Beth Andrews

Changes in Performance After Implementation of a Multifaceted Electronic-Health-Record-Based Quality Improvement System

Background:Electronic health record (EHR) systems have the potential to revolutionize quality improvement (QI) methods by enhancing quality measurement and integrating multiple proven QI strategies. Objectives:To implement and evaluate a multifaceted QI intervention using EHR tools to improve quality measurement (including capture of contraindications and patient refusals), make point-of-care reminders more accurate, and provide more valid and responsive clinician feedback (including lists of patients not receiving essential medications) for 16 chronic disease and preventive service measures. Design:Time series analysis at a large internal medicine practice using a commercial EHR. Subjects:All adult patients eligible for each measure (range approximately 100–7500). Measures:The proportion of eligible patients who satisfied each measure after removing those with exceptions from the denominator. Results:During the year before the intervention, performance improved significantly for 8 measures. During the year after the intervention, performance improved significantly for 14 measures. For 9 measures, the primary outcome improved more rapidly during the intervention year than during the previous year (P < 0.001 for 8 measures, P = 0.02 for 1). Four other measures improved at rates that were not significantly different from the previous year. Improvements resulted from increases in patients receiving the service, documentation of exceptions, or a combination of both. For 5 drug-prescribing measures, more than half of physicians achieved 100% performance. Conclusions:Implementation of a multifaceted QI intervention using EHR tools to improve quality measurement and the accuracy and timeliness of clinician feedback improved performance and/or accelerated the rate of improvement for multiple measures simultaneously.

Maximum likelihood estimation for α-stable autoregressive processes

PCT No. PCT/SE87/00116 Sec. 371 Date Aug. 26, 1988 Sec. 102(e) Date Aug. 26, 1988 PCT Filed Mar. 9, 1987 PCT Pub. No. WO87/05261 PCT Pub. Date Sep. 11, 1987.A pressure vessel of composite material built up by fiber wound around an elongated hollow body having at least one end fitting with an axially extending peripheral surface which has a plurality of protrusions distributed over the surface with portions of the fiber material passing over the end fitting and applied around the protrusions as to be peripherally and axially distributed thereover and molded into the composite material. The pressure vessel may also include two end fittings with different radii with the body having on an axially limited portion thereof radially extending pin-like protrusions distributed around its circumference with the fibers being wound in different directions relative to the axis on opposite sides of the protrusions with the directions being adapted to the radii of the end fittings.

Rank-based estimation for all-pass time series models

An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models are useful for identifying and modeling noncausal and noninvertible autoregressive-moving average processes. We establish asymptotic normality and consistency for rank-based estimators of all-pass model parameters. The estimators are obtained by minimizing the rank-based residual dispersion function given by Jaeckel [Ann. Math. Statist. 43 (1972) 1449--1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The behavior of the estimators for finite samples is studied via simulation and rank estimation is used in the deconvolution of a simulated water gun seismogram.

Time series models with asymmetric Laplace innovations

We propose autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedastic (GARCH) models driven by asymmetric Laplace (AL) noise. The AL distribution plays, in the geometric-stable class, the analogous role played by the normal in the alpha-stable class, and has shown promise in the modelling of certain types of financial and engineering data. In the case of an ARMA model we derive the marginal distribution of the process, as well as its bivariate distribution when separated by a finite number of lags. The calculation of exact confidence bands for minimum mean-squared error linear predictors is shown to be straightforward. Conditional maximum likelihood-based inference is advocated, and corresponding asymptotic results are discussed. The models are particularly suited for processes that are skewed, peaked, and leptokurtic, but which appear to have some higher order moments. A case study of a fund of real estate returns reveals that AL noise models tend to deliver a superior fit with substantially less parameters than normal noise counterparts, and provide both a competitive fit and a greater degree of numerical stability with respect to other skewed distributions.

Rank-Based Estimation for Autoregressive Moving Average Time Series Models

We establish asymptotic normality and consistency for rank-based estimators of autoregressive-moving average model parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449-1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation. Copyright 2007 The Author Journal compilation 2007 Blackwell Publishing Ltd.

Rank-Based Estimation for GARCH Processes

We consider a rank-based technique for estimating generalized autoregressive conditionally heteroskedastic (GARCH) model parameters, some of which are scale transformations of conventional GARCH parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given in Jaeckel (1972, Annals of Mathematical Statistics 43, 1449–1458). They are useful for GARCH order selection and preliminary estimation. We give a limiting distribution for the rank estimators that holds when the true parameter vector is in the interior of its parameter space and when some GARCH parameters are zero. The limiting theory is used to show that the rank estimators are robust, can have the same asymptotic efficiency as maximum likelihood estimators, and are relatively efficient compared to traditional Gaussian and Laplace quasi-maximum likelihood estimators. The behavior of the estimators for finite samples is studied via simulation, and we use rank estimation to fit a GARCH model to exchange rate log-returns.