2,579 research outputs found

    Hospital Car Parking: The Impact of Access Costs

    Get PDF
    NHS Trusts have statutory powers to raise income, which allow them to decide whether to charge, and how much to charge, for hospital car parking. Trusts are not obliged to provide parking facilities on their premises, but provision will inevitably incur costs in the form of maintenance, security and staffing. If Trusts choose not to charge for parking, then these costs must be covered from other sources of revenue, potentially diverting resources from patient care. Charges typically account for around 0.25% of a hospital?s income, but can be as high as 1%. The government offers financial support to people on low incomes who incur travel expenses when accessing health care.

    Optimising cardiac services using routinely collected data and discrete event simulation

    Get PDF
    Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces

    The Design and Evaluation of a Clinical Process Mapping Methodology (CPMM) to Support Information Systems (IS) Innovation in a Healthcare Context

    Get PDF
    This paper discusses the development, and assesses the appropriateness, of a Clinical Process Mapping Methodology (CPMM) to support information systems (ISs) innovation in acute hospitals. It is based on an ongoing longitudinal study in acute academic teaching hospitals in Ireland. The key rationale underpinning the research was that any attempt to develop ISs to support or change clinical work, must be based on a sophisticated, holistic and granular understanding of existing practices. Drawing on the insights gleaned through this observational study, an initial CPMM was developed by adapting elements from existing modelling languages to fit the clinical context in question. Our observations highlight the complex, collaborative and contingent nature of clinical practice, and the important mediating role played by technical and non-technical artefacts. This complexity would caution against viewing modelling as a panacea, which can be used to map the world in an objective or unproblematic manner. While modelling can be very helpful for facilitating new perspectives on work, and for facilitating productive collective sensemaking processes, it should be borne in mind that all models are purposeful, and necessarily partial, representations of the \u27real\u27 world. This underlines the importance of using any modelling approach in a discriminating and reflective way

    A hybrid system dynamics, discrete event simulation and data envelopment analysis to investigate boarding patients in acute hospitals

    Get PDF
    Timely access to health services has become increasingly difficult due to demographic change and aging people growth. These create new heterogeneous challenges for society and healthcare systems. Congestion at acute hospitals has reached unprecedented levels due to the unavailability of acute beds. As a consequence, patients in need of treatment endure prolonged waiting times as a decision whether to admit, transfer, or send them home is made. These long waiting times often result in boarding patients in different places in the hospital. This threatens patient safety and diminishes the service quality while increasing treatment costs. It is argued in the extant literature that improved communication and enhanced patient flow is often more effective than merely increasing hospital capacity. Achieving this effective coordination is challenged by the uncertainties in care demand, the availability of accurate information, the complexity of inter-hospital dynamics and decision times. A hybrid simulation approach is presented in this paper, which aims to offer hospital managers a chance at investigating the patient boarding problem. Integrating ‘System Dynamic’ and ‘Discrete Event Simulation’ enables the user to ease the complexity of patient flow at both macro and micro levels. ‘Design of Experiment’ and ‘Data Envelopment Analysis’ are integrated with the simulation in order to assess the operational impact of various management interventions efficiently. A detailed implementation of the approach is demonstrated on an emergency department (ED) and Acute Medical Unit (AMU) of a large Irish hospital, which serves over 50,000 patients annually. Results indicate that improving transfer rates between hospital units has a significant positive impact. It reduces the number of boarding patients and has the potential to increase access by up to 40% to the case study organization. However, poor communication and coordination, human factors, downstream capacity constraints, shared resources and services between units may affect this access. Furthermore, an increase in staff numbers is required to sustain the acceptable level of service delivery

    Pediatric In-Patient Days Predictive Model

    Get PDF
    The study objective is to develop a predictive model for pediatric inpatient days based on ambulatory outpatient visits and emergency department visits. This model aims to study the relationship between ambulatory visits and inpatient days, and determine if in-patient days can be predicted based on sub-specialty practice. Such a model does not currently exist, and when created and validated such a model could be utilized for various important management decisions, including refined insight into inpatient capacity and operational efficiency for self-governing children’s hospitals with large sub-specialty practices. The data set was a sample of convenience from one health system in the PEDSnet database. The requested data set yielded 3,832,428 distinct records, inclusive of all billed encounters for January through December 2017. Multi-regression analysis was used to predict variations in weekly occupied days over time. Ordinary least squared regression model results were used to examine the predictive power of outpatient variables. This enabled comparison of beta values for as many combinations of predictors as possible, in an efficient manner and yielded 80 models. The conclusion was that big data from one children’s hospital within a children’s health system was able to predict in-patient occupancy for greater than 50% of the variance
    • …
    corecore