Analysis of the Internal Bed Regulation Committees from hospitals of a Southern Brazilian city

Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.

[Regulatory aspects and measures of the Emergency Care Network: a game of disputes between the public and private interests]

The study analyzes regulatory aspects and measures as social production in the Emergency Care Network (RUE) of two health regions. This is a multiple case study of qualitative character, performed via 61 interviews with public administrators, users, and health services managers. The analysis had as theoretical reference the Theory of Social Production. We identified professional, lay, clientelistic, and governmental regulatory measures, in the systemic aspects, of the services and of access. The main results point to regulatory flows produced by movements of various social actors, with emphasis on the action of representatives of hospital service providers, especially private ones, characterizing the proposal of another regime: market regulation. We emphasize the limits and powers of arrangements, such as hospital and the Mobile Emergency Care Service (SAMU) regulation centers, the internal hospital regulation centers, and the use of WhatsApp. Health regulation in RUE consists of complex, contradictory, and conflicting social processes, whose flows are produced at the limit between the public and private interests.

Hospital indicators after implementation of bed regulation strategies: an integrative review

OBJECTIVES

to analyze the scientific evidence available in literature on hospital indicators after implementation of bed regulation strategies.

METHODS

this is an integrative review conducted with studies available in five databases and in the reference database of the Center for Study and Research in Nursing Services Management in October 2019. Articles on hospital bed management, available in full in English, Spanish or Portuguese, without temporal delimitation were included.

RESULTS

1,118 eligible articles were found, of which 37 were duplicated. Among 1,081 pre-selected studies, 112 studies were eligible and 11 articles were included. Six studies addressed the emergency services. Three addressed hospital indicators in general, another focused on a psychiatric ward and one analyzed the indicators of two hospitals administered differently.

CONCLUSIONS

the studies focused on emergency services, demonstrating the importance of organizing these services for health institutions.

Indicators of surgical patients after the implementation of an Internal Bed Regulation Committee in a university hospital

OBJECTIVE

To analyze the indicators of surgical patients after the implementation of an Internal Bed Regulation Committee in a university hospital.

METHOD

Longitudinal, quantitative, and retrospective study. The data collection was conducted in the Hospital Management Information institutional system, from which the information of patients submitted to surgical procedures from January 2015 to June 2018 were obtained. To verify the data trends, a simple linear regression model was used.

RESULTS

The predominance of patients aged 20 to 39 and hospitalized on an emergency basis was observed. An ascending trend for structure indicators was verified regarding the number of surgical procedures and patients per surgical room. The process indicators were stagnant. An ascending trend was presented by the result indicators related to the number of surgical patients, hospitalized surgical patients, surgical procedures, and patients with Hospitalization Authorization.

CONCLUSION

A change in the mean values of the process indicators was observed, showing the performance of this service. Organizational changes were also observed regarding the establishment of norms, processes, and flows.