaining relevance in certain acute care patient populations today, such as pediatrics, oncology, or whole organ transplant patients. An extensive literature review was conducted in the beginning of this process to verify the assertions and assumptions made at the outset of this thesis and to ground the study in research and empirical evidence. In order to establish a framework of comparison, several case studies of recently constructed healthcare facilities that utilized the semi-private patient room model were identified. The selected facilities, located in London, United Kingdom, Deventer and Groningen in The Netherlands, were visited in the summer of 2009 and a thoughtful analysis of each was conducted during and upon returning from this tour. An assumption that not all patient populations would be appropriate for sharing a patient room led an investigation to identify which patient care scenarios might benefit from a semi-private room model. Patient populations were evaluated based upon characteristics, such as acuity, diagnosis and average length of stay. Several scenarios were recognized and deemed most qualified as being appropriate conditions for sharing a patient room, such as pediatric patients, rehabilitation patients and transplant patients. To guide the redesign of the semi-private patient room a set of design guidelines intended to address the particular needs of staff, families and patients sharing a room were identified and modeled after the pattern structure used in Christopher Alexander\u27s A Pattern Language. The final product of this study is a re-conceptualized and redesigned semi-private patient room that encompasses the attributes that make the private patient room a viable model for care, while providing for a more cost and operationally efficient room model with a smaller environmental footprint. To ground this study in an existing care context, the Medical University of South Carolina (MUSC) was selected as the site and operational context for the redesigned semi-private patient room. MUSC is the only hospital in South Carolina that services whole organ and bone marrow transplant patients, therefore the renal transplant inpatient unit was chosen as an appropriate patient care scenario for the redesigned room. The intention of the redesigned room is that it would satisfy, to the fullest extent possible, the design guidelines originally established to inform an improved, more patient-centered and operationally supportive shared patient room model for the contemporary American healthscape

Guidelines for preventing the transmission of Mycobacterium tuberculosis in health-care facilities, 1994

TB Infection-Control Guidelines Work Group: Carmine J. Bozzi, Dale R. Burwen, Samuel W. Dooley, Patricia M. Simone, Consuelo Beck-Sague, Elizabeth A. Bolyard, William R. Jarvis, Robert T. Hughes, Linda S. Martin, Robert J. Mullan, Brian M. Willis.Reprinted from Morbidity and mortality weekly report, CDC recommendations and reports, October 28, 1994, volume 43, number RR-13, pages 1-132"--cover.This document updates and replaces all previously published guidelines for the prevention of Mycobacterium tuberculosis transmission in health-care facilities. The purpose of this revision is to emphasize the importance of a) the hierarchy of control measures, including administrative and engineering controls and personal respiratory protection; b) the use of risk assessments for developing a written tuberculosis (TB) control plan; c) early identification and management of persons who have TB; d) TB screening programs for health-care workers (HCWs); e) HCW training and education; and f) the evaluation of TB infection-control programs.Includes bibliographical references (p. 106-112) and index.8602125Infectious DiseasePrevention and ControlSupersededInfectious DiseasePrevention and ControlSupersede

U.S. Army Medical Command’s Medical Treatment Facilities’ Response to SARS-CoV-2 (COVID-19)

Starting in December 2019 to the current time in May 2022, COVID-19 was a devastating pandemic with approximately 440 million cases and 6 million deaths worldwide (Centers for Disease Control and Prevention [CDC], 2021). The United States (US) with roughly 90 million cases and 1 million deaths (CDC, 2021) was one of the epicenters of the outbreak since the beginning. The pandemic has significantly impacted the health systems across the US with unpredictable surges of highly infectious patients with uncertain symptomology and acuity levels, requiring isolation and critical level of care (Brambilla et al., 2021). Based on the findings from the available literature and case reports of the pandemic impacts and responses, it is clear that the pandemic has put unprecedented pressure on US healthcare facilities, which are not intentionally designed to respond to a pandemic of this scale. Hospitals have struggled to adapt to the increased care complexity, infection control requirements, and the sheer volume of patients (Cohen et al., 2021). The need for such adaptability in the healthcare system has never been clearer as we have observed major deficiencies in how facilities have responded to the pandemic and how the buildings have failed to facilitate and support the required changes in spaces and operations

Recommendations for change in infection prevention programs and practice

Fifty years of evolution in infection prevention and control programs have involved significant accomplishments related to clinical practices, methodologies, and technology. However, regulatory mandates, and resource and research limitations, coupled with emerging infection threats such as the COVID-19 pandemic, present considerable challenges for infection preventionists. This article provides guidance and recommendations in 14 key areas. These interventions should be considered for implementation by United States health care facilities in the near future

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