Frictions as Barriers to Perioperative Alignment

Abstract

The quality of the relationship between the sterile processing department (SPD) and the operating room (OR) is an important determinant of OR safety and performance. In this article, the concept of "friction" refers to the SPD behaviors and attributes that can negatively affect OR performance. Panels of SPD professionals initially were asked to identify and operationally define different ways in which behaviors of a hospital's SPD could compromise OR performance. A national convenience sample of OR nurses (N=291) rated 14 frictions in terms of their agreement or disagreement that each had a negative effect on OR performance in their hospital. Overall, more than 50% of the entire sample agreed that 2 frictions, "SPD does not communicate effectively with the OR" (55%) and "SPD inventories are insufficient for surgical volume" (52%), had negative effect on OR performance. However, a latent class analysis revealed 3 distinct classes of nurses who varied with respect to their level of agreement that SPD-OR frictions negatively affected OR performance. The observed heterogeneity in how different groups of nurses viewed different frictions suggests that effective efforts aimed at reducing performance-limiting frictions should be customized so that resources can be used where they are most needed.

Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster Analysis

“…The parameters of the LCCA models can be treated as representing the class membership probability and the conditional response probability is estimated using a maximum likelihood criterion based on the expectation maximization algorithm. 25 The pattern of conditional response probabilities within each class provides an interpretational framework for understanding derived classes. 10 Generally, the conditional response probability pattern is relatively homogeneous/similar for the participants within a given class but heterogeneous/different from the patterns of participants who are members of the other classes.…”

“…The number of classes was based on several goodness-of-fit measures, including the log-likelihood ratio χ 2 , akaike information criteria (AIC), and Bayesian information criterion (BIC). 25 The smaller AIC and/or BIC indicates a better model. The final classification was selected based on the BIC values, number of parameters, size of classification errors, and interpretability.…”