Hospital Bed Capacity Research Articles

Predictors of nurses' turnover intention at one and five years' time.

This study examined the role of job dimensions, job satisfaction, psychological stress and job burnout in predicting turnover intention at one and five years' time among professional nurses in the Philippines. A cross-sectional design was utilized in the study using a convenience sample of 549 registered nurses from six hospitals in the Central Philippines during the period of October 2018 to January 2019. Five self-report scales were used in this study. Overall, 46.1% (n=253) and 78.9% (n=433) of nurses reported planning to leave the organization at one and five years' time, respectively. The type of hospital, job satisfaction and job burnout explained variances for turnover intention at one and five years' time. Separately, hospital bed capacity and job stress predicted turnover intention at five years' time. None of the job dimensions accurately predicted turnover intention in nurses. Higher turnover intention results at one and five years' time were dependent on nurses' psychological stress, job burnout and job satisfaction. Organizational measures aimed at reducing stress and burnout and increasing job satisfaction in nurses are critically important to enhance the retention of nurses and ensure continued quality of nursing care.

Coronavirus Disease 2019 (COVID-19): Protecting Hospitals From the Invisible.

Coronavirus disease 2019 (COVID-19) is optimized to spread widely: Its signs and symptoms are largely indistinguishable from those of other respiratory viruses. This commentary specifically addresses best ways to protect our hospitals against COVID-19.

Models and methods for determining the optimal number of beds in hospitals and regions: a systematic scoping review

BackgroundDetermining the optimal number of hospital beds is a complex and challenging endeavor and requires models and techniques which are sensitive to the multi-level, uncertain, and dynamic variables involved. This study identifies and characterizes extant models and methods that can be used to determine the required number of beds at hospital and regional levels, comparing their advantages and challenges.MethodsA systematic search was conducted using Web of Science, Scopus, Embase and PubMed databases, with the search terms hospital bed capacity, hospital bed need, hospital, bed size, model, and method.ResultsTwenty-three studies met the criteria to be included in the review. Of these studies, a total of 11 models and 5 methods were identified, mainly designed to determine hospital bed capacity at the regional level. Common determinants of the required number of hospital beds in these models included demographic changes, average length of stay, admission rates, and bed occupancy rates.ConclusionsThere are no specific norms for the required number of beds at hospital and regional levels, but some of the identified models and methods may be used to estimate this number in different contexts. Moreover, it is important to consider alternative approaches to planning hospital capacity like care pathways to fix the limitations of “bed numbers”.

An Analysis of the Spread of COVID-19 and its Effects on Indonesia’S Economy: A Dynamic Simulation Estimation

The coronavirus disease COVID-19 has spread all over the world, including Indonesia, and as the rate increases each day, the death toll also rise. However, the effects of the outbreak on the economy has not been calculated especially in developing countries. Therefore, the aim of this study is to analyze the effects of the disease on Indonesia’s economy. Hence, A System Dynamics method was used, and the results showed there are four scenarios which are worst, bad, moderate, and desirable. Furthermore, in order to ensure economic growth of 3.5%, a vaccine needs to be discovered by August 2020, recovery period of 7 days, ventilator efficiency of 70%, hospital bed capacity of 51,161, and social distancing needs to be practiced. Therefore, these results can be useful for policy makers to handle the pandemic.

Guidance for diabetic foot management during COVID-19 pandemic

The Indian COVID-19 situation is and will demand more and more hospital bed capacity to manage. With diabetic foot disease (DFD) being a leading cause of hospital bed occupancy, managing these patients based on evidence-based guidelines can significantly reduce the rates of hospitalization. Every hospital bed that is not needed for a patient with DFD , gives room for occupancy by a patient suffering from COVID-19. The goal of doctors treating diabetic foot is aimed at early successful treatment of infections and preventing amputations, decreasing the hospital stay of inpatients, and effective cost reduction. Hence, changing our way of approach to managing a patient with diabetic foot and implementing new and unique ways is the need of the hour at this time of crisis. This guidance also has a section on managing diabetes in people with diabetic foot during the COVID19 pandemic.

Identifying priorities for primary care investment in Ireland through a population-based analysis of avoidable hospital admissions for ambulatory care sensitive conditions (ACSC)

BackgroundIn 2016, the Irish acute hospital system operated well above internationally recommended occupancy targets. Investment in primary care can prevent hospital admissions of ambulatory care sensitive conditions (ACSCs).ObjectiveTo measure the...

Redesain Rumah Sakit Umum Daerah (RSUD) Banyumas BerdasarkanPedoman Perancangan Rumah Sakit Peraturan Kementerian Kesehatan

<p class="Abstract"><em>A hospital is a health care institution providing patient treatment, with a complex building system. In Indonesia, there is a regulation for hospital building system set by The Republic of Indonesia’s Minister of Health arranging laws on hospital zoning plan, patient flow management, room requirement and size spesification, and structural and utilities system plan. Banyumas Dictrict Hospital is classified as type B academic hospital established in 1925. In order to develop hospital facilities and increase hospital bed capacity, Banyumas District Hospital demands area expansion and new buildings construction. This periodical development leads tol building system problems as zoning, hospital entrance and accessibility, and building flow system. Banyumas District Hospital is in need of post-occupancy-evaluation which is used as redeseign method, with the intention of restoring hospital to the regulation on hospital building by Minister of Health. Redesign result realized through the processing siteplan design, building design, and material application.</em></p>

2850. Burden of Difficult-to-Treat Antibiotic-Resistant (DTR) Gram-Negative Infections in the United States, 2012–2017

BackgroundDifficult-to-treat resistance (DTR) is a metric for clinically relevant “pan-resistance” to available high-efficacy, low-toxicity antibiotic treatment options at any given time. Previous DTR prevalence estimates in Gram-negative (GN) bloodstream isolates from 2009 to 2014 have ranged between 1 and 1.5%. We sought to estimate the national burden of DTR GN isolates and more recent trends by region, site, and species.MethodsClinical cultures with GN isolates were identified from inpatient encounters in hospitals reporting at least one culture with susceptibility testing for a given month to Premier Healthcare Database or Cerner Health Facts Database from 2012 to 2017. DTR was defined as intermediate susceptibility or resistance to all tested carbapenems, other β-lactams, and fluoroquinolones, but not including agents introduced 2014 onwards. For each year, a raking procedure generated weights to extrapolate the sample estimate to match American Hospital Association distributions based on US census division, hospital bed capacity, teaching status, and urban designation. A weighted means survey procedure was used to extrapolate the sample estimate to obtain national DTR burden. Trends in DTR incidence were examined by using weighted multivariable logistic regression.ResultsExtrapolating from a 373-hospital sample, the estimated 2017 US inpatient burden of DTR isolates was 3,315 (1.3%) among sterile-site and 31,509 (1.7%) among all cultures, ranging from 0.5% to 3.3% in Mountain and New England regions respectively. P. aeruginosa was the most common species overall (37%), while A. baumannii was most common among sterile sites (31%). Between 2012 and 2017, there was no annual percent change in DTR incidence for sterile sites [OR 0.99 (0.93, 1.06)] but for all cultures it decreased 4.1% annually [OR 0.95 (0.91, 0.99)], including 9% annually for A. baumannii [OR 0.905 (0.860, 0.953)] and K. pneumonia [OR 0.903 (0.824, 0.991)], respectively.ConclusionThe US inpatient burden of GN isolates displaying DTR is relatively low, varies by region, and has remained stable or declined slightly in recent years. Periodic inclusion of emerging antibiotics in the DTR classification will allow for a dynamic index between resistance and available agents. Disclosures All Authors: No reported Disclosures.

545. Incidence of Carbapenem Non-Susceptible Acinetobacter spp. and Carbapenem-Resistant Pseudomonas aeruginosa Clinical Cultures among Patients in US Acute Care Hospitals, 2012–2017

BackgroundCarbapenem-nonsusceptible Acinetobacter spp. (CNAB) and carbapenem-resistant Pseudomonas aeruginosa (CRPA) are recognized causes of severe and difficult to treat healthcare-associated infections. This study estimated and compared the incidence of CNAB and CRPA among patients admitted to US acute care hospitals in 2012–2017.MethodsWe measured the incidence of positive clinical cultures from inpatient encounters in a cohort of over 300 hospitals submitting data to the Premier Healthcare Database and Cerner Health Facts in 2012–2017. We included clinical cultures from any body site yielding Acinetobacter spp./P. aeruginosa non-susceptible/resistant to imipenem, meropenem, or doripenem. Cultures collected on days 1–3 of hospitalization were considered community-onset (CO) and cultures from later were hospital-onset (HO). Duplicate isolates identified within 14 days of an incident culture and surveillance cultures were excluded. For each year, a raking procedure generated weights to extrapolate the sample estimate to match the American Hospital Association distributions based on US census division, hospital bed capacity, teaching status, and urban designation. We compared estimated rates in 2017 vs. 2012 using weighted multivariable logistic regression adjusting for hospital characteristics and hospital-level clustering.ResultsIn 2017, the estimated rates of HO and CO CNAB rates were 0.77 and 1.39/10,000 discharges, and HO and CO CRPA rates were 3.14 and 6.57, respectively. Compared with 2017, rates of HO CNAB decreased 49% (Odds Ratio (OR) 0.51; 95% CI: 0.34–0.75) and rates of CO CNAB decreased 29% (OR 0.71; 95% CI: 0.54–0.92). For CRPA, the incidence of HO decreased (OR 0.66; CI: 0.49–0.88) with no change in CO rates (OR 0.93; CI: 0.79–1.11). Assessment of cultures from sterile sites alone showed similar results, but they did not reach statistical significance, Figure 1.ConclusionWe estimate significant national decreases in the rates of HO and CO CNAB, and HO CRPA. Risk factors and effective interventions to reduce CO CRPA might differ from CNAB and HO CRPA. Additional prevention strategies are needed to address CO CRPA. Disclosures All authors: No reported disclosures.

Modeling Patient Flow among Hospital Wards Using Non-Diagnostic Data

Hospital bed capacity is a limited resource and a key concern in health care planning. Using discrete-event simulation modeling and the MIMIC-III data set, this paper produces a demographic and metadata-only model of patient transfer within hospital wards. The model successfully approximated the underlying transfer dynamics (95.63% accuracy measured using RMSE). The accompanying visualization may be used to examine patient flow. The simulation will be used as a test bed for future work concerning flow of artificially generated patient admissions and can in general be useful in simulating patient flow in cases where demographic information is available but transfer records are not.

Structural Changes in the Hungarian Healthcare System Between 2000 and 2017

BackgroundThe rigid and old-fashioned structure of the Hungarian healthcare system has been discussed since the mid-1990s and is at the center of professional and policy debates. It is characterized by the too high number of acute care hospital beds in international comparison; access is regionally unequal; levels of progressive care are mixed; and there is a nonuniform emergency service system with unequal access to the emergency room, heterogeneous quality of care, and unexploited opportunities of modern health technology (eg, 1-day surgery, minimally invasive procedures, telemedicine). ObjectivesThe aim of this study is to analyze the indicators of ongoing structural changes of the Hungarian healthcare system between 2000 and 2017. MethodsData are derived from the Organisation for Economic Co-operation and Development Heath Statistics, Hungarian National Statistical Office, National Health Insurance Fund Administration and the database of the European Structural Funds. The methods used for the analysis are descriptive statistics, trend analysis, and longitudinal data. ResultsThe total number of hospitals beds showed a 32% reduction between 2005 and 2017. Parallel with this subsequent reduction of hospital bed capacities, we can see a moderate reduction (22.3%) in the number of discharged patients from hospitals: from 2005 to 2017, 2.55 million to 1.95 million. The average length of stay in acute hospital care has decreased from 6.3 to 5.1 days. About 25 to 27 small local hospitals lost their acute or short-term care profile (mainly intensive care units, internal medicine, surgery, and pediatric care wards) and became long-term care, chronic care, or rehabilitation profile hospitals. ConclusionStructural change is in progress in the Hungarian healthcare system, and some efficiency gains have been reached. Nevertheless, still there are significant potential efficiency gains in the better organization and management of health services in addition to the dissemination and better incorporation of modern healthcare technologies.

The impact of an ageing population on the required hospital capacity: results from forecast analysis on administrative data.

In this study, we evaluate the impact of population ageing on the required hospital capacity. We used hospital discharge (years 2003-2014) and population data to estimate the required hospital capacity by 2025 for older inpatients (≥ 75years) taking into account population changes and trends in hospital admission rates and length of stay. In addition, we developed an alternative scenario to evaluate the impact of accelerated ageing based on the peaks in population ageing from 2030 onwards. The number of inpatient stays for our study population is expected to increase from 478,027 in 2014 to 590,313 in 2025 (+ 23.5%). The average length of stay is expected to decrease by 18.4% (- 2.3days). As a consequence, the number of inpatient days and the required bed capacity will only increase by 42,709 days (+ 0.7%) and 72 beds (+ 0.4%), respectively. The accelerated ageing scenario shows that the increase between 2014 and 2025 is more pronounced for inpatient stays (+ 50.5%), inpatient days (+ 21.9%) and hospital beds (+ 21.1%). Ageing will, if no drastic policy actions are taken, impact the required hospital capacity. This can initially (by 2025) be more or less controlled by further reductions in length of stay. From 2030, it is expected that the required hospital bed capacity will increase exponentially with a pronounced shift between general acute care beds towards geriatric and chronic care beds. If policy makers want to revert this trend, substantial investments in hospital alternatives will be required.

Operational patient-bed assignment problem in large hospital settings including overflow and uncertainty management

Managing patient to bed allocations is an everyday task in hospitals which in recent years has moved into focus due to a general rise in occupancy levels and the resulting need to efficiently manage tight hospital bed-capacities. This holds true especially when being faced with high volatility and uncertainty regarding patient arrivals and lengths of stay. In our work with a large German hospital we identified three main stakeholders, namely patients, nurses, and doctors, whose individual objectives and constraints regarding patient-bed allocation (PBA) lead to a potential trade-off situation. We developed a decision support model that tackles the PBA problem considering this trade-off, while also being capable of handling overflow situations. In addition, we anticipate emergency patient arrivals based on historical probability distributions and account for uncertainty regarding patient arrival and discharge dates. We develop a greedy look-ahead heuristic which allows for generating solutions for large real-life operational planning situations involving high ratios of emergency patients. We demonstrate the performance of our heuristic approach by comparison with the results of a near-optimal solution achieved by Gurobi’s MIP solver. Finally, we tested our approach using data sets from the literature as well as actual clinic data from our case study hospital, for which we were able to reduce overflow by over 96% while increasing overall utilization by 5%.

Medicaid's Institutions for Mental Diseases (IMD) Exclusion Rule: A Policy Debate-Argument to Retain the IMD Rule.

Medicaid's Institutions for Mental Diseases (IMD) Exclusion Rule: A Policy Debate-Argument to Retain the IMD Rule.

Predictors and outcomes of nurse professional autonomy: A cross-sectional study.

This paper is a report examining the level of professional autonomy as well as its predictors and outcomes among practicing nurses in the Philippines. This study adopted a descriptive, cross-sectional research design. One hundred sixty-six (n=166) nurses participated in the study or a response rate of 83% during the months of January 2017 to May 2017. Seven self-reported scales were used for this study: the Nurse Autonomy Scale, Job Stress Scale, Burnout Measure Scale, Job Satisfaction Index, Organizational Commitment Questionnaire, Turnover Intention Inventory Scale, and the Six Dimension Scale of Nursing Performance. Inferential and descriptive statistics were used to analyse the collected data. Nurses demonstrated moderate levels of professional autonomy with education and hospital bed capacity as strong predictors. Regression analysis showed positive effects of professional autonomy on nurses' job outcomes such as organizational commitment, job satisfaction, and work performance. Consistent to international studies, nurses in the Philippines demonstrated moderate levels of professional autonomy. Nurses who had higher levels of autonomy tended to be high performing, satisfied, and committed in their jobs. Organizational efforts are critically important to foster autonomy in practicing nurses through adequate support, education, training, and developed policies.

How many beds? Capacity implications of hospital care demand projections in the Irish hospital system, 2015-2030.

Existing Irish hospital bed capacity is low by international standards while Ireland also reports the highest inpatient bed occupancy rate across OECD countries. Moreover, strong projected population growth and ageing is expected to increase demand for hospital care substantially by 2030. Reform proposals have suggested that increased investment and access to nonacute care may mitigate some increased demand for hospital care over the next number of years, and it is in this context that the Irish government has committed to increase the supply of public hospital beds by 2600 by 2027. Incorporating assumptions on the rebalancing of care to nonhospital settings, this paper analyses the capacity implications of projected demand for hospital care in Ireland to 2030. This analysis employs the HIPPOCRATES macrosimulation projection model of health care demand and expenditure developed in the ESRI to project public and private hospital bed capacity requirements in Ireland to 2030. We examine 6 alternative projection scenarios that vary assumptions related to population growth and ageing, healthy ageing, unmet demand, hospital occupancy, hospital length of stay, and avoidable hospitalisations. We project an increased need for between 4000 and 6300 beds across public and private hospitals (an increase of between 26.1% and 41.1%), of which 3200 to 5600 will be required in public hospitals. These findings suggest that government plans to increase public hospital capacity over the 10years to 2027 by 2600 may not be sufficient to meet demand requirements to 2030, even when models of care changes are accounted for.

The U.S. Medicare Disproportionate Share Hospital program and capacity planning

U.S. Medicare's Disproportionate Share Hospital (DSH) program provides financial assistance to hospitals that serve low-income populations. Statutory formulas determine the DSH payments made to hospitals; however, the formulas applied to “large” urban hospitals (at least 100 beds) are more generous than those used for “small” urban hospitals (less than 100 beds). The purpose of this study is to determine if the DSH program's 100-bed threshold influences bed capacity planning. We find that the threshold drives a significant discontinuity in the distribution of urban hospital bed capacities, with hospitals tending to maintain just enough beds to qualify for higher DSH payments. The magnitude of the discontinuity is greatest in hospitals with strong incentives to manage bed capacity; and, compared to government and not-for-profit hospitals, for-profit hospitals are more likely to manage capacity to meet the 100-bed threshold. Our findings highlight the potential for regulatory institutions to have unintended influence on capacity planning decisions.

Perceptions of organizational support and its impact on nurses' job outcomes.

Strong organizational support can promote a sense of well-being and positive work behaviors in nurses. However, despite the importance of organizational support in nursing, this topic remains unexplored in the Philippines. The aim of this study was to examine the impact of organizational support perceptions on nurses' work outcomes (organizational commitment, work autonomy, work performance, job satisfaction, job stress, and turnover intention). A descriptive, cross-sectional research design was adopted in this study to collect data from one hundred eighty (180) nurses in the Philippines during the months of September 2015 to December 2015. Seven standardized tools were used: the Job Satisfaction Index, the Job Stress Scale, the Burnout Measure Scale, the Work Autonomy Scale, the Six Dimension Scale of Nursing Performance, the Turnover Intention Inventory Scale, and the Perception of Organizational Support Scale. Nurses employed in government-owned hospitals perceived low levels of organizational support as compared to private hospitals. Significant correlations were identified between perceived organizational support (POS), hospital bed capacity, and nurses' work status. No significant correlations were found between perceived organizational supportand the six outcomes perceived by nurses in the Philippines (organizational commitment, work performance, job autonomy, job satisfaction, job stress, and turnover intention). Perceptions of organizational support were low in Filipino nurses compared to findings in other international studies. Perceived organizational support did not influence job outcomes in nurses.

Neurosurgery in Ukraine: nowadays and perspectives

In 2016 in Ukraine 111 neurosurgery units with hospital bed capacity amounted to 3118 functioned. Over last 4 years (2013-2016) the level of hospitalization to neurosurgical units has increased by 7%, the volume of neurosurgery interventions rose by 9%. Surgical activity increased by 3.4%.Overall in Ukraine some reduction in general and post-operative lethality is observed.Whereas bed capacity in the SI Romodanov Neurosurgery Institute is 11.6% (361 out of 3118) of the total quantity of neurosurgery patient capacity the Institute performs two thirds of all high-technology (endoscopic, endovascular, laser interventions and those using neuro-navigation etc.) neurosurgery interventions with total and postoperative lethality indices 1.3% and 1.4% in 2017, respectively, which are the lowest indices ever the Institute has been existing and the history of nueroservice in the country. The number of high-technology interventions of IV and V degree of complexity out of total interventions in Neurosurgery Institute amounts to 72%.In 2017 the task of the Government on implementation of innovative mechanisms of financing the institutes of NAMS of Ukraine so called pilot project was brought off.The care at life threatening conditions (in different period from 50% to 74% hospitalized patients) – severe brain and spinal injuries, acute strokes is of top-priority for neurosurgery service.Limitedness of budget financing determines the three directions in the development of specialty and activity of the Institute: 1) determination of priorities with full costing of certain neurosurgical units at the expense of reduced budgeting of others; 2) non-budgetary funding on the basis of public-private partnership with self-financing structures development; 3) the Institute functioning as an autonomous budgetary establishment (access to credits, self-financing activity etc.) is most perspective.Certification of neurosurgical units, residency implementation by unified training module according to the recommendations of the European Association of Neurosurgical Societies (EANS) followed by licensing of professionals by the Ukrainian Association of Neurosurgeons and top neurosurgery physicians in the world are the necessary condition for improving secondary care.

Emergency department throughput: an intervention.

Shortening emergency department (ED) boarding time and managing hospital bed capacity by expediting the inpatient discharge process have been challenging for hospitals nationwide. The objective of this study is was to explore the effect of an innovative prospective intervention on hospital workflow, specifically on early inpatient discharges and the ED boarding time. The intervention consisted of a structured nursing "admission discharge transfer" (ADT) protocol receiving new admissions from the ED and helping out floor nursing with early discharges. ADT intervention was implemented in a 38-bed hospitalist run inpatient unit at an academic hospital. The study population consisted of 4486 patients (including inpatient and observation admissions) who were hospitalized to the medicine unit from March 2013-March 2014. Of these hospitalizations, 2259 patients received the ADT intervention. Patients' demographics, discharge and ED boarding data were collected for from March 4, 2013 to March 31, 2014 for both intervention and control groups (28weeks each). Chi-square and unpaired t tests were utilized to compare population characteristics. Poisson regression analysis was conducted to estimate the association between intervention and hospital length of stay adjusted for differences in patient demographics. Mean age of the study population was 58.6years, 23% were African Americans and 55% were women. A significant reduction in ED boarding time (p<0.001) and improvement in early (before 2PM) hospital discharges (p=0.01) were noticed among patients in the intervention groups. There was a slight but significant reduction in hospital length of stay for observation patients in the intervention group; however, no such difference was noted for inpatient admissions. Our study showed that dedicating nursing resources towards ED-boarded patients and early inpatient discharges can significantly improve hospital workflow and reduce hospital length of stay.