IDENTIFYING SOURCES OF INEFFICIENCY IN HEALTHCARE.
Ontology highlight
ABSTRACT:
SUBMITTER: Chandra A
PROVIDER: S-EPMC7755154 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
IDENTIFYING SOURCES OF INEFFICIENCY IN HEALTHCARE.
Chandra Amitabh A Staiger Douglas O DO
The quarterly journal of economics 20200107 2
In medicine, the reasons for variation in treatment rates across hospitals serving similar patients are not well understood. Some interpret this variation as unwarranted, and push standardization of care as a way of reducing allocative inefficiency. An alternative interpretation is that hospitals with greater expertise in a treatment use it more because of their comparative advantage, suggesting that standardization is misguided. A simple economic model provides an empirical framework to separat ...[more]
Similar Datasets
Project description:BackgroundEvaluating hospital efficiency is a process to optimize resource utilization and allocation. This is vital due to hospitals being the largest financial cost in a health system. To limit avoidable uses of hospital resources, it is important to identify the sources of hospital inefficiencies and to put in place measures towards their reduction and elimination. Thus, the purpose of this research is to examine the sources of hospital inefficiency in the Eastern Mediterranean Region, and existing strategies tackling this issue.MethodsIn this study, the electronic databases MEDLINE (via PubMed), Web of Science, Embase, Google, Google Scholar, and reference lists of selected articles, were explored. Studies on inefficiency, sources of inefficiency, and strategies for inefficiency reduction in the Eastern Mediterranean region hospitals, published between January 1999 and May 2018, were identified. A total of 1466 articles were selected using the initial criteria. After further reviews based on the inclusion and exclusion criteria, 56 studies were eligible for this study. The chosen studies were conducted in Iran (n?=?35), Saudi Arabia (n?=?5), Tunisia (n?=?5), Jordan (n?=?4), Pakistan (n?=?2), the United Arab Emirates, Palestine, Iraq, Oman, and Afghanistan (n?=?1 each). These studies were analyzed using content analysis in MAXQDA 10.ResultsThe analysis showed that approximately 41% of studies used data envelopment analysis (DEA) to measure hospital efficiency. Sources of hospital inefficiency were divided into four categories for analysis: Hospital products and services, hospital workforce, hospital services delivery, and hospital system leakages.ConclusionThis study has revealed some sources of inefficiency in the Eastern Mediterranean Region hospitals. Inefficiencies are thought to originate from excess workforce, excess beds, inappropriate hospital sizes, inappropriate workforce composition, lack of workforce motivation, and inefficient use of health system inputs. It is suggested that health policymakers and managers use this evidence to develop appropriate strategies towards the reduction of hospital inefficiency.
| S-EPMC6852759 | biostudies-literature
Project description:The global diffusion of epidemics, computer viruses and rumors causes great damage to our society. One critical issue to identify the multiple diffusion sources so as to timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this chapter, we introduce an effective method to identify multiple diffusion sources, which can address three main issues in this area: (1) How many sources are there? (2) Where did the diffusion emerge? (3) When did the diffusion break out? For simplicity, we use rumor source identification to present the approach.
| S-EPMC7122829 | biostudies-literature
Project description:Yersinia pestis, the etiologic agent of plague, has shaped the course of human history, killing millions of people in three major pandemics. This bacterium is still endemic in parts of Asia, Africa, and the Americas, where it poses a natural disease threat to human populations. Y. pestis has also recently received attention as a possible bioterrorism agent. Thus, rapid methods to distinguish between bioterrorism and naturally occurring plague infections are of major importance. Our study is the first to demonstrate that variable-number tandem repeats (VNTRs) in the Y. pestis genome can link human case isolates to those obtained from suspected environmental sources of infection. We demonstrate the valuable utility of VNTR markers in epidemiological investigations of naturally occurring plague and the forensic analysis of possible bioterrorism events.
| S-EPMC548095 | biostudies-literature
Project description:Aggregation of macroparasites among hosts is a near-universal pattern, and has important consequences for the stability of host-parasite associations and the impacts of disease. Identifying which potential drivers are contributing to levels of aggregation observed in parasite-host associations is challenging, particularly for observational studies. We apply beta regressions in a Bayesian framework to determine predictors of aggregation, quantified using Poulin's index of discrepancy (D), for 13 species of parasites infecting Icelandic Rock Ptarmigan (Lagopus muta) collected over 12 years. 1,140 ptarmigan were collected using sampling protocols maximizing consistency of sample sizes and of composition of host ages and sexes represented across years from 2006-2017. Parasite species, taxonomic group (insect, mite, coccidian, or nematode), and whether the parasite was an ecto- or endoparasite were tested as predictors of aggregation, either alone or by modulating an effect of parasite mean abundance on D. Parasite species was an important predictor of aggregation in models. Despite variation in D across samples and years, relatively consistent aggregation was demonstrated for each specific host-parasite association, but not for broader taxonomic groups, after taking sample mean abundance into account. Furthermore, sample mean abundance was consistently and inversely related to aggregation among the nine ectoparasites, however no relationship between mean abundance and aggregation was observed among the four endoparasites. We discuss sources of variation in observed aggregation, sources both statistical and biological in nature, and show that aggregation is predictable, and distinguishable, among infecting species. We propose explanations for observed patterns and call for the review and re-analysis of parasite and other symbiont distributions using beta regression to identify important drivers of aggregation-both broad and association-specific.
| S-EPMC9419717 | biostudies-literature
Project description:ObjectiveTo examine the effects of policy, health system, and sociodemographic characteristics on the likelihood that uninsured persons pay a lower price at their regular source of care, or that they are aware of lower priced providers in their community.Data sourcesThe 2003 Community Tracking Study household survey, a nationally representative sample of the U.S. population and 60 randomly selected communities.Study designThe survey asked uninsured persons if they paid full or reduced cost at their usual source of medical care, or if they were aware of providers in their community that charge less for uninsured people. We use binomial and multinomial logistic regression analysis to examine the effects of various policy, health system, and sociodemographic characteristics on use and awareness of lower priced providers. We focus especially on the effects of safety-net capacity, measured by safety-net hospitals, community health centers, physicians' charity care, and Community Access Program (CAP) grants.Principal findingsLess than half of the uninsured (47.5 percent) reported that they used or were aware of a lower priced provider in their community. Multivariate regression analysis shows that greater safety-net capacity is associated with a higher likelihood of having a lower priced provider as the regular source of care and greater awareness of lower priced providers. Lower incomes and racial/ethnic minorities also had a higher likelihood of having a lower priced provider, although health status did not have statistically significant effects.ConclusionAlthough increased safety-net capacity may lead to more uninsured having a lower priced provider, many uninsured who live near safety-net providers are not aware of their presence. Greater outreach designed to increase awareness may be needed in order to increase the effectiveness of safety-net providers in improving access to care for the uninsured.
| S-EPMC1955746 | biostudies-literature
Project description:Most proteins undergo different kinds of modification after translation. Protein acetylation is one of the most crucial post-translational modifications, which causes direct or indirect impact on various biological activities in vivo. As a member of Class III HDACs, SIRT1 was the closest one to the yeast sir2 and drew most attention, while a small number of known SIRT1 substrates caused difficulties to clarify its function. In this work, we designed a novel computational method to screen SIRT1 substrates based on manually collected data and Support Vector Machines (SVMs). Unlike other approaches, we took both primary sequence and protein functional features into consideration. Through integrating functional features, the Matthews correlation coefficient (MCC) for the prediction increased from 0.10 to 0.65. The prediction results were verified by independent dataset and biological experiments. The validation results demostrated that our classifier could effectively identify SIRT1 substrates and filter appropriate candidates for further research. Furthermore, we provide online tool to support SIRT1 substrates prediction, which is freely available at http://bioinfo.bjmu.edu.cn/huac/ .
| S-EPMC5496887 | biostudies-other
Project description:BackgroundMethylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis.ResultsA sizeable proportion of systematic variability due to variables expressing 'batch' and 'sample position' within 'chip' was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals' methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to 'batch' (1.3%) and 'sample position' (0.6%), and in a diminished variability attributable to 'chip' within a batch (0.9%). After ComBat or the residuals' corrections, a larger number of significant sites (k?=?600 and k?=?427, respectively) were associated to smoking status than the SVA correction (k?=?96).ConclusionsThe three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.
| S-EPMC5863487 | biostudies-literature
Project description:Radon is the leading source of lung cancer mortality after smoking in Chiang Mai, Thailand. Finding a source of carcinogens is one of the important measures for preventing the cancer risk for this region. Specific sites at Pa Miang, Doi Saket have the highest incidences of lung cancer and have a combination of factors that influence indoor radon concentration. Our study identified the sources of indoor radon within several houses. The results indicate that geological and topographic characteristics, including active faults and mountain terraces, are the main sources of indoor radon, especially for wooden houses. Besides building materials, the design of the houses, ventilation conditions, and lifestyle choices are all factors influencing indoor radon concentrations and its associated risk. Although radon levels (29-101 Bq m-3) and total indoor annual effective doses (0.9-3.8 mSv year-1) received from all sources at these sites have shown no significant health risk due to radon exposure , this investigation will be useful as a starting point to guide strategies to respond and prevent the risk of lung cancer, especially in Chiang Mai.
| S-EPMC7576592 | biostudies-literature
Project description:Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifacts. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models.
| S-EPMC2771876 | biostudies-literature
Project description:Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.
| S-EPMC4104449 | biostudies-literature