How did healthcare professionals define patient engagement in quality management? A survey study.

Background: Patient and family engagement (PFE) can positively impact the patient experience and care process outcomes. There is no unique type of PFE, and the process is usually defined by the quality management department or professionals responsible for this process in the hospital. The objective of this study is to define PFE in quality management based on the professional's perspective.

How do hospitals engage patients and family members in quality management? A grounded theory study of hospitals in Brazil.

Background: Patient and family engagement (PFE) is considered an essential element of the transformation of the healthcare system. However, it is characterised by its complexity and a small number of institutions that have implemented the mechanisms of engagement.

Objective: To understand PFE in quality management (QM) in the hospital environment.

Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of São Paulo.

This work aims to obtain an artificial neural network to simulate hospitalizations for respiratory diseases influenced by pollutant gaseous such as CO, PM, PM, NO, O, and SO emitted from 2011 to 2017, in the city of São Paulo. The hospitalization costs were also be calculated. MLP and RBF neural networks have been tested by varying the number of neurons in the hidden layer and the type of equation of the output function.

CUSUM chart to monitor autocorrelated counts using Negative Binomial GARMA model.

Cumulative sum control charts have been used for health surveillance due to its efficiency to detect soon small shifts in the monitored series. However, these charts may fail when data are autocorrelated. An alternative procedure is to build a control chart based on the residuals after fitting autoregressive moving average models, but these models usually assume Gaussian distribution for the residuals.

CUSUM control charts to monitor series of Negative Binomial count data.

To detect outbreaks of diseases in public health, several control charts have been proposed in the literature. In this context, the usual generalized linear model may be fitted for counts under a Negative Binomial distribution with a logarithm link function and the population size included as offset to model hospitalization rates. Different statistics are used to build CUSUM control charts to monitor daily hospitalizations and their performances are compared in simulation studies.