The association between bed occupancy rates and hospital quality in the English National Health Service

Remote Vital Sign Monitoring in Admission Avoidance Hospital at Home: A Systematic Review

OBJECTIVES

To examine randomized controlled trials (RCTs) of "hospital at home" (HAH) for admission avoidance in adults presenting with acute physical illness to identify the use of vital sign monitoring approaches and evidence for their effectiveness.

DESIGN

Systematic review.

SETTING AND PARTICIPANTS

This review compared strategies for vital sign monitoring in admission avoidance HAH for adults presenting with acute physical illness. Vital sign monitoring can support HAH acute multidisciplinary care by contributing to safety, determining requirement of further assessment, and guiding clinical decisions. There are a wide range of systems currently available, including reliable and automated continuous remote monitoring using wearable devices.

METHODS

Eligible studies were identified through updated database and trial registries searches (March 2, 2016, to February 15, 2023), and existing systematic reviews. Risk of bias was assessed using the Cochrane risk of bias 2 tool. Random effects meta-analyses were performed, and narrative summaries provided stratified by vital sign monitoring approach.

RESULTS

Twenty-one eligible RCTs (3459 participants) were identified. Two approaches to vital sign monitoring were characterized: manual and automated. Reporting was insufficient in the majority of studies for classification. For HAH compared to hospital care, 6-monthly mortality risk ratio (RR) was 0.94 (95% CI 0.78-1.12), 3-monthly readmission to hospital RR 1.02 (0.77-1.35), and length of stay mean difference 1.91 days (0.71-3.12). Readmission to hospital was reduced in the automated monitoring subgroup (RR 0.30 95% CI 0.11-0.86).

CONCLUSIONS AND IMPLICATIONS

This review highlights gaps in the reporting and evidence base informing remote vital sign monitoring in alternatives to admission for acute illness, despite expanding implementation in clinical practice. Although continuous vital sign monitoring using wearable devices may offer added benefit, its use in existing RCTs is limited. Recommendations for the implementation and evaluation of remote monitoring in future clinical trials are proposed.

Spinal anesthesia in ambulatory patients

PURPOSE OF THIS REVIEW

To assess current practice in the use of spinal anesthesia in major ambulatory surgery, highlighting its advantages over general anesthesia and identifying potential areas for improvement to facilitate a transition to a sustainable healthcare system.

RECENT FINDINGS

Spinal anesthesia might be preferred in selected populations when compared to general anesthesia providing the highest standards of healthcare quality.The use of local anesthetics with short half-life has proven to be efficient in achieving high anesthesia success rates. Spinal anesthesia does not increase perioperative complications; instead, it has shown a reduction in postoperative nausea and vomiting, an improvement in patient comfort, and a favorable economic impact when compared to general anesthesia.

SUMMARY

Spinal anesthesia is an appropriate method for anesthesia in ambulatory patients, offering advantages over general anesthesia in selected populations.The use of spinal anesthesia is expanding to meet surgical needs. Therefore, it is crucial to plan ahead and anticipate organizational failures in the ambulatory setting to maintain safety and efficiency during outpatient procedures and surgeries.

Associations between BMI and hospital resource use in patients hospitalised for COVID-19 in England: a community-based cohort study

BACKGROUND

Excess weight is a major risk factor for severe disease after infection with SARS-CoV-2. However, the effect of BMI on COVID-19 hospital resource use has not been fully quantified. This study aimed to identify the association between BMI and hospital resource use for COVID-19 admissions with the intention of informing future national hospital resource allocation.

METHODS

In this community-based cohort study, we analysed patient-level data from 57 415 patients admitted to hospital in England with COVID-19 between April 1, 2020, and Dec 31, 2021. Patients who were aged 20-99 years, had been registered with a general practitioner (GP) surgery that contributed to the QResearch database for the whole preceding year (2019) with at least one BMI value measured before April 1, 2020, available in their GP record, and were admitted to hospital for COVID-19 were included. Outcomes of interest were duration of hospital stay, transfer to an intensive care unit (ICU), and duration of ICU stay. Costs of hospitalisation were estimated from these outcomes. Generalised linear and logit models were used to estimate associations between BMI and hospital resource use outcomes.

FINDINGS

Patients living with obesity (BMI >30·0 kg/m2) had longer hospital stays relative to patients in the reference BMI group (18·5-25·0 kg/m2; IRR 1·07, 95% CI 1·03-1·10); the reference group had a mean length of stay of 8·82 days (95% CI 8·62-9·01). Patients living with obesity were more likely to be admitted to ICU than the reference group (OR 2·02, 95% CI 1·86-2·19); the reference group had a mean probability of ICU admission of 5·9% (95% CI 5·5-6·3). No association was found between BMI and duration of ICU stay. The mean cost of COVID-19 hospitalisation was £19 877 (SD 17 918) in the reference BMI group. Hospital costs were estimated to be £2736 (95% CI 2224-3248) higher for patients living with obesity.

INTERPRETATION

Patients admitted to hospital with COVID-19 with a BMI above the healthy range had longer stays, were more likely to be admitted to ICU, and had higher health-care costs associated with hospital treatment of COVID-19 infection as a result. This information can inform national resource allocation to match hospital capacity to areas where BMI profiles indicate higher demand.

FUNDING

National Institute for Health Research.

Patient outcomes, efficiency, and adverse events for elective hip and knee replacement in private and NHS hospitals: a population-based cohort study in England

Background

Since the early 2000s, the National Health Service (NHS) in England has expanded provision of publicly funded care in private hospitals as a strategy to meet growing demand for elective care. This study aims to compare patient outcomes, efficiency and adverse events in private and NHS hospitals when providing elective hip and knee replacement.

Methods

We conducted a population-based cohort study including patients ≥18 years, undergoing a publicly funded elective hip or knee replacement in private and NHS hospitals in England between January 1st 2016 and March 31st 2019. Comparative probability was estimated for three patient outcome measures (in-hospital mortality, emergency readmissions with 28 days, hospital transfers), two efficiency measures (pre-operative length of stay (LOS) >0 day and post-operative LOS >2 days), and four adverse events (hospital-associated infection, adverse drug reactions, pressure ulcers, venous thromboembolism). Probit regression was used to adjust for observable confounding followed by instrumental variable (IV) analyses to also account for unobserved confounding at the patient-level. Propensity score matching was then used as a robustness check.

Findings

Our study sample included 169,232 patients in private hospitals, and 262,659 patients in NHS hospitals. Estimates from probit regression indicated that treatment in private hospital was associated with reduced probability of in-hospital mortality (-0.0009, 95% CI -0.0010, -0.0007), emergency readmissions (-0.0181, 95% CI -0.0191, -0.0172), hospital transfers (-0.0076, 95% CI -0.0084, -0.0068), prolonged post-operative LOS (-0.1174, 95% CI -0.1547, -0.0801), hospital-associated infection (-0.0115, 95% CI -0.0123, -0.0107), adverse drug reactions (-0.0051, 95% CI -0.0056, -0.0046), pressure ulcers (-0.0017, 95% CI -0.0019, -0.0014), and venous thromboembolism (-0.0027, 95% CI -0.0031, -0.0022). IV analyses produced no significant differences between private and NHS hospitals, except for lower probability in private hospitals of hospital-associated infection (-0.0057, 95% CI -0.0081, -0.0032), and greater probability in private hospitals of prolonged post-operative LOS (0.2653, 95% CI 0.1833, 0.3472). Propensity score matching produced similar results to probit regression.

Interpretation

Our findings indicate there is potentially important unobservable confounding at the patient-level between private and NHS hospitals not adjusted for when using probit regression or propensity score matching.

Funding

This research did not receive any dedicated funding.

Multivariate Relationships Between Health Outcomes and Health System Performance Indicators: An Integrated Factor Analysis With Canonical Correlations

OBJECTIVES

This study aimed to investigate the relationships between sets of variables related to health system performance indicators and health outcomes.

METHODS

The relationships between a set of health outcomes and a set of health system performance indicators of a developing country were examined using multivariate statistical analysis techniques. A combinative strategy of explanatory factor analysis and the canonical correlation coefficient was used to define linear structural relationships between study variables. Province-based data were gathered from2 official statistical records of the Turkish Statistical Institute for the year 2019. Life expectancy at birth, infant mortality rate, and crude death rate were accepted as health outcome indicators.

RESULTS

The explanatory factor analysis indicated 2 independent variable groups, namely (1) health-related human resources and capacity and (2) health service utilization characteristics. The results of the canonical correlation analysis illustrated good performance to define sparse linear combinations of the 2 groups of variables. There existed strong positive correlations between health outcomes and health-related human resources and capacity indicators (rc = 0.83; P < .001) and health service utilization indicators (rc = 0.59; P < .001).

CONCLUSIONS

The results of this study support the view that there is a linear and strong positive relationship between health outcomes and health-related human resources and capacity indicators. Further studies will combine big data analytics with multivariate statistical analysis techniques by studying large health system performance data sets.

Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System

Based upon 30-years of research by the author, a new approach to hospital bed planning and international benchmarking is proposed. The number of hospital beds per 1000 people is commonly used to compare international bed numbers. This method is flawed because it does not consider population age structure or the effect of nearness-to-death on hospital utilization. Deaths are also serving as a proxy for wider bed demand arising from undetected outbreaks of 3000 species of human pathogens. To remedy this problem, a new approach to bed modeling has been developed that plots beds per 1000 deaths against deaths per 1000 population. Lines of equivalence can be drawn on the plot to delineate countries with a higher or lower bed supply. This method is extended to attempt to define the optimum region for bed supply in an effective health care system. England is used as an example of a health system descending into operational chaos due to too few beds and manpower. The former Soviet bloc countries represent a health system overly dependent on hospital beds. Several countries also show evidence of overutilization of hospital beds. The new method is used to define a potential range for bed supply and manpower where the most effective health systems currently reside. The method is applied to total curative beds, medical beds, psychiatric beds, critical care, geriatric care, etc., and can also be used to compare different types of healthcare staff, i.e., nurses, physicians, and surgeons. Issues surrounding the optimum hospital size and the optimum average occupancy will also be discussed. The role of poor policy in the English NHS is used to show how the NHS has been led into a bed crisis. The method is also extended beyond international benchmarking to illustrate how it can be applied at a local or regional level in the process of long-term bed planning. Issues regarding the volatility in hospital admissions are also addressed to explain the need for surge capacity and why an adequate average bed occupancy margin is required for an optimally functioning hospital.

Disaster Preparedness and Hospital Safety in State Hospitals in Lima (Peru)

INTRODUCTION

Peru's health infrastructures, particularly hospitals, are exposed to disaster threats of different natures. Traditionally, earthquakes have been the main disaster in terms of physical and structural vulnerability, but the coronavirus disease 2019 (COVID-19) pandemic has also shown their functional vulnerability. Public hospitals in Lima are very different in terms of year constructed, type of construction, and number of floors, making them highly vulnerable to earthquakes. In addition, they are subject to a high demand for care daily. Therefore, if a major earthquake were to occur in Lima, the hospitals would not have the capacity to respond to the high demand.

OBJECTIVE

The aim of this study was to analyze the Hospital Safety Index (HSI) in hospitals in Lima (Peru).

MATERIALS AND METHODS

This was a cross-sectional observational study of 18 state-run hospitals that met the inclusion criteria; open access data were collected for the indicators proposed by the Pan American Health Organization (PAHO) Version 1. Associations between variables were calculated using the chi-square test, considering a confidence level of 95%. A P value less than .05 was considered to determine statistical significance.

RESULTS

The average bed occupancy rate was 90%, the average age was 70 years, on average had one bed per 25,126 inhabitants, and HSI average score was 0.36 with a vulnerability of 0.63. No association was found between HSI and hospital characteristics.

CONCLUSION

Most of the hospitals were considered Category C in earthquake and disaster safety, and only one hospital was Category A. The hospital situation needs to be clarified, and the specific deficiencies of each institution need to be identified and addressed according to their own characteristics and context.

Exploring the determinants of private healthcare providers' market power: A performance-based perspective

This research focuses on market power in the private healthcare sector. This topic has been poorly explored by the extant literature and the reasons mainly rely on the peculiarities of the sector and the specific market. In fact, health providers' market power is influenced by multiple factors and by the fact that prices are often regulated by national or regional public authorities. To fill this gap, the article explores the relationship between performance characteristics and health providers' market power, measured through the Lerner index. The research is based on the analysis of panel data for 437 Italian private healthcare providers over the period 2012-2020. To explore the determinants of health providers' market power, this research employs System-generalized method of moments (SYS-GMM) estimation models. The results highlight a significant and non-linear relationship between market power and process performance, as well as with gender diversity. Intangible assets are another input variable that has a significant and positive relationship with market power. The study contributes to the identification of the performance characteristics driving health providers' market power.