Forecasting emergency department hourly occupancy using time series analysis.

STUDY OBJECTIVE: To develop a novel predictive model for emergency department (ED) hourly occupancy using readily available data at time of prediction with a time series analysis methodology. METHODS: We performed a retrospective analysis of all ED visits from a large academic center during calendar year 2012 to predict ED hourly occupancy. Due to the time-of-day and day-of-week effects, a seasonal autoregressive integrated moving average with external regressor (SARIMAX) model was selected. For each hour of a day, a SARIMAX model was built to predict ED occupancy up to 4-h ahead. We compared the resulting model forecast accuracy and prediction intervals with previously studied time series forecasting methods. RESULTS: The study population included 65,132 ED visits at a large academic medical center during the year 2012. All adult ED visits during the first 265 days were used as a training dataset, while the remaining ED visits comprised the testing dataset. A SARIMAX model performed best with external regressors of current ED occupancy, average department-wide ESI, and ED boarding total at predicting up to 4-h-ahead ED occupancy (Mean Square Error (MSE) of 16.20, and 64.47 for 1-hr- and 4-h- ahead occupancy, respectively). Our 24-SARIMAX model outperformed other popular time series forecasting techniques, including a 60% improvement in MSE over the commonly used rolling average method, while maintaining similar prediction intervals. CONCLUSION: Accounting for current ED occupancy, average department-wide ESI, and boarding total, a 24-SARIMAX model was able to provide up to 4 h ahead predictions of ED occupancy with improved performance characteristics compared to other forecasting methods, including the rolling average. The prediction intervals generated by this method used data readily available in most EDs and suggest a promising new technique to forecast ED occupancy in real time.

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain).

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients' hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes.

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.

Situação epidemiológica atual - 24 de setembro de 2020 / Current epidemiological situation - september 24, 2020

Apresenta panorama da pandemia COVID-19 no Estado da Bahia, em 24 de setembro de 2020. Descreve de forma detalhada a situação da pandemia no Estado, contempla informações relacionadas ao registro de casos notificados da COVID-19, taxa de crescimento, distribuição de casos confirmados nas Macrorregiões regiões de saúde, casos confirmados segundo raça/cor, ocupação de leitos de UTI, perfil dos casos de Síndrome Multissistêmica Pediátrica, número de curados, número de óbitos.

Theoretical bounds and approximation of the probability mass function of future hospital bed demand.

Failing to match the supply of resources to the demand for resources in a hospital can cause non-clinical transfers, diversions, safety risks, and expensive under-utilized resource capacity. Forecasting bed demand helps achieve appropriate safety standards and cost management by proactively adjusting staffing levels and patient flow protocols. This paper defines the theoretical bounds on optimal bed demand prediction accuracy and develops a flexible statistical model to approximate the probability mass function of future bed demand. A case study validates the model using blinded data from a mid-sized Massachusetts community hospital. This approach expands upon similar work by forecasting multiple days in advance instead of a single day, providing a probability mass function of demand instead of a point estimate, using the exact surgery schedule instead of assuming a cyclic schedule, and using patient-level duration-varying length-of-stay distributions instead of assuming patient homogeneity and exponential length of stay distributions. The primary results of this work are an accurate and lengthy forecast, which provides managers better information and more time to optimize short-term staffing adaptations to stochastic bed demand, and a derivation of the minimum mean absolute error of an ideal forecast.

The 85% bed occupancy fallacy: The use, misuse and insights of queuing theory.

Queuing theory can and has been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper, we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and 'treatment' rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision-making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool, reducing variation should be considered.

The implications of high bed occupancy rates on readmission rates in England: A longitudinal study.

Hospital bed occupancy rates in the English National Health Service have risen to levels considered clinically unsafe. This study assesses the association of increased bed occupancy with changes in the percentage of overnight patients discharged from hospital on a given day, and their subsequent 30-day readmission rate. Longitudinal panel data methods are used to analyse secondary care records (n = 4,193,590) for 136 non-specialist Trusts between April 2014 and February 2016. The average bed occupancy rate across the study period was 90.4%. A 1% increase in bed occupancy was associated with a 0.49% rise in the discharge rate, and a 0.011% increase in the 30-day readmission rate for discharged patients. These associations became more pronounced once bed occupancy exceeded 95%. When bed occupancy rates were high, hospitals discharged a greater proportion of their patients. Those were mostly younger and less clinically complex, suggesting that hospitals are successfully prioritising early discharge amongst least vulnerable patients. However, while increased bed occupancy was not associated with a substantial increase in overall 30-day readmission rates, the relationship was more pronounced in older and sicker patients, indicating possible links with short-fallings in discharge processes.

Anticipated advantages and disadvantages of a move to 100% single-room hospital in Australia: A case study.

OBJECTIVES: There is an international policy trend for building government hospitals with greater proportions of single-occupancy rooms. The study aim was to identify advantages and disadvantages for patients and nursing staff of a pending move to 100% single-room hospital, in anticipation of the challenges for nurse managers of a different ward environment. This paper presents these findings, summarizing potential advantages and disadvantages as well as comparison with findings from similar studies in England. METHODS: Mixed method case study design was undertaken in four wards of a large hospital with multi-bed rooms. Three components of a larger study are reported here: nurse surveys and interviews, patient interviews of their experiences of the current multi-bedroom environment and expectations of new single-room environment. Integration was achieved via data transformation where results of the nursing staff survey and interviews and patient interviews were coded as narrative allowing for quantitative and qualitative data to be merged. RESULTS: Four constructs were derived: physical environment; patient safety and comfort; staff safety; and importance of interaction. CONCLUSION: There are important factors that inform nurse managers when considering a move to an all single-room design. These factors are important for nurses' and patients' well-being. IMPLICATIONS FOR NURSING MANAGEMENT: This study identified for nurse managers key factors that should be considerd when contributing to the design of a 100% single-room hospital. Nurses' voices are critically important to inform the design for a safe and efficient ward environment.

Management decision of hospital surge: assessing seasonal upsurge in inpatient medical bed occupancy rate among public acute hospitals in Hong Kong.

BACKGROUND: There were recurrent upsurges in demand for public hospital services in Hong Kong. An understanding of the contribution of some possible factors for the rise in health care burden would help to inform hospital management strategies. AIM: To evaluate the utilization patterns of hospitalizations in medical wards among public acute hospitals in Hong Kong during surge periods. DESIGN: Retrospective study. METHODS: By extracting the information in press releases between 2014 and 2018, descriptive statistics about medical ward occupancy situation during six surge periods were generated. A time series model was constructed to estimate the occupancy rate at each hospital and assess its relationship with the intensity of seasonal influenza activity, extreme weather, day of week and long holidays. RESULTS: There was a significant increase in the number of admissions to medical wards in all six surge periods. A significant variation in occupancy rate between weekdays and geographic regions was observed. The occupancy rate in 10, out of 15, hospitals was significantly associated with the influenza activity, while there was limited effect of weather on the occupancy rate. A significant holiday effect was observed during Christmas and Chinese New Year, resulting in a lower bed occupancy rate. CONCLUSIONS: A differential burden in public hospitals during surge periods was reported. Contingency bed and staff management shall be tailored to individual hospitals, given their differences in the determinants for inpatient bed occupancy.

Invasive MRSA infections in neurosurgical patients - a decade of progress.

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is an important cause of bloodstream infection (BSI), which is declining in many countries, including Ireland. However, it also causes other invasive infections, such as meningitis in neurosurgical patients. It is unclear whether the decline in MRSA BSI is reflected in other invasive infections and in specialist units. AIM: To investigate trends in the incidence of MRSA invasive infection in a national neurosurgical centre over a 10-year period. METHODS: A retrospective review of neurosurgical patients with MRSA recovered from sterile sites and indicating invasive infection, according to internationally agreed definitions was conducted between January 2006 and December 2015. Rates per 10,000 bed days used (BDU) and neurosurgical bed days used (NBDU) were calculated and trends were analysed. RESULTS: Forty-four cases of invasive MRSA infection were identified over the study period. The majority were BSI (26, 59%) followed by ventriculitis (8, 18%). Invasive MRSA infections declined significantly from 0.52 per 10,000 BDU (or 4.65 per 10,000 NBU) in 2006 to 0.22 per 10,000 BDU (or 2.04 per 10,000 NBDU) in 2015, p < .01, despite an increase in neurosurgical clinical activity. Half of the infections occurred in patients with no previous history of MRSA colonisation/infection. The mean length-of-stay for neurosurgical patients with invasive MRSA infections was 67 days (median 32.5 days), significantly greater for other neurosurgical patients (p < .01). CONCLUSION: There has been a significant decrease in invasive MRSA infections in neurosurgical patients, reflecting national and international trends for MRSA BSI. This indicates that infection prevention and control measures have been effective in reducing invasive MRSA infections overall, thus contributing to improved patient care.

Predictors of surgical approach for the management of renal cell carcinoma: a population-based study from New South Wales.

BACKGROUND: The simultaneous adoption of laparoscopy and partial nephrectomy (PN) has been reported to result in unintended consequences. We aim to examine the contemporary (2001-2009) trends in use of PN and laparoscopy within a population-wide database. METHODS: All renal cell carcinomas diagnosed between 2001 and 2009 in New South Wales, Australia, were identified from the Central Cancer Registry. Records of treatment were electronically linked. All patients with localized renal cell carcinoma who underwent surgical treatment were selected for this study (3771 patients). Follow-up was to the end of 2010. Multivariable logistic regression analysis was used to determine predictors of PN and laparoscopy. RESULTS: In the entire cohort, 150 (4%), 495 (13%), 1516 (40%) and 1610 (43%) received laparoscopic PN, open PN, laparoscopic radical nephrectomy (RN) and open RN, respectively. Between the years 2001 and 2009, there was a gradual increase in the use of PN except between 2005 and 2007. Between 2001 and 2009, a rapid uptake of laparoscopy was observed. Multivariable analysis demonstrated that age (OR: 0.99 (0.98-0.99)), year of surgery (OR: 1.15 (1.11-1.19)), comorbidity (OR: 1.40 (1.12-1.76)) and hospital case load >8/year (OR: 2.39 (1.49-3.81)) predicted use of PN. Year of surgery (OR: 1.19 (1.15-1.21)), comorbidity (OR: 0.70 (0.58-0.85)), non-teaching hospital (OR: 0.70 (0.53-0.93)), private hospital (OR: 1.86 (1.45-2.38)) and hospital case load > 8/year (OR: 3.36 (2.22-5.09)) predicted use of laparoscopy. CONCLUSION: The unintended decrease in PN associated with increased use of laparoscopic RN had reversed by 2009. Hospital case load predicts the use of PN and laparoscopy.

Hospital-Level Changes in Adult ICU Bed Supply in the United States.

OBJECTIVES: Although the number of intensive care beds in the United States is increasing, little is known about the hospitals responsible for this growth. We sought to better characterize national growth in intensive care beds by identifying hospital-level factors associated with increasing numbers of intensive care beds over time. DESIGN: We performed a repeated-measures time series analysis of hospital-level intensive care bed supply using data from Centers for Medicare and Medicaid Services. SETTING: All United States acute care hospitals with adult intensive care beds over the years 1996-2011. PATIENTS: None. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We described the number of beds, teaching status, ownership, intensive care occupancy, and urbanicity for each hospital in each year of the study. We then examined the relationship between increasing intensive care beds and these characteristics, controlling for other factors. The study included 4,457 hospitals and 55,865 hospital-years. Overall, the majority of intensive care bed growth occurred in teaching hospitals (net, +13,471 beds; 72.1% of total growth), hospitals with 250 or more beds (net, +18,327 beds; 91.8% of total growth), and hospitals in the highest quartile of occupancy (net, +10,157 beds; 54.0% of total growth). In a longitudinal multivariable model, larger hospital size, teaching status, and high intensive care occupancy were associated with subsequent-year growth. Furthermore, the effects of hospital size and teaching status were modified by occupancy: the greatest odds of increasing ICU beds were in hospitals with 500 or more beds in the highest quartile of occupancy (adjusted odds ratio, 18.9; 95% CI, 14.0-25.5; p < 0.01) and large teaching hospitals in the highest quartile of occupancy (adjusted odds ratio, 7.3; 95% CI, 5.3-9.9; p < 0.01). CONCLUSIONS: Increasingly, intensive care bed expansion in the United States is occurring in larger hospitals and teaching centers, particularly following a year with high ICU occupancy.

Trends in elderly psychiatric admissions to the Brazilian public health care system

Objective: To evaluate trends in psychiatric bed occupancy by elderly inpatients in the Brazilian public health care system between 2000 and 2010 and to determine the leading psychiatric diagnosis for hospital admissions. Methods: Data from all 895,476 elderly psychiatric admissions recorded in the Brazilian Public Health Care Database (DATASUS) between January 2000 and February 2010 were analyzed. Polynomial regression models with estimated curve models were used to determine the trends. The number of inpatient days was calculated for the overall psychiatric admissions and according to specific diagnoses. Results: A moderate decreasing trend (p < 0.001) in the number of inpatient days was observed in all geriatric psychiatric admissions (R2 = 0.768) and in admissions for organic mental disorders (R2 = 0.823), disorders due to psychoactive substance use (R2 = 0.767), schizophrenia (R2 = 0.680), and other diagnoses (R2 = 0.770), but not for mood disorders (R2 = 0.472). Most admissions (60 to 65%) were due to schizophrenia. Conclusion: There was a decreasing trend in inpatient days for elderly psychiatric patients between 2000 and 2010. The highest bed occupancy was due to schizophrenia, schizotypal, and delusional disorders.

Trends in elderly psychiatric admissions to the Brazilian public health care system.

OBJECTIVE:: To evaluate trends in psychiatric bed occupancy by elderly inpatients in the Brazilian public health care system between 2000 and 2010 and to determine the leading psychiatric diagnosis for hospital admissions. METHODS:: Data from all 895,476 elderly psychiatric admissions recorded in the Brazilian Public Health Care Database (DATASUS) between January 2000 and February 2010 were analyzed. Polynomial regression models with estimated curve models were used to determine the trends. The number of inpatient days was calculated for the overall psychiatric admissions and according to specific diagnoses. RESULTS:: A moderate decreasing trend (p < 0.001) in the number of inpatient days was observed in all geriatric psychiatric admissions (R2 = 0.768) and in admissions for organic mental disorders (R2 = 0.823), disorders due to psychoactive substance use (R2 = 0.767), schizophrenia (R2 = 0.680), and other diagnoses (R2 = 0.770), but not for mood disorders (R2 = 0.472). Most admissions (60 to 65%) were due to schizophrenia. CONCLUSION:: There was a decreasing trend in inpatient days for elderly psychiatric patients between 2000 and 2010. The highest bed occupancy was due to schizophrenia, schizotypal, and delusional disorders.

Nationwide cohort study of hip fractures: time trends in the incidence rates and projections up to 2035.

SUMMARY: A growing elderly population is expected worldwide, and the burden of hip fractures on health care system will continue to increase. By 2035, there will be a 2.7-fold increase in the number of hip fractures in Taiwan. The study provides quantitative basis for the future distribution of medical resources. INTRODUCTION: Hip fractures have long been recognized as a major public health concern. The study aimed to determine time trends in the incidence of hip fractures and to forecast the number of hip fractures expected in Taiwan up to 2035. METHODS: A nationwide survey was conducted using data from the Taiwan National Health Insurance Research Database from 2004 to 2011. A total of 141,397 hip fractures were identified, with a mean of 17,675 fractures/year. Annual incidences of hip fractures were calculated and tested for trends. Projections of the incidence rates of hip fractures and bed days associated with hip fractures were calculated using Poisson regression on the historical incidence rates in combination with population projections from 2012 to 2035. RESULTS: The incidence rates of hip fracture during 2004-2011 were 317 and 211 per 100,000 person-years among women and men, respectively. Over this 8-year period, the age-standardized incidence of hip fracture decreased by 13.4% among women and 12.2% among men. Despite the decline in the age-standardized incidence, the absolute number of hip fractures increased owing to the aging population. The number of hip fractures is expected to increase from 18,338 in 2010 to 50,421 in 2035-a 2.7-fold increase. The number of bed days for 2010 and 2035 was estimated at 161,248 and 501,995, respectively, representing a 3.1-fold increase. CONCLUSIONS: The socioeconomic impact of hip fractures will be high in the near future. This study provides a quantitative basis for future policy decisions to serve this need.

Comparison of nurse staffing based on changes in unit-level workload associated with patient churn.

AIM: This analysis compares the staffing implications of three measures of nurse staffing requirements: midnight census, turnover adjustment based on length of stay, and volume of admissions, discharges and transfers. BACKGROUND: Midnight census is commonly used to determine registered nurse staffing. Unit-level workload increases with patient churn, the movement of patients in and out of the nursing unit. Failure to account for patient churn in staffing allocation impacts nurse workload and may result in adverse patient outcomes. METHOD(S): Secondary data analysis of unit-level data from 32 hospitals, where nursing units are grouped into three unit-type categories: intensive care, intermediate care, and medical surgical. RESULT: Midnight census alone did not account adequately for registered nurse workload intensity associated with patient churn. On average, units were staffed with a mixture of registered nurses and other nursing staff not always to budgeted levels. Adjusting for patient churn increases nurse staffing across all units and shifts. CONCLUSION: Use of the discharges and transfers adjustment to midnight census may be useful in adjusting RN staffing on a shift basis to account for patient churn. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers should understand the implications to nurse workload of various methods of calculating registered nurse staff requirements.