Nonparametric Frontier Model as a Tool for Exploratory Analysis of Hospital Stays

2001 ◽  
Vol 40 (03) ◽  
pp. 241-247
Author(s):  
C. Beguin
Keyword(s):  
Length Of Stay ◽  
Diagnosis Related Groups ◽  
Exploratory Analysis ◽  
Hospital Financing ◽  
Financing System ◽  
Free Disposal Hull ◽  
Free Disposal ◽  
Large Databases ◽  
Hospital Stays ◽  
Belgian Hospital

Abstract:Diagnosis-related groups (DRG) were introduced in 1995 to the Belgian hospital financing system. Trimming rules are generally used when mean length of stay (LOS) is estimated by DRG. This paper proposes the use of frontier models instead of trimming rules. These models allow to take into account the characteristics of the patients, to rank hospital stays, and to indicate stays presenting discrepancy between the patient’s characteristics and the resources consumed. The analysis is done with the nonparametric Free Disposal Hull (FDH) model and the method developed by Wilson to detect extreme observations, when defining the frontier is adapted to analyze large databases.

The association of psychiatric and neurological comorbidities with outcomes in traumatic injury patients

2021 ◽  
pp. 009121742198914
Author(s):  
Jennifer Brady ◽  
R David Hayward ◽  
Elango Edhayan
Keyword(s):  
Length Of Stay ◽  
Psychiatric Illness ◽  
Traumatic Injury ◽  
Lower Mortality ◽  
Linear Modeling ◽  
Psychiatric Comorbidities ◽  
Trauma Outcomes ◽  
Hospital Stays ◽  
The Impact ◽  
The Relationship

Introduction Mental illness is a well-known risk factor for injury and injury recidivism. The impact of pre-existing psychiatric illness on trauma outcomes, however, has received less attention. Our study examines the relationship of pre-existing psychiatric illness on trauma outcomes including length of stay, cost, and mortality. Methods Patient data were obtained from the Healthcare Cost and Utilization Project’s State Inpatient Database. All patients admitted for trauma in the Detroit metropolitan area from 1/1/2006 to 12/31/2014 were included. The relationship between individual psychiatric comorbidities (depression, psychosis, and other neurological disorders) and outcomes were evaluated with logistic regression (mortality) and generalized linear modeling (length of stay and cost). Results Over 260,000 records were reviewed. Approximately one-third (29.9%) of patients had one or more psychiatric diagnoses. Patients with depression had longer hospital stays (RR = 1.12, p < 0.001) and higher costs (RR = 1.07, p < 0.001), but also lower mortality (OR = 0.69, p < 0.001). Patients with psychosis had longer stays (RR = 1.18, p < 0.001), higher costs (RR = 1.02, p = 0.002), and lower mortality (OR = 0.61, p < 0.001). Patients with other neurological comorbidities had higher mortality (OR = 1.23, p < 0.001), longer stays (RR = 1.29, p < 0.001), and higher costs (RR = 1.10, p < 0.001). Conclusion Patients with a psychiatric disorder required longer care and incurred greater costs, whereas mortality was higher for only those with a neurological disorder. Identifying patients’ psychiatric comorbidities at the time of admission for trauma may help optimize treatment. Addressing these conditions may help reduce the cost of trauma care.

scholarly journals Order Picking and E-Commerce: Introducing Non-Parametric Efficiency Measurement for Sustainable Retail Logistics

2021 ◽  
Vol 16 (4) ◽  
pp. 846-858
Author(s):  
Matthias Klumpp ◽  
Dominic Loske
Keyword(s):  
Goodness Of Fit ◽  
New Technologies ◽  
Order Picking ◽  
Free Disposal Hull ◽  
Free Disposal ◽  
Efficiency Assessment ◽  
Parametric Data ◽  
Proposed Model ◽  
Dea Models ◽  
Non Parametric

Order picking is a crucial but labor- and cost-intensive activity in the retail logistics and e-commerce domain. Comprehensive changes are implemented in this field due to new technologies like AI and automation. Nevertheless, human worker’s activities will be required for quite some time in the future. This fosters the necessity of evaluating manual picker-to-part operations. We apply the non-parametric Data Envelopment Analysis (DEA) to evaluate the efficiency of n = 23 order pickers processing 6109 batches with 865,410 stock keeping units (SKUs). We use distance per location, picks per location, as well as volume per SKU as inputs and picks per hour as output. As the convexity axiom of standard DEA models cannot be fully satisfied when using ratio measures with different denominators, we apply the Free Disposal Hull (FDH) approach that does not assume convexity. Validating the efficiency scores with the company’s efficiency assessment, operationalized by premium payments shows a 93% goodness=of-fit for the proposed model. The formulated non-parametric approach and its empirical application are promising ways forward in implementing empirical efficiency measurements for order picking operations within e-commerce operations.

scholarly journals Many small steps to reducing length of stay: A visit to the DH pilot enhanced recovery scheme

2010 ◽  
Vol 92 (8) ◽  
pp. 266-268
Author(s):  
Matthew Worrall
Keyword(s):  
Length Of Stay ◽  
Health Service ◽  
Patient Experience ◽  
Enhanced Recovery ◽  
Enhanced Recovery Programme ◽  
Department Of Health ◽  
Recovery Scheme ◽  
Hospital Stays ◽  
Improve Patient

Enhanced recovery (ER) is one of the current buzz terms in the health service but it seems to mean a different thing depending on to whom you speak. The Department of Health (DH) invited applications from acute trusts across England to become 'innovation sites' for the enhanced recovery programme. These sites are supported by DH as they implement a defined programme that aims to improve patient experience through shorter hospital stays. The Bulletin spent a day at one of them, West Hertfordshire Hospitals NHS Trust, to witness the changes made.

scholarly journals WHEN BIAS KILLS THE VARIANCE: CENTRAL LIMIT THEOREMS FOR DEA AND FDH EFFICIENCY SCORES

2014 ◽  
Vol 31 (2) ◽  
pp. 394-422 ◽  
Author(s):  
Alois Kneip ◽  
Léopold Simar ◽  
Paul W. Wilson
Keyword(s):  
Central Limit ◽  
Limit Theorems ◽  
Central Limit Theorems ◽  
Data Envelopment ◽  
Free Disposal Hull ◽  
Average Efficiency ◽  
Free Disposal ◽  
Monte Carlo Experiments ◽  
The Individual ◽  
Individual Scores

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiencies of production units. In applications, both efficiency scores for individual units as well as average efficiency scores are typically reported. While several bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods have existed for making inference about mean efficiency levels. This paper shows that standard central limit theorems do not apply in the case of means of DEA or FDH efficiency scores due to the bias of the individual scores, which is of larger order than either the variance or covariances among individual scores. The main difficulty comes from the fact that such statistics depend on efficiency estimators evaluated at random points. Here, new central limit theorems are developed for means of DEA and FDH scores, and their efficacy for inference about mean efficiency levels is examined via Monte Carlo experiments.

scholarly journals How Efficiently Do Elite US Universities Produce Highly Cited Papers?

Publications ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 4 ◽  
Author(s):  
Klaus Wohlrabe ◽  
Félix de Moya Anegon ◽  
Lutz Bornmann
Keyword(s):  
Research Evaluation ◽  
Research Output ◽  
Harvard University ◽  
Institutional Research ◽  
Free Disposal Hull ◽  
Highly Cited Papers ◽  
Boston College ◽  
Free Disposal ◽  
Data Efficiency ◽  
Highly Cited

While output and impact assessments were initially at the forefront of institutional research evaluations, efficiency measurements have become popular in recent years. Research efficiency is measured by indicators that relate research output to input. The additional consideration of research input in research evaluation is obvious, since the output depends on the input. The present study is based on a comprehensive dataset with input and output data for 50 US universities. As input, we used research expenses, and as output the number of highly-cited papers. We employed Data Efficiency Analysis (DEA), Free Disposal Hull (FDH) and two more robust models: the order-m and order-α approaches. The results of the DEA and FDH analysis show that Harvard University and Boston College can be called especially efficient compared to the other universities. While the strength of Harvard University lies in its high output of highly-cited papers, the strength of Boston College is its small input. In the order-α and order-m frameworks, Harvard University remains efficient, but Boston College becomes super-efficient. We produced university rankings based on adjusted efficiency scores (subsequent to regression analyses), in which single covariates (e.g., the disciplinary profile) are held constant.

SOLUTION METHODS FOR NONCONVEX FREE DISPOSAL HULL MODELS: A REVIEW AND SOME CRITICAL COMMENTS

2014 ◽  
Vol 31 (01) ◽  
pp. 1450010 ◽  
Author(s):  
KRISTIAAN KERSTENS ◽  
IGNACE VAN DE WOESTYNE
Keyword(s):  
Technical Efficiency ◽  
Computational Efficiency ◽  
Recent Article ◽  
Free Disposal Hull ◽  
Free Disposal ◽  
Solution Methods ◽  
Minimum Costs ◽  
Empirical Illustration

This note first succinctly summarizes the currently available methods to solve the various nonconvex free disposal hull (FDH) models for technical efficiency as well as for minimum costs. It also offers some empirical illustration as to their computational efficiency. Second, this note briefly points out that the recent article by (26) and its correction by (4) proposing an extended enumeration method to solve for technical efficiency evaluated relative to this family of FDH models contain no original results.

scholarly journals Multimodal Analgesia in Lumbar Fusion Surgery: Impact Amidst the Opioid Epidemic

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Yi Lu ◽  
Erica Bertoncini
Keyword(s):  
Length Of Stay ◽  
Lumbar Fusion ◽  
Multimodal Analgesia ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Control Group ◽  
P Value ◽  
Standard Group ◽  
Fusion Surgery ◽  
Hospital Stays

Abstract INTRODUCTION Spine surgery traditionally relies on opioid analgesics for postoperative pain management. Opioids are associated with prolonged hospital stays and opioid use disorders. Opioid-focused prescribing habits in surgery have partially contributed to the opioid epidemic. METHODS A retrospective analysis was performed comparing patients receiving a multimodal analgesia regimen after lumbar fusion surgery vs control group receiving standard analgesia regimen. The multimodal regimen consisted of Acetaminophen 975 mg TID, Toradol 7.5 mg Q6 hours for 24-ho followed by Celebrex 100 mg BID for 7-d, Robaxin 500 mg Q6 hours prn for muscle spasms, Gabapentin 300 mg/100 mg TID for 4-wk, and prn narcotic. The standard regimen consisted of Acetaminophen 975 mg TID, narcotic prn, and muscle relaxant prn. There were 12 patients in the multimodal group and 26 patients in the control group evaluated over 3-mo and 6-mo time periods respectively. Primary outcomes included hospital length-of-stay, total and IV narcotic requirements in Morphine Milligram Equivalent (MME), and VASS pain scores. RESULTS Study results demonstrate differences between patient populations when focusing on the opioid-naïve participants. Opioid-naïve patients in the multimodal group were found to have significantly lower IV narcotic requirement than the control (0.22+/−0.67 mg/d for multimodal vs 5.36+/−5.56 mg/d for standard group, P-value = .001). These patients also had shorter hospital stays than the control (2.78+/−0.83 d for multimodal vs 3.53+/−1.17 d for standard group) but the difference was just below our threshold for significance (P-value = .066). Including both opioid-naïve and opioid-tolerant patients, no significant differences were found in hospital length-of-stay, MME, IV narcotic requirement nor VASS score between the multimodal group and the control groups (P-values of .46, .81, .36, and .91, respectively). CONCLUSION Overall, the study favors using multimodal analgesia in those undergoing lumbar spinal fusion surgeries as evident by considerably reduced IV narcotic requirement and nearly significant shortened hospital length-of-stay in opioid-naïve patients compared to control.

scholarly journals Characteristics of hospitalizations with influenza diagnosis, France, 2012-2017

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
M Pivette ◽  
V de Lauzun ◽  
N Nicolay ◽  
A Scanff ◽  
B Hubert
Keyword(s):  
Length Of Stay ◽  
Diagnosis Related Groups ◽  
Laboratory Data ◽  
Incidence Rates ◽  
H3n2 Virus ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Age Group ◽  
Hospital System ◽  
The Impact

Abstract Background Seasonal influenza surveillance in France is based on several data sources (ambulatory data, emergency department and intensive care unit (ICU) admissions, laboratory data, mortality). However, the data do not provide a complete measure of the impact of the epidemics on the hospital system. The objective of the study was to describe the characteristics of influenza hospitalizations from the French national hospital discharge database (PMSI) between 2012 and 2017 and to precise the burden of influenza by age group and by season. Methods All hospitalizations in metropolitan France with at least one ICD-10 code related to influenza (J09, J10, J11) as a principal, related or associated diagnosis between 1 July 2012 to 30 June 2017 were extracted from the PMSI. For each season, the total number of hospitalizations, admissions to ICU, incidence and lethality rates, lengths of stay and classification in diagnosis-related groups were described by age group. Results During the 5 seasons, 91 255 hospitalizations with an influenza-diagnosis were identified. The incidence varied significantly between seasons, from 12.7/100 000 in 2013-2014 to 45.9/100 000 in 2016-2017. A high number of cases was observed in elderlies in 2014-2015 and 2016-2017, marked by the circulation of A (H3N2) virus. The proportion of hospitalizations with an admission in ICU was 10%, and was higher in the 40-79 age group (19%). Lethality increased steadily with age, from 0.5% under 20 years to 10% in 80 years and older. Length of stay also increased with age. Significant regional disparities were observed, with higher incidence rates in South-Eastern France each season. Conclusions The analysis of influenza hospitalizations from the PMSI provides important elements on influenza burden, not available in the current surveillance systems. An annual analysis, stratified by age group, would provide an indicator of the impact of the epidemics on hospital system at the end of each influenza season. Key messages Important influenza incidence variations were observed between seasons by age groups. Severity and impact of influenza (mortality, ICU, length of stay) varied significantly by age group.

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