Estimating the intensity of ward admission and its effect on emergency department access block

Strategies to mitigate emergency department crowding and its impact on cardiovascular patients

Emergency department (ED) crowding is a worsening global problem caused by hospital capacity and other health system challenges. While patients across a broad spectrum of illnesses may be affected by crowding in the ED, patients with cardiovascular emergencies-such as acute coronary syndrome, malignant arrhythmias, pulmonary embolism, acute aortic syndrome, and cardiac tamponade-are particularly vulnerable. Because of crowding, patients with dangerous and time-sensitive conditions may either avoid the ED due to anticipation of extended waits, leave before their treatment is completed, or experience delays in receiving care. In this educational paper, we present the underlying causes of crowding and its impact on common cardiovascular emergencies using the input-throughput-output process framework for patient flow. In addition, we review current solutions and potential innovations to mitigate the negative effect of ED crowding on patient outcomes.

Cost-effectiveness analysis of direct admission to acute geriatric unit versus admission after an emergency department visit for elderly patients

BACKGROUND

Elderly individuals represent an increasing proportion of emergency department (ED) users. In the Greater Paris University Hospitals (APHP) direct-admission study, direct admission (DA) to an acute geriatric unit (AGU) was associated with a shorter hospital length of stay (LOS), lower post-acute care transfers, and lower risk of an ED return visit in the month following the AGU hospitalization compared with admission after an ED visit. Until now, no economic evaluation of DA has been available.

METHODS

We aimed to evaluate the cost-effectiveness of DA to an AGU versus admission after an ED visit in elderly patients. This was conducted alongside the APHP direct-admission study which used electronic medical records and administrative claims data from the Greater Paris University Hospitals (APHP) Health Data Warehouse and involved 19 different AGUs. We included all patients ≥ 75 years old who were admitted to an AGU for more than 24 h between January 1, 2013 and December 31, 2018. The effectiveness criterion was the occurrence of ED return visit in the month following AGU hospitalization. We compared the costs of an AGU stay in the DA versus the ED visit group. The perspective was that of the payer. To characterise and summarize uncertainty, we used a non-parametric bootstrap resampling and constructed cost-effectiveness accessibility curves.

RESULTS

At baseline, mean costs per patient were €5113 and €5131 in the DA and ED visit groups, respectively. ED return visit rates were 3.3% (n = 81) in the DA group and 3.9% (n = 160) in the ED group (p = 0.21). After bootstrap, the incremental cost-effectiveness ratio was €-4249 (95%CI= -66,001; +45,547) per ED return visit averted. Acceptability curves showed that DA could be considered a cost-effective intervention at a threshold of €-2405 per ED return visit avoided.

CONCLUSION

The results of this cost-effectiveness analysis of DA to an AGU versus admission after an ED visit for elderly patients argues in favor of DA, which could help provide support for public decision making.

The Association Between Age and Admission to an Inappropriate Ward: A Cross-Sectional Survey in France

Half of elderly patient hospitalizations are preceded by an emergency department (ED) visit. Hospitalization in inappropriate wards (IWs), which is more frequent in case of ED overcrowding and high hospital occupancy, leads to increased morbidity. Elderly individuals are the most exposed to these negative health care outcomes. Based on a nationwide cross-sectional survey involving all EDs in France, the aim of this study was to explore whether age was associated with admission to an IW after visiting an ED. Among the 4384 patients admitted in a medical ward, 4065 were admitted in the same hospital where the ED was located, among which 17.7% were admitted to an IW. Older age was associated with an increased likelihood of being admitted to an IW (OR = 1.39; 95% CI = 1.02-1.90 for patients aged 85 years and older and OR = 1.40; 95% CI = 1.02-1.91 for patients aged 75-84 years, compared with those under 45 years). ED visits during peak periods and cardio-pulmonary presenting complaint were also associated with an increased likelihood of admission to an IW. Despite their higher vulnerability, elderly patients are more likely to be admitted to an IW than younger patients. This result reinforces the need for special attention to be given to the hospitalization of this fragile population.

The effect of direct admission to acute geriatric units compared to admission after an emergency department visit on length of stay, postacute care transfers and ED return visits

Background

Compared with conventional hospitalization, admission to an acute geriatric care unit (AGU) is associated with better outcomes in elderly patients. In 2012, 50% of the hospitalizations of elderly patients were preceded by an emergency department (ED) visit. Hospital occupancy, access blocks and overcrowding experienced by patients during ED visits are associated with increased morbidity.

Objective

Our aim was to evaluate the effect of direct admission (DA) to an AGU on both the hospital length of stay and morbidity of elderly patients.

Design

This study was a retrospective cohort study conducted using electronic medical records and administrative claims data from the Greater Paris University Hospitals (APHP) health data warehouse involving 19 different AGUs.

Participants

We included all patients ≥ 75 years old who were admitted to an AGU for more than 24 h between January 1, 2013, and December 31, 2018.

Intervention

Direct admission to the AGU compared to admission after an ED visit.

Main measures

The main outcome was hospital length of stay. Two outcomes were used to analyse morbidity: postacute care and rehabilitation ward transfer at the end of the index hospitalization and ED return visit within 30 days after the index hospitalization (for those who survived to hospitalization). We used an inverse probability of treatment weighting (IPTW) approach to balance the differences in patient baseline variables between the two groups. Univariate linear and logistic regression models were built to estimate the effect of DA on hospital length of stay and the likelihood of postacute care transfer and ED return visit.

Key results

Among the 6583 patients included in the study, DA was associated with a lower hospital length of stay (estimate = -1.28; 95% CI = -1.76–0.80), and a lower likelihood of postacute care transfer (OR = 0.87; 95% CI = 0.77–0.97). It was not significantly associated with a lower risk of ED return visits (OR = 0.81; 95% CI = 0.60–1.08) in the following month.

Conclusion

DA should be prioritized, and reorganization of the geriatric pathway around DA should be encouraged due to the frailty of elderly individuals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03241-x.

Early diagnosis of Alzheimer's disease on ADNI data using novel longitudinal score based on functional principal component analysis

Methods: Alzheimer's disease (AD) is a worldwide prevalent age-related neurodegenerative disease with no available cure yet. Early prognosis is therefore crucial for planning proper clinical intervention. It is especially true for people diagnosed with mild cognitive impairment, to whom the prediction of whether and when the future disease onset would happen is particularly valuable. However, such prognostic prediction has been proven to be challenging, and previous studies have only achieved limited success. Approach: In this study, we seek to extract the principal component of the longitudinal disease progression trajectory in the early stage of AD, measured as the magnetic resonance imaging (MRI)-derived structural volume, to predict the onset of AD for mild cognitive impaired patients two years ahead. Results: Cross-validation results of LASSO regression using the longitudinal functional principal component (FPC) features show significant improved predictive power compared to training using the baseline volume 12 months before AD conversion [area under the receiver operating characteristic curve (AUC) of 0.802 versus 0.732] and 24 months before AD conversion (AUC of 0.816 versus 0.717). Conclusions: We present a framework using the FPCA to extract features from MRI-derived information collected from multiple timepoints. The results of our study demonstrate the advantageous predictive power of the population-based longitudinal features to predict the disease onset compared with using only cross-sectional data-based on volumetric features extracted from a single timepoint, demonstrating the improved prediction power using FPC-derived longitudinal features.

Multi-dimensional Measurements of Crowding for Pediatric Emergency Departments: A Systematic Review

The absence of accepted crowding measurement tools that encompass the unique characteristics of pediatric emergency departments (EDs) creates a deficit in advancing efforts to identify and evaluate solutions for this growing problem. In this systematic review, we examined 4 studies that reported on the development and testing of multidimensional pediatric ED crowding measurements. Two investigations involved models (PEDOCS, SOTU-PED) that measured factors indicative or contributory to crowding. A third investigation developed a model mapping the flow of patients through the pediatric ED. The final study modeled the magnitude of physician’s work load, particularly when this load is high when crowding is likely present, based on patient arrivals, presenting complaints and conditions, and tests ordered. These works from 4 studies on measuring crowding in pediatric EDs show promise, but this field is at an early stage. Future work should concentrate on comparing the utility of crowding measurements across multiple pediatric ED settings.

Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research

OBJECTIVE

To sample 40 physician organizations stratified on the basis of longitudinal cost of care measures for qualitative interviews in order to describe the range of care delivery structures and processes that are being deployed to influence the total costs of caring for patients.

DATA SOURCES

Three years of physician organization-level total cost of care data (n = 156 in California) from the Integrated Healthcare Association's value-based pay-for-performance program.

STUDY DESIGN

We fit total cost of care data using mixture and K-means clustering algorithms to segment the population of physician organizations into sampling strata based on 3-year cost trajectories (ie, cost curves).

PRINCIPAL FINDINGS

A mixture of multivariate normal distributions can classify physician organization cost curves into clusters defined by total cost level, shape, and within-cluster variation. K-means clustering does not accommodate differing levels of within-cluster variation and resulted in more clusters being allocated to unstable cost curves. A mixture of regressions approach focuses overly on anomalous trajectories and is sensitive to model coding.

CONCLUSIONS

Statistical clustering can be used to form sampling strata when longitudinal measures are of primary interest. Many clustering algorithms are available; the choice of the clustering algorithm can strongly impact the resulting strata because various algorithms focus on different aspects of the observed data.

Functional principal component analysis of glomerular filtration rate curves after kidney transplant

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

Exit block in emergency departments: a rapid evidence review

BACKGROUND

Exit block (or access block) occurs when 'patients in the ED requiring inpatient care are unable to gain access to appropriate hospital beds within a reasonable time frame'. Exit block is an increasing challenge for Emergency Departments (EDs) worldwide and has been recognised as a major factor in leading to departmental crowding. This paper aims to identify empirical evidence, highlighting causes, effects and strategies to limit exit block.

METHODS

A computerised literature search was conducted of English language empirical evidence published between 2008 and 2014 using a combination of terms relating to exit block in ED.

RESULTS

233 references were identified following the computerised search. Of these, 32 empirical articles of varying scientific quality were identified as relevant and results were presented under a number of headings. The majority of studies presented data relating to the impact of exit block on departments, patients and staff. A smaller number of articles evaluated interventions designed to reduce exit block. Evidence suggests that exit block is more likely to occur in more densely populated areas and less likely to occur in paediatric settings. Bed occupancy appears to be associated with exit block. Evidence supporting the impact of initiatives pointed towards increasing workforce and inpatient bed resources within the hospital setting to reduce block.

CONCLUSIONS

Further evidence is needed, especially within the NHS setting to increase the understanding around factors that cause exit block, and interventions that are shown to relieve it without compromising patient outcomes.

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards.

A framework for feature extraction from hospital medical data with applications in risk prediction

BACKGROUND

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities.

RESULTS

Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72).

CONCLUSIONS

The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Hippocampal shape analysis in Alzheimer's disease using functional data analysis

The hippocampus is one of the first affected regions in Alzheimer's disease. The left hippocampi of control subjects, patients with mild cognitive impairment and patients with Alzheimer's disease are represented by spherical harmonics. Functional data analysis is used in the hippocampal shape analysis. Functional principal component analysis and functional independent component analysis are defined for multivariate functions with two arguments. A functional linear discriminant function is also defined. Comparisons with other approaches are carried out. Our functional approach gives promising results, especially in shape classification.