Dataset Information

The Safety of Hospital Beds: Ingress, Egress, and In-Bed Mobility.

ABSTRACT: To explore the safety of the standard and the low hospital bed, we report on a microanalysis of 15 patients' ability to ingress, move about the bed, and egress. The 15 participants were purposefully selected with various disabilities. Bed conditions were randomized with side rails up or down and one low bed with side rails down. We explored the patients' use of the side rails, bed height, ability to lift their legs onto the mattress, and ability to turn, egress, and walk back to the chair. The standard bed was too high for some participants, both for ingress and egress. Side rails were used by most participants when entering, turning in bed, and exiting. We recommend that side rails be reconsidered as a means to facilitate in-bed movement, ingress, and egress. Furthermore, single deck height settings for all patients are not optimal. Low beds as a safety measure must be re-evaluated.

SUBMITTER: Morse JM

PROVIDER: S-EPMC5371163 | biostudies-literature | 2015 Jan-Dec

REPOSITORIES: biostudies-literature

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The Safety of Hospital Beds: Ingress, Egress, and In-Bed Mobility.

Morse Janice M JM Gervais Pierre P Pooler Charlotte C Merryweather Andrew A Doig Alexa K AK Bloswick Donald D

Global qualitative nursing research 20150101

To explore the safety of the standard and the low hospital bed, we report on a microanalysis of 15 patients' ability to ingress, move about the bed, and egress. The 15 participants were purposefully selected with various disabilities. Bed conditions were randomized with side rails up or down and one low bed with side rails down. We explored the patients' use of the side rails, bed height, ability to lift their legs onto the mattress, and ability to turn, egress, and walk back to the chair. The s ...[more]

PMID: 28462302

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Project description:BACKGROUND:Following the regional outbreak in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, presenting the healthcare systems with huge challenges worldwide. In Germany the coronavirus diseases 2019 (COVID-19) pandemic has resulted in a slowly growing demand for health care with a sudden occurrence of regional hotspots. This leads to an unpredictable situation for many hospitals, leaving the question of how many bed resources are needed to cope with the surge of COVID-19 patients. OBJECTIVE:In this study we created a simulation-based prognostic tool that provides the management of the University Hospital of Augsburg and the civil protection services with the necessary information to plan and guide the disaster response to the ongoing pandemic. Especially the number of beds needed on isolation wards and intensive care units (ICU) are the biggest concerns. The focus should lie not only on the confirmed cases as the patients with suspected COVID-19 are in need of the same resources. MATERIAL AND METHODS:For the input we used the latest information provided by governmental institutions about the spreading of the disease, with a special focus on the growth rate of the cumulative number of cases. Due to the dynamics of the current situation, these data can be highly variable. To minimize the influence of this variance, we designed distribution functions for the parameters growth rate, length of stay in hospital and the proportion of infected people who need to be hospitalized in our area of responsibility. Using this input, we started a Monte Carlo simulation with 10,000 runs to predict the range of the number of hospital beds needed within the coming days and compared it with the available resources. RESULTS:Since 2 February 2020 a total of 306 patients were treated with suspected or confirmed COVID-19?at this university hospital. Of these 84 needed treatment on the ICU. With the help of several simulation-based forecasts, the required ICU and normal bed capacity at Augsburg University Hospital and the Augsburg ambulance service in the period from 28 March 2020 to 8 June 2020 could be predicted with a high degree of reliability. Simulations that were run before the impact of the restrictions in daily life showed that we would have run out of ICU bed capacity within approximately 1 month. CONCLUSION:Our simulation-based prognosis of the health care capacities needed helps the management of the hospital and the civil protection service to make reasonable decisions and adapt the disaster response to the realistic needs. At the same time the forecasts create the possibility to plan the strategic response days and weeks in advance. The tool presented in this study is, as far as we know, the only one accounting not only for confirmed COVID-19 cases but also for suspected COVID-19 patients. Additionally, the few input parameters used are easy to access and can be easily adapted to other healthcare systems.

Project description:BackgroundUnderstanding the factors that influence hospital mobility, especially in the context of a heightened focus on falls prevention, is needed to improve care.ObjectiveThis qualitative study uses a socioecological framework to explore factors that influence hospital mobility in older adults.DesignQualitative research PARTICIPANTS: Semi-structured interviews and focus groups were conducted with medically-ill hospitalized older adults (n = 19) and providers (hospitalists, nurses, and physical and occupational therapists (n = 48) at two hospitals associated with an academic health system.ApproachInterview and focus group guides included questions on perceived need for mobility, communication about mobility, hospital mobility culture, and awareness of patients' walking activity. Data were analyzed thematically and mapped onto the constructs of the socioecological model.Key resultsA consistent theme among patients and providers was that "mobility is medicine." Categories of factors reported to influence hospital walking activity included intrapersonal factors (patients' health status, fear of falls), interpersonal factors (patient-provider communication about mobility), organizational factors (clarity about provider roles and responsibilities, knowledge of safe patient handling, reliance on physical therapy for mobility), and environmental factors (falls as a never event, patient geographical locations on hospital units). Several of these factors were identified as potentially modifiable targets for intervention. Patients and providers offered recommendations for improving awareness of patient's ambulatory activity, assigning roles and responsibility for mobility, and enhancing education and communication between patients and providers across disciplines.ConclusionPatients and providers identified salient factors for future early mobility initiatives targeting hospitalized older adults. Consideration of these factors across all stages of intervention development and implementation will enhance impact and sustainability.

Project description:BackgroundRoemer's Law, a widely cited principle in health care policy, states that hospital beds that are built tend to be used. This simple but powerful expression has been invoked to justify Certificate of Need regulation of hospital beds in an effort to contain health care costs. Despite its influence, a surprisingly small body of empirical evidence supports its content. Furthermore, known geographic factors influencing health services use and the spatial structure of the relationship between hospital bed availability and hospitalization rates have not been sufficiently explored in past examinations of Roemer's Law. We pose the question, "Accounting for space in health care access and use, is there an observable association between the availability of hospital beds and hospital utilization?"MethodsWe employ an ecological research design based upon the Anderson behavioral model of health care utilization. This conceptual model is implemented in an explicitly spatial context. The effect of hospital bed availability on the utilization of hospital services is evaluated, accounting for spatial structure and controlling for other known determinants of hospital utilization. The stability of this relationship is explored by testing across numerous geographic scales of analysis. The case study comprises an entire state system of hospitals and population, evaluating over one million inpatient admissions.ResultsWe find compelling evidence that a positive, statistically significant relationship exists between hospital bed availability and inpatient hospitalization rates. Additionally, the observed relationship is invariant with changes in the geographic scale of analysis.ConclusionsThis study provides evidence for the effects of Roemer's Law, thus suggesting that variations in hospitalization rates have origins in the availability of hospital beds. This relationship is found to be robust across geographic scales of analysis. These findings suggest continued regulation of hospital bed supply to assist in controlling hospital utilization is justified.

Project description:BackgroundPredicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.MethodsWe obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.ResultsIn both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.ConclusionsWe identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.Trial registrationThe ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Dataset Information

The Safety of Hospital Beds: Ingress, Egress, and In-Bed Mobility.

Publications

The Safety of Hospital Beds: Ingress, Egress, and In-Bed Mobility.

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