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Background: Prior research has explored the link between health information technology (HIT) and performance of accountable care organizations (ACOs). However, the challenges of HIT use in ACOs for the management of chronic diseases among Medicare beneficiaries remain less examined. Purpose: Given the high costs of implementing HIT and the occurrence of multiple chronic conditions (MCC) among elderly individuals, it is important to understand the extent to which HIT capabilities enable chronic disease management among the Medicare population. Methodology/Approach: Regression analysis was conducted using data from multiple sources for the year 2017, including Leavitt Partners data, the ACO Public Use File published by the Shared Savings Program of the Centers for Medicare & Medicaid Services (CMS), and the CMS hospital referral region data. The sample consisted of 470 ACOs. Results: Findings revealed that health information exchange (HIE)- and HIT-enabled patient engagement reduced unplanned admissions for Medicare patients with MCC. When primary care services were utilized, HIE- and HIT-enabled patient engagement and medication reconciliation further decreased unplanned admissions. Conclusion: This study provides empirical support for HIT's role in reinforcing the applicability of the chronic care model to improve health outcomes. Practice Implications: From the managerial perspective, adopting HIT functions that support care management may be important for ACOs to improve patient outcomes. [ABSTRACT FROM AUTHOR]

The ten key characteristics (KCs) of carcinogens are based on characteristics of known human carcinogens and encompass many types of endpoints. We propose that an objective review of the large amount of cancer mechanistic evidence for the chemical bisphenol A (BPA) can be achieved through use of these KCs. A search on metabolic and mechanistic data relevant to the carcinogenicity of BPA was conducted and web-based software tools were used to screen and organize the results. We applied the KCs to systematically identify, organize, and summarize mechanistic information for BPA, and to bring relevant carcinogenic mechanisms into focus. For some KCs with very large data sets, we utilized reviews focused on specific endpoints. Over 3000 studies for BPA from various data streams (exposed humans, animals, in vitro and cell-free systems) were identified. Mechanistic data relevant to each of the ten KCs were identified, with receptor-mediated effects, epigenetic alterations, oxidative stress, and cell proliferation being especially data rich. Reactive and bioactive metabolites are also associated with a number of KCs. This review demonstrates how the KCs can be applied to evaluate mechanistic data, especially for data-rich chemicals. While individual entities may have different approaches for the incorporation of mechanistic data in cancer hazard identification, the KCs provide a practical framework for conducting an objective examination of the available mechanistic data without a priori assumptions on mode of action. This analysis of the mechanistic data available for BPA suggests multiple and inter-connected mechanisms through which this chemical can act. [ABSTRACT FROM AUTHOR]

In September 2020, a global aerosol forecasting model was implemented as an ensemble member of the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting System (GEFS) v12.0.1 (hereafter referred to as "GEFS-Aerosols"). In this study, GEFS-Aerosols simulation results from September 1, 2019 to September 30, 2020 were evaluated using an aerosol budget analysis. These results were compared with results from other global models as well as reanalysis data. From this analysis, the global average lifetimes of black carbon (BC), organic carbon (OC), dust, sea salt, and sulfate are 4.06, 4.29, 4.59, 0.34 and 3.3 days, respectively, with the annual average loads of 0.135, 1.29, 4.52, 6.80 and 0.50 TG. Compared to National Aeronautics and Space Administration (NASA)'s Goddard Earth Observing System-Goddard Chemistry Aerosol and Radiation Transport Model (GEOS4-GOCART), the aerosols in GEFS-Aerosols have a relatively short lifetime because of the faster removal processes in GEFS-Aerosols. Meanwhile, in GEFS-Aerosols, aerosol emissions are the determining factor for the mass and composition of aerosols in the atmosphere. The size (bin) distribution of aerosol emissions is as important as its total emissions, especially in simulations of dust and sea salt. Also most importantly, the strong monthly and interannual variations in natural sources of aerosols in GEFS-Aerosols suggests that improving the accuracy of prognostic concentrations of aerosols is important for applying aerosol feedback to weather and climate predictions. [ABSTRACT FROM AUTHOR]

This study assessed the distribution of Covid Provider Relief Funds (PRFs) to 3,886 private and public general acute care hospitals during 2020–2022. Marginal effects from two-part regression were analyzed. More than 13% of study hospitals did not receive PRFs. Some targeted groups of hospitals, that is, safety-net hospitals and high-impact hospitals (those with high COVID-19 admissions), were the most likely to receive PRFs. Hospitals providing the most uncompensated care, and facilities serving counties with high concentrations of Black or Hispanic populations, were less likely to receive PRFs. Among facilities receiving subsidies, rural, high-impact, safety-net, and financially vulnerable hospitals received more PRFs in relation to their total revenues. Those serving impoverished communities received a larger proportion of PRFs relative to their total revenues, while those in areas with a high concentration of Hispanics received a smaller proportionate subsidy. [ABSTRACT FROM AUTHOR]

Trade-in programs are offered extensively in business-to-business (B2B) markets. The success of such programs depends on well-designed and executed trade-in policies as well as accurate prediction of return flow to [...]

Objective: This study sought to understand the relationship of hospital performance with high-level electronic medical record (EMR) adoption, hospitalists staffing levels, and their potential interaction. Materials and methods: We evaluated 2,699 non-federal, general acute hospitals using 2016 data merged from four data sources. We performed ordinal logistic regression of hospitals' total performance score (TPS) on their EMR capability and hospitalists staffing level while controlling for other market- and individual-level characteristics. Results: Hospitalists staffing level is shown to be positively correlated with TPS. High-level EMR adoption is associated with both short-term and long-term improvement on TPS. Large, urban, non-federal government hospitals, and academic medical centers tend to have lower TPS compared to their respective counterparts. Hospitals belonging to medium- or large-sized healthcare systems have lower TPS. Higher registered nurse (RN) staffing level is associated with higher TPS, while higher percentage of Medicare or Medicaid share of inpatient days is associated with lower TPS. Discussion: Although the main effects of hospitalists staffing level and EMR capability are significant, their interaction is not, suggesting that hospitalists and EMR act through separate mechanisms to help hospitals achieve better performance. When hospitals are not able to invest on both simultaneously, given financial constraints, they can still reap the full benefits from each. Conclusion: Hospitalists staffing level and EMR capability are both positively correlated with hospitals' TPS, and they act independently to bolster hospital performance. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this article was to investigate the organizational and market-level variables associated with sustained superior hospital performance on Value-Based Purchasing total performance scores (TPS). Methodology: TPS for 2014 through 2017 was obtained from the Centers for Medicare & Medicaid Services Hospital Compare website. Market-level data were from the 2017 Area Health Resource File, and hospital-level data were from the 2014 American Hospital Association Annual Survey database. We specified a logistic regression model to identify significant predictors of hospitals with sustained superior performance on TPS, that is, "sustainers." Principal Findings: Only 8.4% of hospitals were classified as sustainers. Hospitals located in rural markets with a high Medicare Advantage penetration had a higher likelihood of being classified as sustainers. High RN staffing levels, lower Medicare share of inpatient days, not-for-profit ownership, and small size were all significant organizational predictors of sustained superior performance. Conclusions: Both modifiable characteristics, such as nurse staffing levels, and nonmodifiable characteristics, such as rural markets and small hospital size, are associated with the likelihood of hospitals sustaining superior performance over time. Practice Implications: Managers need to carefully examine their staffing levels as they pursue interventions to sustain high TPS overtime. Moreover, factors such as Medicare share of inpatient days and size need to be considered when understanding barriers to sustained performance on Value-Based Purchasing domains. [ABSTRACT FROM AUTHOR]

Objective (or Study Question): To examine the association between hospitalists staffing levels and contract type with CMS Total Performance Score (TPS).Data Sources/study Setting: Total performance scores were obtained from CMS, hospital-level data from the 2015 American Hospital Association Annual Survey Database, and unemployment rates from the Area Resource Health File.Study Design: We used cluster analysis to classify hospitals based on the distribution of various hospitalist contracts, and we used regression analysis to examine the association between TPS and hospitalist staffing levels and contract distributions. Hospital-level predictors included hospitalists staffing levels, RN staffing levels, and Magnet status. Market-level variables were unemployment rates and competition.Principal Findings: Higher staffing levels of employed hospitalists or hospitalists with a group contract are associated with higher TPS (with coefficient estimates of 0.85 and 0.83, respectively, and the same standard error of 0.22). Higher staffing levels of hospitalists under individual contract are negatively associated with TPS (with coefficient estimate of -0.43 and standard error of 0.21). Based on the regression analysis using hospital clusters as independent variables, hospitals with individual contracts or without hospitalists providing care had significantly worse TPS compared to hospitals that predominantly employ hospitalists (with coefficient estimate of -1.80 and standard error of 0.61). Magnet status, RN staffing levels, and small and medium size were positively associated with TPS. Medicare share of inpatient days, teaching status, AMCs, and for-profit and public nonfederal ownership were negatively associated with TPS.Conclusions: Adequate hospitalist staffing level is important for hospitals to achieve better performance. Hospitals need to consider the mix of arrangements or contracts that they have with hospitalists. [ABSTRACT FROM AUTHOR]

With the growing availability of digitized text data both publicly and privately, there is a great need for effective computational tools to automatically extract information from texts. Because the Chinese language differs most significantly from alphabet-based languages in not specifying word boundaries, most existing Chinese text-mining methods require a prespecified vocabulary and/or a large relevant training corpus, which may not be available in some applications. We introduce an unsupervised method, top-down word discovery and segmentation (TopWORDS), for simultaneously discovering and segmenting words and phrases from large volumes of unstructured Chinese texts, and propose ways to order discovered words and conduct higher-level context analyses. TopWORDS is particularly useful for mining online and domain-specific texts where the underlying vocabulary is unknown or the texts of interest differ significantly from available training corpora. When outputs from TopWORDS are fed into context analysis tools such as topic modeling, word embedding, and association pattern finding, the results are as good as or better than that from using outputs of a supervised segmentation method. [ABSTRACT FROM AUTHOR]

Email marketing has been an increasingly important tool for today’s businesses. In this paper, we propose a counting-process-based Bayesian method for quantifying the effectiveness of email marketing campaigns in conjunction with customer characteristics. Our model explicitly addresses the seasonality of data, accounts for the impact of customer characteristics on their purchasing behavior, and evaluates effects of email offers as well as their interactions with customer characteristics. Using the proposed method, together with a propensity-scorebased unit-matching technique for alleviating potential confounding, we analyze a large email marketing data set of an online ticket marketplace to evaluate the short- and long-term effectiveness of their email campaigns. It is shown that email offers can increase customer purchase rate both immediately and during a longer term. Customers’ characteristics such as length of shopping history, purchase recency, average ticket price, average ticket count, and number of genres purchased also affect customers’ purchase rate. A strong positive interaction is uncovered between email offer and purchase recency, suggesting that customers who have been inactive recently are more likely to take advantage of promotional offers., Statistics

The tumor suppressor LKB1 is a serine/threonine protein kinase that is frequently mutated in human lung adenocarcinoma (LUAD). LKB1 regulates a complex signaling network that is known to control cell polarity and metabolism; however, the pathways that mediate the tumor suppressive activity of LKB1 are incompletely defined. To identify mechanisms of LKB1- mediated growth suppression we developed a spheroid-based cell culture assay to study LKB1- dependent growth. Using this assay, along with genome-wide CRISPR screens and validation with orthogonal methods, we discovered that LKB1 suppresses growth, in part, by activating the PIKFYVE lipid kinase, which promotes the internalization of wild-type EGFR. Our findings reveal a new mechanism of regulation of EGFR, which may have implications for the treatment of LKB1 -mutant LUAD.