Bed Utilization Research Articles

Adhesive solid-state fermentation producing Aspergillus niger conidia on stainless-steel dixon ring supports.

An adhesive solid-state fermentation (adSSF) mode was developed to produce Aspergillus niger conidia, which used a stainless-steel Dixon ring as the support and water-retaining adhesive to load nutritional media on its surface. To obtain high conidia yields, the components of the water-retaining adhesive were screened, optimized by single-factor optimization and response surface methodology, and the optimal dosages of the main components were: wheat bran powder 0.023g·cm-3bed, cassava starch 0.0022g·cm-3bed, and xanthan gum 0.0083g·cm-3bed. The experimentally tested conidia yield was 4.2-fold that without water-retaining adhesive but was 3.7% lower than the maximum yield predicted by the model. The observed double-side growth of A. niger on the Dixon ring supports improved space utilization of the fermentation bed, and the void fraction can increase with the shrinkage of the gel layer. In 1.6L tray reactors with three-point online temperature monitoring, the inner-bed temperature of adSSF was at most 4°C lower than the adsorbed carrier solid-state fermentation (ACSSF) mode, and the conidia yield was 1.68 × 108 conidia.cm-3bed, 61.5% higher than that of the ACSSF bed at the same time, but when the fermentation time was extended to 168h, the conidia yield of the adSSF bed and ACSSF bed were close to each other. The results revealed that the high voidage of the adSSF bed was the main reason for low bed temperature, which can benefit the inner-bed natural convection and water evaporation.

Forecasting severe respiratory disease hospitalizations using machine learning algorithms

BackgroundForecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.MethodsThe dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting.ResultsWe found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data.ConclusionsActive SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

Improving Performance in Surgical department through Communicating Information to Decision Makers: Intervention Study in a tertiary Hospital

Hospital performance evaluation is crucial for early detection of pitfalls, and problem-solving. Data shouldn’t be passively collected. They must be measured and understood through an underlying conceptual framework. Purpose: The General Surgical Department in collaboration with the Public Health Department had the objective of developing a module for periodic monitoring and evaluation of the eight general surgical units based on calculated key performance indicators (KPIs), Composite Indices (CIs) and communication of growing out information through policy briefs directed to the heads of units for better resources allocation and evidence based decision making. Methodology: An operations research using pre-post intervention design. The exploratory component was completed in six months in the 8 units of the department. Pretest service statistical data analyzed KPIs derived from hospital bed and surgical room utilization. Data were utilized for the calculation of 5 CIs for each unit. Study intervention included the development of policy briefs (PB) that were submitted to the head of the department. Informed decisions were made guided by the info-graph data in the form of traffic color coded ranking matrix to promote various services and to improve performance in the department units particularly lower ranked units. The post-test included reanalyzing of the service statistical data. Findings: Comparing pre-posttest figures revealed improvement in the calculated KPIs and CIs. Originality and practical implications: Bringing up a novel, feasible monitoring and evaluation model using refitted tools would support surgical unit heads in evidence-based decision-making and eventually improve performance efficiency.

Kaizen-led utilization improvement at medical oncology daycare unit.

293 Background: Optimizing utilization of oncology day care units is key in throughput, patient experience and minimizing errors. At our oncology center, the medical oncology day care (MODC) unit was at a infusion chair/bed utilization rate of 92% with a turnaround time for multiagent or long-infusion chemotherapeutic regimens of 275 minutes. This resulted in prolonged wait times for patients and delayed treatment initiations appointments. Therefore, we set out to improve the utilization of the treatment chairs and chemotherapy turnaround time at the MODC. Methods: The goal of this project was to increase MODC chair/bed utilization up to 150% and decrease chemotherapy infusion time by 30% by the end of 4/2024. The interventions integrated Agile Project Methodology to ensure adaptability and responsiveness, with DMAIC (Define, Measure, Analyze, Improve, Control) serving as the guiding quality improvement tool for the improvement plan. Interventions began with project charter approval involving stakeholders from all relevant departments, followed by a comprehensive assessment and gap analysis using DMAIC to identify and address wasted steps in the process. This involved process mapping to streamline operations and eliminate unnecessary tasks. Key interventions included pre-chemotherapy blood work 48 hour prior to treatment, patient segmentation based on treatment session-length, proactive communication with patients regarding treatment session times, and coordination with the chemotherapy pharmacy for medication readiness. Furthermore, strict adherence to defined timelines for patient assessment and chemotherapy order placement was enforced. A sensitive Key Performance Indicator (KPI) structure was developed to measure the outcomes of the interventions, aligning with Agile principles for continuous improvement. Sustainability was ensured through the creation of a sustainability plan aligned with a dashboard to maintain success thresholds. Results: The results demonstrated significant improvements; MODC median bed utilization increasing to 168%, single-agent infusion turnaround time reduced to 150 minutes (33% decrease), and long-agent infusion turnaround time decreased to 210 minutes (30% decrease). Sustainability studies are now underway. Conclusions: These findings underscore the effectiveness of interventions in not only increasing chemotherapy infusion center capacity but also decreasing chemotherapy turnaround time utilizing a Kaizen-led approach.Results of these interventions are expected to enhance efficiency and improve patient care within the MODC unit.

Comparison of effectiveness indicators before and during the COVID-19 pandemic in a university hospital

Objective: Our aim in this study is to reveal to what extent hospital efficiency indicators changed in a University Hospital between the period before the Covid 19 pandemic (2018-2019) and the period when the pandemic started and continued (2020-2021). Method: The study planned as a retrospective and descriptive type, data belongs to a 4-year period (2018-2021). The number of personnel for the years examined in the study, the number of applications to the outpatient clinic and emergency department calculated monthly and the number of deceased patients. Bed occupancy rate, inpatient rate, average patient stay days, bed turnover rate and bed turnover interval were used as hospital bed utilization efficiency indicators. Result: Among all years, April 2020 had the lowest number of emergency applications (n=1287) and total number of outpatient clinic applications (n=7530). The year with the highest bed occupancy rate was 2020 with 77.2±6.7. The highest inpatient rate was in 2020 with 6.7±2.1, the highest average day of stay was in 2021 with 10.8±2.2, the highest bed turnover rate was in 2018 and 2019 with 4.7±0.3 and the highest bed turnover interval was observed in 2021 with 5.2±2.2. April 2020 was the month with the highest inpatient rate at 11.8% and the highest bed turnover interval at 16.8. Conclusion: The number of patients applying to emergency departments and outpatient clinics decreased, the inpatient rate increased, the bed turnover rate decreased, the bed turnover interval extended and the average day of stay increased during the pandemic period.

Carbon Dioxide Adsorption by Variation in Operating Parameters of Sound Assisted Fluidization Using Coal Based Fine Activated Carbon

This research delves into the promising domain of CO2 capture through fine solid activated carbon adsorbent, offering a more energy-efficient alternative to traditional adsorption methods. The central challenge addressed here is the utility of cheaper CO2 adsorbent, fine powder materials whose properties can be precisely tailored via molecular-level fictionalization. Equally vital is selecting an optimal fluidizing column configuration that maximizes CO2 interaction with adsorption particles and enhances adsorption efficiency. The proposed solution is a fluidized bed column uniquely equipped with integrated acoustic vibrations to counteract interparticle forces common in fine powders. For adsorption evaluations, sound-assisted fluidized-bed experimentation on a laboratory size was set up. Adsorbent material activated carbon made up of coal underwent rigorous testing between a range of 20 Hz-200 Hz and 20 dB-135 dB. Results reveal the beneficial effects of acoustic enhancement of fluidization quality and adsorption efficiency, increased adsorption capacity, enhanced bed utilization, and accelerated adsorption rates. Extensive research has been conducted on the detailed effects of major operational variables on adsorption performance, notably frequency, sound intensity, and minimum fluidization velocity. The findings highlight the pivotal role of particle size with mean size 75 microns range as a determinant of adsorption capacity at 100 Hz and 125 dB. At the end of experimentation, the adsorbent considered for the experiment is compared to the study adsorption capacity at operating conditions. The research concludes with a discussion on the effects of influencing parameters for adsorption on employing sound vibrations using fluidization technique adsorption for CO2 capture.

Exploration of the adsorption and desorption performance of volatile organic compounds by activated carbon with different shapes based on fixed-bed experiments

Activated carbon (AC) has been widely used in volatile organic compounds (VOCs) treatment of industrial exhaust gases. Rather than modifying specific pore size distributions and surface properties, altering the shape of AC offers a more feasible approach to enhance its adsorption performance. This study investigates the adsorption-desorption performance of two different shaped ACs with highly similar properties for the removal of VOCs. The clover-shaped AC (CSAC) has a 27.46% lower internal void fraction and a 39.10% higher external void fraction compared to cylindrical AC (CAC), resulting in denser packing and longer contact time with VOCs. Adsorption experiments showed the CSAC has 40% longer adsorption breakthrough (BT) times for ethanol, ethyl acetate, and n-hexane on average, and 20% higher saturation adsorption capacity per unit volume. CSAC also has higher partition coefficients, with the highest values for ethanol, ethyl acetate, and n-hexane being 0.0187, 0.0382, and 0.0527 mol kg−1·Pa−1, respectively. The desorption process for selected VOCs is non-spontaneous and endothermic. Optimal desorption conditions were identified as an inlet space velocity of 3535 h−1, a desorption temperature of 150 °C, and a pulsed inlet method. To investigate the possibility of the application of CSAC in real-world scenarios, xylene was chosen as a representative industrial VOC. Results showed CSAC has 20% higher BT time and saturation adsorption capacity for xylene compared to CAC under different bed heights. The desorption efficiency for xylene on both ACs is below 40%. With increasing xylene inlet concentration, the mass transfer zone (MTZ) height initially increases but stabilizes beyond 1704 mg m−3. At identical bed heights, the MTZ height of CSAC is 29% shorter than CAC, indicating a higher bed utilization efficiency.

The allocation and utilization efficiency of hospital beds in Sichuan Province, China.

To analyze the allocation and utilization efficiency of hospital beds in Sichuan Province, China, and to provide a scientific basis for improving the rational allocation and efficient utilization. The supply and demand balance method, health resource agglomeration degree (HRAD), bed efficiency index and bed utilization model were used to evaluate the allocation and utilization efficiency of hospital beds in Sichuan Province from 2017 to 2021. The number of hospital beds per 1000 population in Sichuan Province increased from 4.97 in 2017 to 5.94 in 2021. The overall supply and demand ratio of hospital beds in Sichuan Province is between 0.85 and 1.01, and the supply and demand situation is a basically balanced situation. The HRAD of hospital beds in Ya'an, Aba, Ganzi and Liangshan is <1, indicating that the equity of hospital beds by geography in these regions is low. The difference between HRAD and population agglomeration degree (PAD) in 9 regions, including Deyang, Aba, Ganzi and Liangshan, is <0, indicating that there are insufficient hospital beds in these areas relative to the agglomerated population. The bed efficiency index of hospital beds in 17 regions, including Chengdu, Zigong, Aba and Ganzi, are all <1, which means that hospital beds are operating with low efficiency. The bed utilization model of Panzhihua is efficiency type, that of Zigong, Deyang and Ziyang is pressure bed type, and that of Nanchong and Ya'an is idle type. The hospital bed allocation in Sichuan Province is relatively good, and the supply and demand situation is in a basically balanced situation. The hospital bed allocation in Aba, Ganzi and Liangshan is insufficient by geography and population. The overall operational efficiency of hospital beds is low, and there are more idle and pressure bed utilization models.

The burden of hyperkalaemia on hospital healthcare resources

Hyperkalaemia is associated with prolonged hospital admission and worse mortality. Hyperkalaemia may also necessitate clinical consults, therapies for hyperkalaemia and high-dependency bed utilisation. We evaluated the ‘hidden’ human and organisational resource utilisation for hyperkalaemia in hospitalised patients. This was a single-centre, observational cohort study (Jan 2017–Dec 2020) at a tertiary-care hospital. The CogStack system (data processing and analytics platform) was used to search unstructured and structured data from individual patient records. Association between potassium and death was modelled using cubic spline regression, adjusted for age, sex, and comorbidities. Cox proportional hazards estimated the hazard of death compared with normokalaemia (3.5–5.0 mmol/l). 129,172 patients had potassium measurements in the emergency department. Incidence of hyperkalaemia was 85.7 per 1000. There were 49,011 emergency admissions. Potassium > 6.5 mmol/L had 3.9-fold worse in-hospital mortality than normokalaemia. Chronic kidney disease was present in 21% with potassium 5–5.5 mmol/L and 54% with potassium > 6.5 mmol/L. For diabetes, it was 20% and 32%, respectively. Of those with potassium > 6.5 mmol/L, 29% had nephrology review, and 13% critical care review; in this group 22% transferred to renal wards and 8% to the critical care unit. Dialysis was used in 39% of those with peak potassium > 6.5 mmol/L. Admission hyperkalaemia and hypokalaemia were independently associated with reduced likelihood of hospital discharge. Hyperkalaemia is associated with greater in-hospital mortality and reduced likelihood of hospital discharge. It necessitated significant utilisation of nephrology and critical care consultations and greater likelihood of patient transfer to renal and critical care.

Low-Resource Hospital Days for Children Following New Tracheostomy.

Children with new tracheostomy and invasive mechanical ventilation (IMV) require transitional care involving caregiver education and nursing support. To better understand hospital resource use during this transition, our study aimed to: (1) define and characterize low-resource days (LRDs) for this population and (2) identify factors associated with LRD occurrence. This retrospective cohort analysis included children ≤21 years with new tracheostomy and IMV dependence admitted to an ICU from 2017 to 2022 using the Pediatric Health Information System database. A LRD was defined as a post tracheostomy day that accrued nonroom charges <10% of each patient's accrued nonroom charges on postoperative day 1. Factors associated with LRDs were analyzed using negative binomial regression. Among 4048 children, median post tracheostomy stay was 69 days (interquartile range 34-127.5). LRDs were common: 38.6% and 16.4% experienced ≥1 and ≥7 LRDs, respectively. Younger age at tracheostomy (0-7 days rate ratio [RR] 2.42 [1.67-3.51]; 8-28 days RR 1.8 (1.2-2.69) versus 29-365 days; Asian race (RR 1.5 [1.04-2.16]); early tracheostomy (0-7 days RR 1.56 [1.2-2.04]), and longer post tracheostomy hospitalizations (31-60 days RR 1.85 [1.44-2.36]; 61-90 days RR 2.14 [1.58-2.91]; >90 days RR 2.21 [1.71-2.86]) were associated with more LRDs. Approximately 1 in 6 children experienced ≥7 LRDs. Younger age, early tracheostomy, Asian race, and longer hospital stays were associated with increased risk of LRDs. Understanding the postacute phase, including bed utilization, serves as an archetype to explore care models for children with IMV dependence.

Organizational and infrastructural risk factors for health care–associated Clostridioides difficile infections or methicillin-resistant Staphylococcus aureus in hospitals

This study explores the infrastructural and organizational risk factors for healthcare-associated (HCA) Clostridioides difficile infections (CDIs) and methicillin-resistant Staphylococcus aureus (MRSA) in hospitals. This is a retrospective observational study involving all eligible inpatient units from 12 hospitals in British Columbia, Canada, from April 1, 2020 to September 16, 2021. The outcomes were the average HCA CDI or MRSA rates. Covariates included, but were not limited to, infection control factors (eg, hand hygiene rate), infrastructural factors (eg, unit age), and organizational factors (eg, hallway bed utilization). Multivariable regression was performed to identify statistically significant risk factors. Older units were associated with higher HCA CDI rates (adjusted relative risk [aRR]: 0.012; 95%confidence interval (CI) [0.004, 0.020]). Higher HCA MRSA rates were associated with decreased hand hygiene rate (aRR: -0.035; 95%CI [-0.063, -0.008]), higher MRSA bioburden (aRR: 9.008; 95%CI [5.586, 12.429]), increased utilization of hallway beds (aRR: 0.680; 95%CI [0.094, 1.267]), increased nursing overtime rate (aRR: 5.018; 95%CI [1.210, 8.826]), and not keeping the clean supply room door closed (aRR: -0.283; 95%CI [-0.536, -0.03]). The study confirmed the multifaceted nature of infection prevention and emphasized the importance of interdepartmental collaboration to improve patient safety.

Design, construction, and validation of obstetric risk classification systems to predict intensive care unit admission.

To develop and validate a support tool for healthcare providers, enabling them to make precise and critical decisions regarding intensive care unit (ICU) admissions for high-risk pregnant women, thus enhancing maternal outcomes. This retrospective study involves secondary data analysis of information gathered from 9550 pregnant women, who had severe maternal morbidity (any unexpected complication during labor and delivery that leads to substantial short-term or long-term health issues for the mother), collected between 2009 and 2010 from the Brazilian Network for Surveillance of Severe Maternal Morbidity, encompassing 27 obstetric reference centers in Brazil. Machine-learning models, including decision trees, Random Forest, Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost), were employed to create a risk prediction tool for ICU admission. Subsequently, sensitivity analysis was conducted to compare the accuracy, predictive power, sensitivity, and specificity of these models, with differences analyzed using the Wilcoxon test. The XGBoost algorithm demonstrated superior efficiency, achieving an accuracy rate of 85%, sensitivity of 42%, specificity of 97%, and an area under the receiver operating characteristic curve of 86.7%. Notably, the estimated prevalence of ICU utilization by the model (11.6%) differed from the prevalence of ICU use from the study (21.52%). The developed risk engine yielded positive results, emphasizing the need to optimize intensive care bed utilization and objectively identify high-risk pregnant women requiring these services. This approach promises to enhance the effective and efficient management of pregnant women, particularly in resource-constrained regions worldwide. By streamlining ICU admissions for high-risk cases, healthcare providers can better allocate critical resources, ultimately contributing to improved maternal health outcomes.

Enhancing orthopedic care: The impact of introducing nurse practitioners

IntroductionOrthopedic surgery constitutes a significant portion of surgical interventions globally, driven by aging populations (Spence et al., 2019). Nurse practitioners (NPs) have emerged as essential team members in orthopedic surgery, offering expertise and advanced training. PurposeThis paper describes a practice development initiative that saw the integration of NPs to the orthopedic team to provide postoperative care to patients undergoing hip and knee arthroplasty procedures. MethodsAggregate data was collected from electronic health records and from online surveys. ResultsUsing descriptive and inferential statistics our data analysis revealed significant improvements post-NP implementation. Monthly surgeries increased by 41.6%, indicating enhanced access to care. The average length of stay decreased statistically for both hip and knee surgeries (p < 0.001), resulting in substantial cost savings and streamlined resource utilization. The readmission rate post-discharge also decreased by 41.6%, reflecting improved continuity of care facilitated by NPs. Surveys among healthcare staff demonstrated positive feedback regarding NP integration, highlighting improved accessibility, bed utilization, and timely treatment of medical issues. ConclusionThis study underscores the multifaceted benefits of NP integration in orthopedic care, including enhanced patient outcomes, improved healthcare efficiency, and positive impacts on the healthcare team dynamics. Continued investment in NP programs in orthopedic settings is crucial for further optimizing care delivery and patient outcomes. Advocating for the expansion of the NP role in orthopedic surgery will contribute to elevating the standard of care in this specialty.

Fast-track Implementing Experience in the Kyrgyz Republic

Fast Track Pathways (FTP) aimed at reducing length of stay and overall costs are increasingly being implemented for emergency surgeries. The objective of this study was to evaluate the implementation of a FTP for emergency general surgery at the Osh State University Clinical Hospital (OSU CH). The study included 98 patients at OSU CH from 2020 to 2023 who underwent laparoscopic appendectomy (LA), laparoscopic cholecystectomy (LC), or laparoscopic inguinal hernia repair (LI). The FTP group comprised 71 patients, with data from 76 control cases assessed prior to FTP implementation. Comparisons were made between time to surgery (TTS), length of stay (LOS), and total length of stay between groups. Direct costs, reimbursements, and patient satisfaction (rated from 1 = never to 4 = always) were also evaluated. The sample comprised 60.6% females with a median age of 40 years. Case distribution varied slightly (56.2% vs 42.1% for LA, 40.4% vs 57.9% for LC, FTP vs control), yet time to surgery was similar between groups (11 hours 39 minutes vs 10 hours 2 minutes, P = 0.633). Postoperative LOS was significantly shorter in the FTP group (15 hours 17 minutes vs 29 hours 9 minutes, P &lt; 0.001), reflecting reduced hospital stay (3 hours 11 minutes vs 20 hours 10 minutes, P &lt; 0.001), with fewer patients requiring inpatient beds and overnight stays (P &lt; 0.001). Direct costs were significantly reduced in the FTP group, while reimbursements were comparable (P &lt; 0.001 and P = 0.999, respectively), and average patient satisfaction was high (3.3/4). In an era of cost reduction, resource optimization, and patient satisfaction, FTP can play a significant role in emergency hospitalization. At OSU CH, FTP significantly reduced hospital stay and bed utilization without impacting reimbursements or patient satisfaction.

Information management of full-cycle inpatient bed reservation for cancer patients under the normalised prevention and control of the COVID-19 pandemic

BackgroundDuring the prolonged COVID-19 pandemic, hospitals became focal points for normalised prevention and control. In this study, we investigated the feasibility of an inpatient bed reservation system for cancer patients that was developed in the department?s public WeChat account. We also explored its role in improving operational efficiency and nursing quality management, as well as in optimising nursing workforce deployment.MethodsWe utilised WeChat to facilitate communication between cancer patients and health care professionals. Furthermore, we collected data on admissions, discharges, average number of hospitalisation days, bed utilisation rate, and the number of bed days occupied by hospitalised patients through the hospital information system and nurses? working hours and competency levels through the nurse scheduling system. The average nursing hours per patient per day were calculated. Through the inpatient bed reservation system, the number of accepted admissions, denied admissions, and cancelled admissions from the reservation system were collected. The impact of the bed reservation system on the department?s operational efficiency was analysed by comparing the number of hospitalisation discharges before and after reservations, as well as the average hospitalisation and bed utilisation rates. By comparing nurses? working hours per month and average nursing hours per patient per day, the system?s impact on nurses? working hours and nursing quality indicators was analysed.ResultsThe average hospitalisation length, bed utilisation rate, and nurses? working hours were significantly lower, and the average number of nursing hours per patient per day was significantly higher after the implementation of the reservation system. The full-cycle bed information management model for cancer patients did not affect the number of discharged patients.ConclusionPatients? ability to reserve bed types from home in advance using the department?s official WeChat-based inpatient bed reservation system allowed nurses to prepare for their work ahead of time. This in turn improved the operational efficiency of the department and nursing quality, and it optimised the deployment of the nursing workforce

Evaluation of Bed Utilization Pattern in a Tertiary Care Teaching Corporate Hospital in India

Evaluation of Bed Utilization Pattern in a Tertiary Care Teaching Corporate Hospital in India

524 Outcomes From Preoperative Risk Scoring for Emergency Laparotomy

Abstract Aim Risk scoring systems can be used to predict outcome, facilitate perioperative decision making and guide management in emergency laparotomy (EL). This study aims to evaluate the differences in outcome between EL patients who were prospectively risk assessed and those who were not. Method A retrospective study was conducted on all 782 patients aged over 18 years who underwent EL at four Australian hospitals between 1 January 2018 and 31 December 2019. Data recorded included demographics, preoperative risk assessment, case urgency, involvement of anaesthetic and intensive care consultants, critical care bed requirement, mortality, and discharge destination. NELA scores were retrospectively calculated for all patients. Results 209 patients had been preoperatively risk assessed, with a mean NELA score of 16.45%, compared to 8.72% for those who were not preoperatively risk assessed. They tended towards a higher ASA score, were booked as a higher urgency operation, and were more likely to receive consultant led care by anaesthetics and intensivists. Postoperatively these patients had increased critical care bed utilisation, 30-day mortality was 14.4% compared to 5.8%, and by 365 days mortality rose to 24.9% compared to 13.8%. Conclusions Use of preoperative risk prediction scores does not in itself reduce risk of mortality. In a health system that does not mandate formal preoperative risk assessment, it appears that clinicians use preoperative risk assessment tools when they perceive risk to be higher, rather than as a routine part of emergency laparotomy care.

Experimental investigation on productivity enhancement of conical solar distillers using various sand bed types based on energy, exergy, and exergo-economic analysis

In this study, performance enhancement of conical distillers was investigated with various natural sand bed types. Four sand types with different colors (compositions and concentrations of chemical compounds) were tested and used as thermal storage materials to reduce heat loss, enhance evaporation process, and daily production. Three similar distillers were designed and investigated experimentally under the same climatic parameters over two consecutive days. Performance parameters of the proposed distillers, including yield, energy, exergy, and exergo-economic were analyzed and compared with those of reference distiller and other related studies. Results showed that the highest yield and performance values were achieved by the distiller combined with a golden sand bed. The utilization of golden sand bed achieves a daily production of 7.38 lm−2day−1 with enhancement of 10.31 %, 22.79 %, 35.91 %, and 60.43 % compared with yellow sand, red sand, white sand, and reference distillers, respectively. The daily energy and exergy efficiency of conical distillers combined with golden, yellow, red, and white sand beds 59.59 %, 44.84 %, 30.33 %, and 17.85 % and 143.52 %, 103.44 %, 64.73 %, and 36.39 %, respectively, higher than that of the reference distiller. Integrating golden, yellow, red, and white sand beds with conical distillers reduces the yield price and payback time by 60.43 %, 45.43 %, 30.65 %, and 18.04 %. The annual energy and exergy output of conical distillers combined with golden, yellow, red, and white sand beds were 130.54, 118.47, 106.60, 96.41, and 81.76 kWh/year and 6.08, 5.08, 4.00, 3.31, and 2.49 kWh/year, respectively.

Expediting Discharges on the Inpatient Medicine Teaching Services

Introduction: Delayed discharges can adversely affect patient flow throughout the hospital. Late afternoon discharges may lead to high hospital occupancy, admission bottlenecks, ER overcrowding, and disrupted ICU transfers. Discharge by noon (DBN) is key to enhancing hospital capacity, bed utilization, and patient satisfaction and outcomes. Last year, DBN for patients returning home with or without home care services (HHC) was chosen as one of three annual priorities for our hospital medicine division. This was implemented in October 2023 but did not gain momentum among the medical resident teaching services (MTS), paving the way for our QI project. Model for Improvement: Baseline data on the percentage of MTS DBN to home and HHC were available from November 2023 to January 2024. We surveyed our residents/faculty and conducted a root-cause analysis. The main barriers to DBN included a lack of planning and motivation, delayed communication with case management, and prolonged rounding. Aims Statement: Our goal was to increase the median percentage of MTS discharge orders placed by noon to home and HHC from 22% to 35% within 3 months. Measures: Our primary outcome measure was the percentage of MTS DBN to home and HHC. Weekly EMR-generated reports were collected. The outcome measure was then calculated and uploaded weekly on a run chart for analysis. Qualitative process measures included assessing if residents were planning discharges and documenting the number of discharges completed before noon. Changes: Our interventions targeted the lack of planning and motivation. These included introducing the expedited initiative and its importance to residents/faculty through conferences. In addition, a process was created where potential next-day discharges were discussed at afternoon sign-out and a tracking chart of DBN orders was designed for each team room. Tests of Change: Our pre-intervention median percentage of MTS DMB to home and HHC was 22% and improved to 40% post-intervention. While we observed a variation in discharge practice based on the rounding attending, we had notably sustained an upward shift in practice from our baseline when data was plotted on a run chart. We are currently continuing data collection to track sustainability and variability among rounding physicians. Conclusion: We attained an upward shift in the percentage of MTS DBN to home and HHC post-intervention. Although an upward shift has been achieved, we are looking to sustain and further improve it. In future PDSA cycles, we will look at individualizing feedback and optimizing interdisciplinary rounds. We plan on further investigating balancing measures such as bed availability, discharge timing for rehabilitation destinations, and patient and staff satisfaction.

Brief admission by self-referral as an add-on to usual care for individuals with self-harm at risk of suicide: cost-effectiveness and 4-year health-economic consequences after a Swedish randomized controlled trial

Background Brief Admission by self-referral (BA) is a crisis-management intervention standardized for individuals with self-harm at risk of suicide. We analyzed its health-economic consequences. Materials and methods BA plus treatment as usual (TAU) was compared with TAU alone in a 12-month randomized controlled trial with 117 participants regarding costs for hospital admissions, coercive measures, emergency care and health outcomes (quality-adjusted life years; QALYs). Participants were followed from 12 months before baseline to up to five years after. Results Over one year BA was associated with a mean annual cost reduction of 4800 or incremental cost of 4600 euros, depending on bed occupancy assumption. Cost-savings were greatest for individuals with >180 admission days in the year before baseline. In terms of health outcomes BA was associated with a QALY gain of 0.078. Uncertainty analyses indicated a significant QALY gain and ambiguity in costs, resulting in BA either dominating TAU or costing 59 000 euros per gained QALY. Conclusion BA is likely to produce QALY gains for individuals living with self-harm and suicidality. Cost-effectiveness depends on targeting high-need individuals and comparable bed utilization between BA and other psychiatric admissions. Future research should elaborate the explanatory factors for individual variations in the usage and benefit of BA.