Achieving Efficiency: Lessons From Four Top-Performing Hospitals

Synthesizes lessons from case studies of how four hospitals achieved greater efficiency, including pursuing quality and access, customizing technology, emphasizing communications, standardizing processes, and integrating care, systems, and providers

Intimate Partner Violence in Omaha

While the greater Omaha area has an extensive network of service providers addressing the needs of Intimate Partner Violence (IPV) survivors and has made great strides to increase collaboration and outreach, opportunities exist to enhance service delivery according to this report

A System Dynamics View of the Acute Bed Blockage Problem in the Irish Healthcare System

Global population ageing is creating immense pressures on hospitals and other healthcare services, compromising their abilities to meet the growing demand from elderly patients. Current demand–supply gaps result in prolonged waiting times in emergency departments (EDs), and several studies have focused on improving ED performance. However, the overcrowding in EDs generally stems from delayed patient flows to inpatient wards – which are congested with inpatients waiting for beds in post-acute facilities. This problem of bed blocking in acute hospitals causes substantial cost burdens on hospitals. This study presents a system dynamics methodology to model the dynamic flow of elderly patients in the Irish healthcare system aimed at gaining a better understanding of the dynamic complexity caused by the system\u27s various parameters. The model evaluates the stock and flow interventions that Irish healthcare executives have proposed to address the problem of delayed discharges, and ultimately reduce costs. The anticipated growth in the nation\u27s demography is also incorporated in the model. Policy makers can also use the model to identify the potential strategic risks that might arise from the unintended consequences of new policies designed to overcome the problem of the delayed discharge of elderly patients

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

Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa

The COVID-19 pandemic has had devastating consequences across the globe, and has led many governments into completely new decision making territory. Developing models which are capable of producing realistic projections of disease spread under extreme uncertainty has been paramount for supporting decision making by many levels of government. In South Africa, this role has been fulfilled by the South African COVID-19 Modelling Consortium's generalised Susceptible-ExposedInfectious-Removed compartmental model, known as the National COVID-19 Epi Model. This thesis adapted and contributed to the Model; its primary contribution has been to incorporate the feature that resources available to the health system are limited. Building capacity constraints into the Model allowed it to be used in the resource-scarce context of a pandemic. This thesis further designed and implemented a goal programming framework to shift ICU beds between districts intra-provincially in a way that aimed to minimise deaths caused by the non-availability of ICU beds. The results showed a 15% to 99% decrease in lives lost when ICU beds were shifted, depending on the scenario considered. Although there are limitations to the scope and assumptions of this thesis, it demonstrates that it is possible to combine mathematical modelling with optimisation in a way that may save lives through optimal resource allocation

Business Process Redesign in the Perioperative Process: A Case Perspective for Digital Transformation

This case study investigates business process redesign within the perioperative process as a method to achieve digital transformation. Specific perioperative sub-processes are targeted for re-design and digitalization, which yield improvement. Based on a 184-month longitudinal study of a large 1,157 registered-bed academic medical center, the observed effects are viewed through a lens of information technology (IT) impact on core capabilities and core strategy to yield a digital transformation framework that supports patient-centric improvement across perioperative sub-processes. This research identifies existing limitations, potential capabilities, and subsequent contextual understanding to minimize perioperative process complexity, target opportunity for improvement, and ultimately yield improved capabilities. Dynamic technological activities of analysis, evaluation, and synthesis applied to specific perioperative patient-centric data collected within integrated hospital information systems yield the organizational resource for process management and control. Conclusions include theoretical and practical implications as well as study limitations

Application of Discrete-Event Simulation for Planning and Operations Issues in Mental Healthcare

Mental health disorders are on the rise around the world. Inadequate service provision and lack of access have led to wide gaps between the need for treatment and service delivery. Despite the popularity of Discrete-event Simulation (DES) in healthcare planning and operations, there is evidence of limited application of DES in planning for mental healthcare services. This paper identifies and reviews all the papers that utilize DES modelling to address planning and operations issues in mental healthcare services. The aim is to contribute a roadmap for the future application of DES in mental healthcare services, with an emphasis on planning and operations

Application of Lean Thinking Using Simulation Modelling in a Private Hospital

Timely access, prompt responses to patient needs, and availability of resources to deliver quality service are the key priorities of healthcare systems, in particular hospitals. To cope with these constraints, healthcare managers have turned into lean thinking and approaches in their attempts to reduce non-value added activities and save costs by reducing wastes. This paper presents a case study of a private hospital in Dublin that used integrated approach of value stream mapping and simulation modeling to assess lean implementation in admission and discharge processes. Simulation enabled the strategic management to examine the outcomes of three possible improvement scenarios on hospital performance before implementing lean strategies. The proposed methodology helped to identify bottlenecks and non-value added procedures. Results analysis showed potential improvement in patients’ admission and discharge cycle times and offered the hospital the cost-saving opportunity of reducing the numbers of bed required

Malaria in Sri Lanka: Current knowledge on transmission and control

Malaria / Disease vectors / Waterborne diseases / Environmental effects / Public health / Economic impact / Social impact / Sri Lanka

Evaluating Michigan's community hospital access: spatial methods for decision support

BACKGROUND: Community hospital placement is dictated by a diverse set of geographical factors and historical contingency. In the summer of 2004, a multi-organizational committee headed by the State of Michigan's Department of Community Health approached the authors of this paper with questions about how spatial analyses might be employed to develop a revised community hospital approval procedure. Three objectives were set. First, the committee needed visualizations of both the spatial pattern of Michigan's population and its 139 community hospitals. Second, the committee required a clear, defensible assessment methodology to quantify access to existing hospitals statewide, taking into account factors such as distance to nearest hospital and road network density to estimate travel time. Third, the committee wanted to contrast the spatial distribution of existing community hospitals with a theoretical configuration that best met statewide demand. This paper presents our efforts to first describe the distribution of Michigan's current community hospital pattern and its people, and second, develop two models, access-based and demand-based, to identify areas with inadequate access to existing hospitals. RESULTS: Using the product from the access-based model and contiguity and population criteria, two areas were identified as being "under-served." The lower area, located north/northeast of Detroit, contained the greater total land area and population of the two areas. The upper area was centered north of Grand Rapids. A demand-based model was applied to evaluate the existing facility arrangement by allocating daily bed demand in each ZIP code to the closest facility. We found 1,887 beds per day were demanded by ZIP centroids more than 16.1 kilometers from the nearest existing hospital. This represented 12.7% of the average statewide daily bed demand. If a 32.3 kilometer radius was employed, unmet demand dropped to 160 beds per day (1.1%). CONCLUSION: Both modeling approaches enable policymakers to identify under-served areas. Ultimately this paper is concerned with the intersection of spatial analysis and policymaking. Using the best scientific practice to identify locations of under-served populations based on many factors provides policymakers with a powerful tool for making good decisions