Can a Patient’s In-Hospital Length of Stay and Mortality Be Explained by Early-Risk Assessments?

<div><p>Objective</p><p>To assess whether a patient’s in-hospital length of stay (LOS) and mortality can be explained by early objective and/or physicians’ subjective-risk assessments.</p><p>Data Sources/Study Setting</p><p>Analysis of a detailed dataset of 1,021 patients admitted to a large U.S. hospital between January and September 2014.</p><p>Study Design</p><p>We empirically test the explanatory power of objective and subjective early-risk assessments using various linear and logistic regression models.</p><p>Principal Findings</p><p>The objective measures of early warning can only weakly explain LOS and mortality. When controlled for various vital signs and demographics, objective signs lose their explanatory power. LOS and death are more associated with physicians’ early subjective risk assessments than the objective measures.</p><p>Conclusions</p><p>Explaining LOS and mortality require variables beyond patients’ initial medical risk measures. LOS and in-hospital mortality are more associated with the way in which the human element of healthcare service (e.g., physicians) perceives and reacts to the risks.</p></div

Logistic Regression Analysis Results for Mortality.

<p>Logistic Regression Analysis Results for Mortality.</p

Regression Analysis Results for LOS.

<p>Regression Analysis Results for LOS.</p

Regression Analysis Results for Physician’s Subjective Assessments (severity level).

<p>Regression Analysis Results for Physician’s Subjective Assessments (severity level).</p

Descriptive Statistics of Variables.

<p>Descriptive Statistics of Variables.</p

Statistical process control for multistage processes with non-repeating cyclic profiles

<p>In many manufacturing processes, process data are observed in the form of time-based profiles, which may contain rich information for process monitoring and fault diagnosis. Most approaches currently available in profile monitoring focus on single-stage processes or multistage processes with repeating cyclic profiles. However, a number of manufacturing operations are performed in multiple stages, where non-repeating profiles are generated. For example, in a broaching process, non-repeating cyclic force profiles are generated by the interaction between each cutting tooth and the workpiece. This article presents a process monitoring method based on Partial Least Squares (PLS) regression models, where PLS regression models are used to characterize the correlation between consecutive stages. Instead of monitoring the non-repeating profiles directly, the residual profiles from the PLS models are monitored. A Group Exponentially Weighted Moving Average control chart is adopted to detect both global and local shifts. The performance of the proposed method is compared with conventional methods in a simulation study. Finally, a case study of a hexagonal broaching process is used to illustrate the effectiveness of the proposed methodology in process monitoring and fault diagnosis.</p