Modeling COVID-19 hospital admissions and occupancy in the Netherlands.

We describe the models we built for predicting hospital admissions and bed occupancy of COVID-19 patients in the Netherlands. These models were used to make short-term decisions about transfers of patients between regions and for long-term policy making. For forecasting admissions we developed a new technique using linear programming. To predict occupancy we fitted residual lengths of stay and used results from queueing theory. Our models increased the accuracy of and trust in the predictions and helped manage the pandemic, minimizing the impact in terms of beds and maximizing remaining capacity for other types of care.

Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy.

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region. Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals' data warehouses and regional infection data as recorded in the Netherlands. The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

EFFECTIVENESS OF USING THE BED FUND AND HUMAN RESOURCES FOR PROVIDING MEDICAL CARE TO CHILDREN WITH RESPIRATORY DISEASES.

OBJECTIVE: The aim: Retrospectively evaluate the effectiveness of the use of beds and human resources for the treatment of children with respiratory diseases in hospitals in the period 2008-2021. PATIENTS AND METHODS: Materials and methods: We calculated indicators that characterize the efficiency of the use of bed and personnel resources: the density of beds per 10,000, the rate of hospitalized children per 10,000 (RH per 10,000), the bed occupancy rate per year (BOR), average length of stay (ALOS), full-time positions (FTP) per 100,000, number of beds per 1 FTP of doctors. RESULTS: Results: During 2008-2021, there was a significant decrease in the density of all types of beds. The percentage of hospitalized children for inpatient treatment decreased, BOR decreased, and ALOS decreased. The density of full-time positions of allergists increased by +23.78%, pediatricians by +4.86%, pulmonologists decreased by -13.15%. In 2021, there were 10.31 beds for 1 FTP of an allergist, 12.8 beds for 1 FTP of a pulmonologist, and 5.83 beds for 1 FTP of a pediatrician. According to the correlation matrix, it was established that the more beds there are for 1 full-time position of a pediatrician and 1 full-time position of an allergist, the longer the ALOS and the bed occupancy rate are. CONCLUSION: Conclusions: When planning staffing of health care institutions, it is necessary to mind the level of urbanization of the region, and ensure status of the general practitioner as a leading medical specialist responsible for medical care during the first meeting with the patient and his subsequent follow-up.

A pragmatic method to compare international critical care beds: Implications to pandemic preparedness and non-pandemic planning.

BACKGROUND: The current method for assessing critical care (CCU) bed numbers between countries is unreliable. METHODS: A pragmatic method is presented using a logarithmic relationship between CCU beds per 1000 deaths and deaths per 1000 population, both of which are readily available. The method relies on the importance of the nearness to death effect, and on the effect of population size. RESULTS: The method was tested using CCU bed numbers from 65 countries. A series of logarithmic relationships can be seen. High versus low countries can be distinguished by adjusting all countries to a common crude mortality rate. Hence at 9.5 deaths per 1000 population 'high' CCU bed countries average of around 30 CCU beds per 1000 deaths, while 'very low' countries only average 3 CCU beds per 1000 deaths. The United Kingdom falls among countries with low critical care provision with an average of 8 CCU beds per 1000 deaths, and during the COVID-19 epidemic UK industry intervened to rapidly manufacture various types of ventilators to avoid a catastrophe. CCU bed numbers in India are around 8.1 per 1000 deaths, which places it in the low category. However, such beds are inequitably distributed with the poorest states all in the 'very low' category. In India only around 50% of CCU beds have a ventilator. CONCLUSION: A feasible region is defined for the optimum number of CCU beds.

Multimodal Quality Improvement Intervention With Dedicated Patient Flow Manager to Reduce Emergency Department Length of Stay and Occupancy: Interrupted Time Series Analysis.

INTRODUCTION: Crowding in the emergency department is a problem worldwide that can affect patient safety and clinical outcomes. The aim of this project was to evaluate a multimodal quality improvement intervention with a new patient flow manager to reduce ED length of stay and ED bed occupancy. METHODS: This single-site interrupted time-series analysis study was conducted in a tertiary hospital emergency department in South Korea. Interventions for a novel system load-balancing approach included a data-driven patient flow tracking informatics system, adding medical specialists, point-of-care creatinine testing (when required before diagnostic imaging) with dedicated imaging test slots for emergency patients, and introducing patient flow managers. Records of adult patients visiting the emergency department from January 2016 to March 2020 were included. Outcomes were ED length of stay and ED bed occupancy. Regression discontinuity analysis of an interrupted time series was used adjusting for seasonality and the number of patients per staff. RESULTS: A total of 46,494 patients in the preintervention period and 151,802 patients in the postintervention period were included. After the intervention, ED length of stay decreased by 4.07 hours, whereas the slope indicated a return to preintervention levels over time. Monthly average ED bed occupancy decreased by 34.6%, and the slope remained consistent over time. DISCUSSION: The multimodal quality improvement intervention that included a patient flow manager was an effective intervention to reduce the ED length of stay and the ED bed occupancy at the study site. The change for length of stay may not sustain over time without further intervention.

[The medical care support of children population by "phthisiology" profile in The Russian Federation: The comparison of normative, factual and estimated rate of hospitalization].

In 1960s, the fundamental normative planning research of health care needs differentiated according age, medical care and its resource support depending on various profiles including "phthisiology" has been carried out regularly. After the budget insurance model of medical care was implemented, the attention to renewal of normative base decreased that resulted in significant regional disproportions between planned (normative) and factual volumes of medical care, including its application to children population on profile "phthisiology" in hospital conditions. THE PURPOSE OF THE STUDY: To compare normative, factual and estimated rate of hospitalization to provide medical care of children population on profile "phthisiology" in hospitals of the subjects of The Russian Federation. MATERIALS AND METHODS: Such methods as analysis of statistical information, normative and analytic technique, method of ratios and proportions, correlation analysis were applied. THE RESULTS: The data on number of patients with active tuberculosis were used to estimate need in mentioned medical care by 14 groups of patients that made up to 0.2 cases per 1000 children that is three times less than factual (0.6) and four times less than normative (0,8) indices. In the comparison groups, deficiency of factual vs. normative volumes of medical care increases as group morbidity increases. However, there are no signs of unmet needs in medical care. Thus, as bed occupancy rate is below approved level in all study groups. There is no correlation between bed occupancy rate and factual vs. normative admission rates ratio (Kendall's tau_b=0,178, р=0,101). CONCLUSION: The mismatch between factual and normative admission rates on profile "phthisiology" demonstrates both uneven provision of medical care in the subjects of The Russian Federation and overestimation of approved (normative) medical care that is four times higher than the estimated rate. To validate the obtained results special study of health care in question is needed with focused on primary data combined with expert assessment of validity of hospitalization.

Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units.

This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital's data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital's control centre and is used in several Dutch hospitals during the second COVID-19 peak.

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

BACKGROUND: Predicting 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. METHODS: We 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. RESULTS: In 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. CONCLUSIONS: We 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 REGISTRATION: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Likelihood of admission to hospital from the emergency department is not universally associated with hospital bed occupancy at the time of admission.

BACKGROUND: The decision to admit into the hospital from the emergency department (ED) is considered to be important and challenging. The aim was to assess whether previously published results suggesting an association between hospital bed occupancy and likelihood of hospital admission from the ED can be reproduced in a different study population. METHODS: A retrospective cohort study of attendances at two Swedish EDs in 2015 was performed. Admission to hospital was assessed in relation to hospital bed occupancy together with other clinically relevant variables. Hospital bed occupancy was categorized and univariate and multivariate logistic regression were performed. RESULTS: In total 89,503 patient attendances were included in the final analysis. Of those, 29.1% resulted in admission within 24 h. The mean hospital bed occupancy by the hour of the two hospitals was 87.1% (SD 7.6). In both the univariate and multivariate analysis, odds ratio for admission within 24 h from the ED did not decrease significantly with an increasing hospital bed occupancy. CONCLUSIONS: A negative association between admission to hospital and occupancy level, as reported elsewhere, was not replicated. This suggests that the previously shown association might not be universal but may vary across sites due to setting specific circumstances.

Clinical characteristics and outcomes of critically ill patients with COVID-19 in Kobe, Japan: a single-center, retrospective, observational study.

PURPOSE: Coronavirus disease 2019 (COVID-19) has placed a great burden on critical care services worldwide. Data regarding critically ill COVID-19 patients and their demand of critical care services outside of initial COVID-19 epicenters are lacking. This study described clinical characteristics and outcomes of critically ill COVID-19 patients and the capacity of a COVID-19-dedicated intensive care unit (ICU) in Kobe, Japan. METHODS: This retrospective observational study included critically ill COVID-19 patients admitted to a 14-bed COVID-19-dedicated ICU in Kobe between March 3, 2020 and June 21, 2020. Clinical and daily ICU occupancy data were obtained from electrical medical records. The last follow-up day was June 28, 2020. RESULTS: Of 32 patients included, the median hospital follow-up period was 27 (interquartile range 19-50) days. The median age was 68 (57-76) years; 23 (72%) were men and 25 (78%) had at least one comorbidity. Nineteen (59%) patients received invasive mechanical ventilation for a median duration of 14 (8-27) days. Until all patients were discharged from the ICU on June 5, 2020, the median daily ICU occupancy was 50% (36-71%). As of June 28, 2020, six (19%) died during hospitalization. Of 26 (81%) survivors, 23 (72%) were discharged from the hospital and three (9%) remained in the hospital. CONCLUSION: During the first months of the outbreak in Kobe, most critically ill patients were men aged ≥ 60 years with at least one comorbidity and on mechanical ventilation; the ICU capacity was not strained, and the case-fatality rate was 19%.

The effect of discharge planning on length of stay and readmission rates of older adults in acute hospitals: A systematic review and meta-analysis of systematic reviews.

AIM: To examine the effectiveness of discharge planning on length of stay and readmission rates among older adults in acute hospitals. BACKGROUND: Discharge planning takes place in all acute hospital settings in many forms. However, it is unclear how it contributes to reducing patient length of stay in hospital and readmission rates. METHODS: Seven systematic reviews were identified and examined. All of the systematic reviews explored the impact of discharge planning on length of stay and readmission rates. RESULTS: A limited meta-analysis of the results in relation to length of stay indicates positive finding for discharge planning as an intervention (MD = -0.71(95% CI -1.05,-0.37; p = .0001)). However, further analysis of the broader findings in relation to length of stay indicates inconclusive or mixed results. In relation to readmission rates both meta-analysis and narrative analysis point to a reduced risk for older people where discharge planning has taken place (RR = 0.78 (95% CI: 0.72, 0.84; p = .00001)). The ability to synthesize results however is severely hampered by the diversity of approaches to research in this area. IMPLICATIONS FOR NURSING MANAGEMENT: It is unclear what impact discharge planning has on length of stay of older people. Indeed, while nurse mangers will be interested in gauging this impact on throughput and patient flow, it is questionable if length of stay is the correct outcome to measure when studying discharge planning as good discharge planning may increase length of stay. Readmission rates may be a more appropriate outcome measure but standardization of approach needs to be considered in this regard. This would assist nurse managers in assessing the impact of discharge planning processes.

Assessing the performance of hospitals before and after the implementation of Iran's Health Sector Evolution Plan (HSEP) using the Pabon Lasso model.

Background: The health sector evolution plan was implemented in 2014 in government hospitals across the country as a part of the universal health coverage achievement programs. This study assessed the performance of hospitals before and after the implementation of this plan, using the Pabon Lasso model. Methods: The population of this study consisted of the hospitals of the country in the 2013-2015 time frame; overall, 874 hospitals (94.5% of the population) were included in the study. In order to assess performance, we used the Pabon Lasso model and hospital performance indicators (Average Length of Stay, Bed Turnover, and Bed Occupancy Rate). The data were collected from the Hospital Information System and provincial deputies of curative affairs and were then analyzed using the descriptive indicators of mean, frequency, and median in SPSS 22. Also, Paired Student T-test and ANOVA were used to compare the performance of different groups of hospitals before and after the implementation of the health sector evolution plan. Results: The implementation of the health sector evolution plan has led to a significant improvement in the three performance indicators in the hospitals of the country. Before the implementation of the health sector evolution plan, the most inefficient, inefficient, fairly efficient, and most efficient zones included 31%, 18%, 17%, and 33% of the studied hospitals, respectively. However, the implementation of the health sector evolution plan changed the percentages to 29%, 21%, 20%, and 30%, respectively. Teaching hospitals, which are governmental and are mostly located in capital cities of the provinces, were overall more inefficient than non-teaching hospitals. Conclusion: The number of the most efficient and most inefficient hospitals has decreased, and the number of average performance hospitals has increased after the implementation of the health sector evolution plan. Therefore, the health sector evolution plan has not led to an overall increase or decrease in the performance of hospitals but has reduced the difference in the performance of hospitals. Equal support of government hospitals along with financial protection against health expenses, improves the performance indicators of hospitals and reduces performance differences among them.

Impact of climate variability on length of stay in hospital for childhood pneumonia in rural Bangladesh.

OBJECTIVES: Pneumonia is a significant contributor to mortality and morbidity in children aged <5 years, and it is also one of the leading causes of hospitalisation for children in this age group. This study assessed the association between climate variability, patient characteristics (i.e. age, sex, weight, parental education, socio-economic status) and length of stay (LOS) in hospital for childhood pneumonia and its economic impact on rural Bangladesh. STUDY DESIGN: An ecological study design was used. METHODS: Data on daily hospitalisation for pneumonia in children aged <5 years (including patient characteristics) and daily climate data (temperature and relative humidity) between 1st January 2012 and 31st December 2016 were obtained from the Matlab Hospital (the International Centre for Diarrhoeal Disease Research, Bangladesh) and the Bangladesh Meteorological Department, respectively. A generalised linear model with Poisson link was used to quantify the association between climate factors, patient characteristics and LOS in hospital. RESULTS: The study showed that average temperature, temperature variation and humidity variation were positively associated with the LOS in hospital for pneumonia. A 1°C rise in average temperature and temperature variation during hospital stay increased the LOS in hospital by 1% (relative risk [RR]: 1.010, 95% confidence interval [CI]: 1.001-1.018) and 9.3% (RR: 1.093, 95% CI: 1.051-1.138), respectively. A 1% increase in humidity variation increased the LOS in hospital for pneumonia by 2.2% (RR: 1.022, 95% CI: 1.004-1.039). In terms of economic impact, for every 1° C temperature variation during the period of hospital stay, there is an addition of 0.81 USD/day/patient as a result of direct costs and 1.8 USD/day/patient for total costs. Annually, this results in an additional 443 USD for direct and 985 USD for total costs. CONCLUSIONS: Climate variation appears to significantly contribute to the LOS in hospital for childhood pneumonia. These findings may help policymakers to develop effective disease management and prevention strategies.

A comparison of nurses' work satisfaction between single-room and multioccupancy adult intensive care units: A mixed-methods integrative review.

OBJECTIVES: To systematically review and compare the evidence for the transition from multi- occupancy adult intensive care units to single room intensive care units. REVIEW METHOD USED: A mixed methods systematic review informed by Joanna Briggs Institute guidelines for Systematic Reviews. DATA SOURCES: The databases CINAHL, Medline and Embase were searched for primary research articles relating from 2008-2019. REVIEW METHODS: The methodological quality of all studies that met the inclusion criteria were assessed using Mixed Methods Appraisal Tool (MMAT). The findings were synthesised into themes. RESULTS: 6349 records were identified, and four of those met the inclusion criteria and included in the review. Eight inter-related themes were revealed, which were teamwork, isolation, patient safety, proximity, staff education, satisfaction, staff morale and ambience. CONCLUSIONS: When planning transitions from multi-occupancy to single room ICU's, although patient safety, and patient and family privacy are paramount, consideration should be also given to the nurse work environment and work satisfaction.

COVID-19-Hospitalized Patients in Karnataka: Survival and Stay Characteristics.

The information on the clinical course of coronavirus disease 2019 (COVID-19) and its correlates which are essential to assess the hospital care needs of the population are currently limited. We investigated the factors associated with hospital stay and death for COVID-19 patients for the entire state of Karnataka, India. A retrospective-cohort analysis was conducted on 445 COVID-19 patients that were reported in the publicly available media-bulletin from March 9, 2020, to April 23, 2020, for the Karnataka state. This fixed cohort was followed till 14 days (May 8, 2020) for definitive outcomes (death/discharge). The median length of hospital stay was 17 days (interquartile range: 15-20) for COVID-19 patients. Having severe disease at the time of admission (adjusted-hazard-ratio: 9.3 (3.2-27.3);P < 0.001) and being aged ≥ 60 years (adjusted-hazard-ratio: 11.9 (3.5-40.6);P < 0.001) were the significant predictors of COVID-19 mortality. By moving beyond descriptive (which provide only crude information) to survival analyses, information on the local hospital-related characteristics will be crucial to model bed-occupancy demands for contingency planning during COVID-19 pandemic.

Converting acute inpatient take to outpatient take with fast-track assessment in internal medicine wards - a before-after study.

BACKGROUND: With an extensive rise in the number of acute patients and increases in both admissions and readmissions, hospitals are at times overcrowded and under immense pressure and this may challenge patient safety. This study evaluated an innovative strategy converting acute internal medicine inpatient take to an outpatient take. Here, acute patients, following referral, underwent fast-track assessment to the needed level of medical care as outpatients, directly in internal medicine wards. METHOD: The two internal medicine wards at Diagnostic Centre, Silkeborg, Denmark, changed their take of acute patients 1st of March 2017. The intervention consisted of acute medical patients being received in medical examination chairs, going through accelerated evaluation as outpatients with assessment within one hour for either admission or another form of treatment. A before-and-after study design was used to evaluate changes in activity. All referred patients for 10 months following implementation of the intervention were compared with patients referred in corresponding months the previous year. RESULTS: A total of 5339 contacts (3632 patients) who underwent acute medical assessment (2633 contacts before and 2706 after) were included. Median hospital length-of-stay decreased from 32.6 h to 22.3 h, and the proportion of referred acute patients admitted decreased with 36.3% points from 94.5 to 58.2%. The median length-of-admission time for the admitted patients increased as expected after the intervention. The risk of being admitted, being readmitted as well as having a hospital length-of-time longer than 24 h, 72 h or 7 days, respectively, were significantly lower during the after-period in comparison to the before-period. Adverse effects, unplanned re-contacts, total contacts to general practice and mortality did not change after the intervention. CONCLUSION: Assessing referred acute patients in medical examination chairs as outpatients directly in internal medicine wards and promoting an accelerated trajectory, reduced inpatient admissions and total length-of-stay considerably. This strategy seems effective in everyday acute medical patients and has the potential to ease the increasing pressure on the acute take for wards receiving acute medical patients.

Predictive model of length of stay in hospital among older patients.

BACKGROUND: Most National Health Service (NHS) hospital bed occupants are older patients because of their frequent admissions and prolonged length of stay (LOS). We evaluated demographic and clinical factors as predictors of LOS in a single NHS Trust and derived an equation to estimate LOS. METHODS: Stepwise logistic and linear regressions were used to predict prolonged LOS (upper-quintile LOS > 17 days) and LOS respectively, from demographic factors and acute and pre-existing conditions. RESULTS: Of 374 (men:women = 127:247) admitted patients (20% to orthogeriatric, 69% to general medical and 11% to surgical wards), median age of 85 years (IQR = 78-90), 77 had acute first hip fracture; 297 had previous hip fracture (median time since previous fracture = 2.4 years) and 21 (7.1%) had recurrent hip fracture, with median time since first fracture = 2.4 years. Median LOS was 6.5 days (IQR = 1.8-14.8), and 38 (10.2%) died after 4.8 days (IQR = 1.6-14.3). Prolonged LOS was associated with discharge to places other than usual residence: OR = 3.1 (95% CI 1.7-5.7), acute stroke: OR = 10.1 (3.7-26.7), acute first hip fractures: OR = 6.8 (3.1-14.8), recurrent hip fractures: OR = 9.5 (3.2-28.7), urinary tract infection/pneumonia: OR = 4.0 (2.1-8.0), other acute fractures: OR = 9.8 (3.0-32.3) and malignancy: OR = 15.0 (3.1-71.8). Predictive equation showed estimated LOS was 11.6 days for discharge to places other than usual residence, 15 days for pre-existing or acute stroke, 9-14 days for acute and recurrent hip fractures, infections, other acute fractures and malignancy; these factors together explained 32% of variability in LOS. CONCLUSIONS: A useful estimate of outcome and LOS can be made by constructing a predictive equation from information on hospital admission, to provide evidence-based guidance for resource requirements and discharge planning.

Recursive neural networks in hospital bed occupancy forecasting.

BACKGROUND: Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday planning. METHODS: We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May-September). RESULTS: An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets. CONCLUSIONS: The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.

Patterns of emergency department visits during Hajj period: Towards healthcare optimization in view of Saudi Arabia's vision 2030.

OBJECTIVE: This study aimed to examine the pattern of emergency department (ED) visits by Hajj patients and determine the urgency of emergency visits at an advanced healthcare center. METHODS: A retrospective review of medical records of Hajj patients visiting the ED at King Abdullah Medical City Makkah from September 1 to October 5, 2015 was conducted. RESULTS: We considered 233 visits by 199 Hajj patients. Most diseases were cardiovascular related. Approximately half of the ED visits led to hospital admission, which were largely during the evening and nighttime. Potentially avoidable visits were significantly encountered during the daytime. Average bed occupation time in the ED was similar for both cases: those admitted to inpatient care and discharged from ED. Results from the Canadian Triage and Acuity Scale revealed that most patients were triaged with a score of III (48.4%) followed by a clinically better score of IV (32%); however, scores did not change significantly throughout the Hajj day. CONCLUSIONS: During Hajj, a significant proportion of patients who visited the ED at the ultimate healthcare facility were discharged within 24 hours, with a higher rate in the morning-afternoon period. Both admitted and discharged cases required equal levels of care. Therefore, an extension in working days at primary care centers and optimization of advanced healthcare facilities during Hajj is currently warranted.

Caesarean section rate and postnatal bed occupancy: a retrospective study replacing assumptions with evidence.

BACKGROUND: Obstetric units across the UK face resource pressures alongside a rising rate of Caesarean section (CS). It is assumed that this places a further burden in the form of postnatal bed demands. The number of inpatient beds has fallen nationally, and this may be used to justify attempts to restrict the CS rate. We set out to replace such assumptions with evidence. We did not find any similar contemporary analysis in a literature search. METHODS: The postnatal length of stay (LOS) of women delivering at Watford General Hospital, a large unit hosting around 5500 deliveries per annum, was stratified by mode of delivery. Differences within and across time periods were analysed. RESULTS: The CS rate rose from 14.5% in 1995 to 30.9% in 2015. The mean LOS post-CS declined from 4.2 to 2.4 days. These data were statistically significant to p < 0.001. Over this period the standardised total postnatal bed use for all delivery modes fell from 11083 days to 7894 days. A 113% rise in the CS rate was accommodated by only a 19.8% rise in postnatal bed use attributable to CS patients. CONCLUSIONS: Whatever pressures may be exacerbated by the rising CS rate, bed occupancy is not one of them. In discussion we widen our argument to suggest that resource pressures should not be used to justify limitations in the CS rate.