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Author(s): Ren, Ai; Golden, Bruce; Alt, Frank; Wasil, Edward; Bjarnadottir, Margret; Hirshon, Jon Mark; Pimentel, Laura | Abstract: Introduction: On January 1, 2014, the State of Maryland implemented the Global Budget Revenue (GBR) program. We investigate the impact of GBR on length of stay (LOS) for inpatients in emergency departments (ED) in Maryland.Methods: We used the Hospital Compare data reports from the Centers for Medicare and Medicaid Services (CMS) and CMS Cost Reports Hospital Form 2552-10 from January 1, 2012–March 31, 2016, with GBR hospitals from Maryland and hospitals from West Virginia (WV), Delaware (DE), and Rhode Island (RI). We implemented difference-in-differences analysis and investigated the impact of GBR implementation on the LOS or ED1b scores of Maryland hospitals using a mixed-effects model with a state-level fixed effect, a hospital-level random effect, and state-level heterogeneity.Results: The GBR impact estimator was 9.47 (95% confidence interval [CI], 7.06 to 11.87, p-valuel0.001) for Maryland GBR hospitals, which implies, on average, that GBR implementation added 9.47 minutes per year to the time that hospital inpatients spent in the ED in the first two years after GBR implementation. The effect of the total number of hospital beds was 0.21 (95% CI, 0.089 to 0.330, p-value = 0 .001), which suggests that the bigger the hospital, the longer the ED1b score. The state-level fixed effects for WV were -106.96 (95% CI, -175.06 to -38.86, p-value = 0.002), for DE it was 6.51 (95% CI, -8.80 to 21.82, p-value=0.405), and for RI it was -54.48 (95% CI, -82.85 to -26.10, p-valuel0.001).Conclusion: Our results indicate that GBR implementation has had a statistically significant negative impact on the efficiency measure ED1b of Maryland hospital EDs from January 2014 to April 2016. We also found that the significant state-level fixed effect implies that the same inpatient might experience different ED processing times in each of the four states that we studied. [West J Emerg Med.

A control chart is proposed which monitors the conformance of a sample to an in-control distribution using the quantile or inverse cumulative distribution function. This method permits detecting changes in the distributional shape which may be undetecte..

A model for the measurements from a multiple stream process is analysed as a special case of a multivariate control chart by principal components analysis. The principal components meet the objective of partitioning the control problem into statistics that are sensitive to two types of assignable causes. Simple test statistics result and performance analyses are provided.

Principal components are useful for multivariate process control. Typically, the principal component variables are often selected to summarize the variation in the process data. We provide an analysis to select the principal component variables to be included in a multivariate control chart that incorporates the unique aspects of the process control problem (rather than using traditional principal component guidelines).

A significant practical disadvantage of multivariate statistical process control is that it is difficult to determine which of the monitored variables is responsible for the out-of-control signal. While there have been several proposed techniques and diagnostics, most of these have disadvantages. Rather than an ad hoc selection, we use three approaches to develop a diagnostic for a chi-square chart and generate the same statistic from each approach. Consequently, intuitive interpretations of the diagnostic are provided and previous proposals are simplified by a focus on a single metric.

A vector case parallel to that described by Ledolter and Abraham ( Technometrics , 23 (1981) 411-414) is extended for an approximation of one-step ahead forecast error covariance for vector ARMA models.

The objective of the study reported in this paper was to examine the role of deep cuts in capital expenditures as a firm's strategy for becoming more efficient and, in turn, more competitive. Based on an empirical study, support is provided for the argument that deep cuts in capital expenditures may be a viable strategy for realigning corporate priorities toward a more efficient operation. The measure of performance utilized in this paper is the stock market reaction to announced deep cuts.

In this study, the effect of incorrect order identification on parameter estimation and forecasting for the vector autoregressive moving average model has been conducted via a Monte Carlo simulation. Five parameter sets, labeled A to E, for a bivariate ARMA(1,1) model were specified within the feasible regions of stationarity and invertibility. It was found that the estimated parameter values for the ARMA(2,1) and ARMA(1,2) models had biases approaching zero, while those for the ARMA(2,0), ARMA(1,0) and ARMA(0,1) models exhibited persistent biases. The forecast findings for the underfitted models are contrary to the univariate results of Ledolter and Abraham (1981) and Kunitomo and Yamamoto (1985). However the findings are consistent with Akaike's (1969). This study has shown that an underfitted model is less desirable than an overfitted one in term of forecast error variances.

Simultaneous confidence intervals for the p means of a multivariate normal random variable with known variances may be generated by the projection method of Scheffe and by the use of Bonferroni's inequality. It has been conjectured that the Bonferroni intervals are shorter than the Scheffe intervals, at least for the usual confidence levels. This conjecture is proved for all p≥2 and all confidence levels above 50%. It is shown, incidentally, that for all p≥2 Scheffe's intervals are shorter for sufficiently small confidence levels. The results are also applicable to the Bonferroni and Scheffe intervals generated for multinomial proportions.

Consider an “intractable” optimization problem for which no efficient solution technique exists. Given a systematic procedure for generating independent heuristic solutions, we seek to obtain interval estimates for the globally optimal solution using statistical inference. In previous work, accurate point estimates have been derived. Determining interval estimates, however, is a considerably more difficult task. In this paper, we develop straightforward procedures which compute confidence intervals efficiently in order to evaluate heuristic solutions and assess deviations from optimality. The strategy presented is applicable to a host of combinatorial optimization problems. The assumptions of our model, along with computational experience, are discussed.

An evaluation was conducted of the effects of several state-level “Dealer Day in Court” laws designed to protect franchisee-dealers from unfair terminations by the major oil companies. The results suggest that these laws were somewhat effective at slowing the decline in the number of dealer-operated stations during the 1970s, but that competition and efficiency in gasoline retailing were not significantly impacted. Market forces favoring vertical integration and new forms of competition in gasoline retailing apparently reduced the influence of these laws.