Using Failure Mode and Effects Analysis in Blood Administration Process in Surgical Care Units: New Categories of Errors.

BACKGROUND: Blood administration failures and errors have been a crucial issue in health care settings. Failure mode and effects analysis is an effective tool for the analysis of failures and errors in such lifesaving procedures. These failures or errors would lead to adverse outcomes for patients during blood administration. OBJECTIVES: The study aimed to: use health care failure mode and effect analysis (HFMEA) for assessing potential failure modes associated with blood administration processes among nurses; develop a categorization of blood administration errors; and identify underlying reasons, proactive measures for identified failure modes, and corrective actions for identified high-risk failures. METHODS: A cross-sectional descriptive study was conducted in surgical care units by using observation, HFMEA, and brainstorming techniques. Prioritization of detected potential failures was performed by Pareto analysis. RESULTS: Eleven practical steps and 38 potential failure modes associated with 11 categories of errors were detected in this process. These categories of errors were newly developed in this study. In total, 17 of 38 potential failures were detected as high-risk failures that occurred during the sample-drawing, checking, preparing, administering, and monitoring steps. For cause analysis of failures and errors, proactive suggested actions were undertaken for 38 potential failure modes, and corrective actions for 17 high-risk failures. CONCLUSION: HFMEA is an efficient and well-organized tool for identification of and reduction in high-risk failures and errors in the blood administration process among nurses without building punitive culture. This tool also helps pay attention to redesigning and standardizing the blood administration process as well as providing training and educational programs for providing knowledge.

Penalized variable selection for accelerated failure time models with random effects.

Accelerated failure time (AFT) models allowing for random effects are linear mixed models under the log-transformation of survival time with censoring and describe dependence in correlated survival data. It is well known that the AFT models are useful alternatives to frailty models. To the best of our knowledge, however, there is no literature on variable selection methods for such AFT models. In this paper, we propose a simple but unified variable-selection procedure of fixed effects in the AFT random-effect models using penalized h-likelihood (HL). We consider four penalty functions (ie, least absolute shrinkage and selection operator (LASSO), adaptive LASSO, smoothly clipped absolute deviation (SCAD), and HL). We show that the proposed method can be easily implemented via a slight modification to existing h-likelihood estimation procedures. We thus demonstrate that the proposed method can also be easily extended to AFT models with multilevel (or nested) structures. Simulation studies also show that the procedure using the adaptive LASSO, SCAD, or HL penalty performs well. In particular, we find via the simulation results that the variable selection method with HL penalty provides a higher probability of choosing the true model than other three methods. The usefulness of the new method is illustrated using two actual datasets from multicenter clinical trials.

Analysis of adverse outcomes in the post-anesthesia care unit based on anesthesia liability data.

STUDY OBJECTIVE: The aim of this study is to provide a contemporary medicolegal analysis of claims brought against anesthesiologists in the United States for events occurring in the post-anesthesia care unit (PACU). DESIGN: In this retrospective analysis, we analyzed closed claims data from the Controlled Risk Insurance Company (CRICO) Comparative Benchmarking System (CBS) database. SETTING: Claims closed between January 1, 2010 and December 31, 2014 were included for analysis if the alleged damaging event occurred in a PACU and anesthesiology was named as the primary responsible service. PATIENTS: Forty-three claims were included for analysis. Data regarding ASA physical status and comorbidities were obtained, whenever available. Ages ranged from 18 to 94. Patients underwent a variety of surgical procedures. Severity of adverse outcomes ranged from temporary minor impairment to death. INTERVENTIONS: Patients receiving care in the PACU. MEASUREMENTS: Information gathered for this study includes patient demographic data, alleged injury type and severity, operating surgical specialty, contributing factors to the alleged damaging event, and case outcome. Some of these data were drawn directly from coded variables in the CRICO CBS database, and some were gathered by the authors from narrative case summaries. RESULTS: Settlement payments were made in 48.8% of claims. A greater proportion of claims involving death resulted in payment compared to cases involving other types of injury (69% vs 37%, p = 0.04). Respiratory injuries (32.6% of cases), nerve injuries (16.3%), and airway injuries (11.6%) were common. Missed or delayed diagnoses in the PACU were cited as contributing factors in 56.3% of cases resulting in the death of a patient. Of all claims in this series, 48.8% involved orthopedic surgery. CONCLUSIONS: The immediate post-operative period entails significant risk for serious complications, particularly respiratory injury and complications of airway management. Appropriate monitoring of patients by responsible providers in the PACU is crucial to timely diagnosis of potentially severe complications, as missed and delayed diagnoses were a factor in a number of the cases reviewed.

Efficient estimation for accelerated failure time model under case-cohort and nested case-control sampling.

Case-cohort (Prentice, 1986) and nested case-control (Thomas, 1977) designs have been widely used as a cost-effective alternative to the full-cohort design. In this article, we propose an efficient likelihood-based estimation method for the accelerated failure time model under case-cohort and nested case-control designs. An EM algorithm is developed to maximize the likelihood function and a kernel smoothing technique is adopted to facilitate the estimation in the M-step of the EM algorithm. We show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. The asymptotic variance of the estimators can be consistently estimated using an EM-aided numerical differentiation method. Simulation studies are conducted to evaluate the finite-sample performance of the estimators and an application to a Wilms tumor data set is also given to illustrate the methodology.

A Review of Healthcare Failure Mode and Effects Analysis (HFMEA) in Radiotherapy.

This paper presents a review of risk analyses in radiotherapy (RT) processes carried out by using Healthcare Failure Mode Effect Analysis (HFMEA) methodology, a qualitative method that proactively identifies risks to patients and corrects medical errors before they occur. This literature review was performed to provide an overview of how to approach the development of HFMEA applications in modern RT procedures, comparing recently published research conducted to support proactive programs to identify risks. On the basis of the reviewed literature, the paper suggests HFMEA shortcomings that need to be addressed.