CPs are effective interventions for improving teamwork, increasing the organizational level of care processes, and decreasing risk of burnout for health care teams in an acute hospital setting. Through this, high-performance teams can be built.
Motivated by a longitudinal oral health study, we evaluate the performance of binary Markov models in which the response variable is subject to an unconstrained misclassification process and follows a monotone or progressive behavior. Theoretical and empirical arguments show that the simple version of the model can be used to estimate the prevalence, incidences, and misclassification parameters without the need of external information and that the incidence estimators associated with the model outperformed approaches previously proposed in the literature. We propose an extension of the simple version of the binary Markov model to describe the relationship between the covariates and the prevalence and incidence allowing for different classifiers. We implemented a Bayesian version of the extended model and show that, under the settings of our motivating example, the parameters can be estimated without any external information. Finally, the analyses of the motivating problem are presented.
Bayesian methods are increasingly used in proof-of-concept studies. An important benefit of these methods is the potential to use informative priors, thereby reducing sample size. This is particularly relevant for treatment arms where there is a substantial amount of historical information such as placebo and active comparators. One issue with using an informative prior is the possibility of a mismatch between the informative prior and the observed data, referred to as prior-data conflict. We focus on two methods for dealing with this: a testing approach and a mixture prior approach. The testing approach assesses prior-data conflict by comparing the observed data to the prior predictive distribution and resorting to a non-informative prior if prior-data conflict is declared. The mixture prior approach uses a prior with a precise and diffuse component. We assess these approaches for the normal case via simulation and show they have some attractive features as compared with the standard one-component informative prior. For example, when the discrepancy between the prior and the data is sufficiently marked, and intuitively, one feels less certain about the results, both the testing and mixture approaches typically yield wider posterior-credible intervals than when there is no discrepancy. In contrast, when there is no discrepancy, the results of these approaches are typically similar to the standard approach. Whilst for any specific study, the operating characteristics of any selected approach should be assessed and agreed at the design stage; we believe these two approaches are each worthy of consideration.
BackgroundPatient safety can be increased by improving the organization of care. A tool that evaluates the actual organization of care, as perceived by multidisciplinary teams, is the Care Process Self-Evaluation Tool (CPSET). CPSET was developed in 2007 and includes 29 items in five subscales: (a) patient-focused organization, (b) coordination of the care process, (c) collaboration with primary care, (d) communication with patients and family, and (e) follow-up of the care process. The goal of the present study was to further evaluate the psychometric properties of the CPSET at the team and hospital levels and to compile a cutoff score table.MethodsThe psychometric properties of the CPSET were assessed in a multicenter study in Belgium and the Netherlands. In total, 3139 team members from 114 hospitals participated. Psychometric properties were evaluated by using confirmatory factor analysis (CFA), Cronbach’s alpha, interclass correlation coefficients (ICCs), Kruskall-Wallis test, and Mann–Whitney test. For the cutoff score table, percentiles were used. Demographic variables were also evaluated.ResultsCFA showed a good model fit: a normed fit index of 0.93, a comparative fit index of 0.94, an adjusted goodness-of-fit index of 0.87, and a root mean square error of approximation of 0.06. Cronbach’s alpha values were between 0.869 and 0.950. The team-level ICCs varied between 0.127 and 0.232 and were higher than those at the hospital level (0.071-0.151). Male team members scored significantly higher than females on 2 of the 5 subscales and on the overall CPSET. There were also significant differences among age groups. Medical doctors scored significantly higher on 4 of the 5 subscales and on the overall CPSET. Coordinators of care processes scored significantly lower on 2 of the 5 subscales and on the overall CPSET. Cutoff scores for all subscales and the overall CPSET were calculated.ConclusionsThe CPSET is a valid and reliable instrument for health care teams to measure the extent care processes are organized. The cutoff table permits teams to compare how they perceive the organization of their care process relative to other teams.
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