Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

Determinants of public institutional births in India: An analysis using the National Family Health Survey (NFHS-5) factsheet data

Background

Institutional births ensure deliveries happen under the supervision of skilled healthcare personnel in an enabling environment. For countries like India, with high neonatal and maternal mortalities, achieving 100% coverage of institutional births is a top policy priority. In this respect, public health institutions have a key role, given that they remain the preferred choice by most of the population, owing to the existing barriers to healthcare access. While research in this domain has focused on private health institutions, there are limited studies, especially in the Indian context, that look at the enablers of institutional births in public health facilities. In this study, we look to identify the significant predictors of institutional birth in public health facilities in India.

Method

We rely on the National Family Health Survey (NFHS-5) factsheet data for analysis. Our dependent variable (DV) in this study is the % of institutional births in public health facilities. We first use Welch's t-test to determine if there is any significant difference between urban and rural areas in terms of the DV. We then use multiple linear regression and partial F-test to identify the best-fit model that predicts the variation in the DV. We generate two models in this study and use Akaike's Information Criterion (AIC) and adjusted R2 values to identify the best-fit model.

Results

We find no significant difference between urban and rural areas (P = 0.02, α =0.05) regarding the mean % of institutional births in public health facilities. The best-fit model is an interaction model with a moderate effect size (Adjusted2 = 0.35) and an AIC of 179.93, lower than the competitive model (AIC = 183.56). We find household health insurance (β = -0.29) and homebirth conducted under the supervision of skilled healthcare personnel (β = -0.56) to be significant predictors of institutional births in public facilities in India. Additionally, we observe low body mass index (BMI) and obesity to have a synergistic impact on the DV. Our findings show that the interaction between low BMI and obesity has a strong negative influence (β = -0.61) on institutional births in public health facilities in India.

Conclusion

Providing households with health insurance coverage may not improve the utilisation of public health facilities for deliveries in India, where other barriers to public healthcare access exist. Therefore, it is important to look at interventions that minimise the existing barriers to access. While the ultimate objective from a policy perspective should be achieving 100% coverage of institutional births in the long run, a short-term strategy makes sense in the Indian context, especially to manage the complications arising during births outside an institutional setting.

Nursing students' critical thinking and associated factors in Vietnam: A multicenter cross-sectional study

AIMS

This study aimed to evaluate the critical thinking abilities of senior nursing students in Vietnam and determine factors associated with their critical thinking disposition and skills.

BACKGROUND

Assessing critical thinking competence is crucial for determining senior nursing students' preparedness for entering the healthcare workforce and can be used to examine current nursing education's ability to cultivate nursing students' critical thinking. However, little research was found on critical thinking among Vietnamese nursing students.

DESIGN

A multicenter cross-sectional research design.

METHODS

A convenience sample of 533 senior nursing students from six universities in Vietnam participated in this study. All participants completed the online questionnaires, including basic information, a subscale of the Motivated Strategy for Learning Questionnaire (MSLQ), Critical Thinking Disposition Scale (CTDS), and Critical Thinking Self-Assessment Scale (CTSAS).

RESULTS

The mean score for the CTDS was 42.81 (standard deviation (SD) = 5.29), while the mean score for the CTSAS was 168.29 (SD = 44.43). Results of the multiple linear-regression analysis showed that an increase in self-study hours per day (B = 0.41, p = 0.007), higher self-efficacy in learning and performance (B = 0.26, p < 0.001), and a more-supportive environment (B = 0.97, p < 0.001) were predictors of critical thinking disposition. Moreover, an increase in self-study hours per day (B = 4.09, p = 0.001), higher self-efficacy in learning and performance (B = 2.65, p < 0.001), a more-supportive environment (B = 7.74, p < 0.001), and more experience with research (B = 7.03, p = 0.03) were predictors of critical thinking skills.

CONCLUSIONS

This study revealed that senior nursing students in Vietnam possess a moderate level of critical thinking abilities. Those students who dedicate more hours to self-study, demonstrate higher self-efficacy in learning and performance, experience a supportive environment, and engage in more research activities exhibit better critical thinking disposition and skills. The findings highlight the ongoing need to enhance critical thinking disposition and skills of nursing students in Vietnam. It is suggested that nursing faculty members should develop the appropriate strategies to improve nursing students' critical thinking disposition and skills.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

Background: In recent years, the length of hospital stay (LOS) following endarterectomy has decreased significantly from 4 days to 1 day. LOS is influenced by several common complications and factors that can adversely affect the patient’s health and may vary from one healthcare facility to another. The aim of this work is to develop a forecasting model of the LOS value to investigate the main factors affecting LOS in order to save healthcare cost and improve management. Methods: We used different regression and machine learning models to predict the LOS value based on the clinical and organizational data of patients undergoing endarterectomy. Data were obtained from the discharge forms of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy). R² goodness of fit and the results in terms of accuracy, precision, recall and F1-score were used to compare the performance of various algorithms. Results: Before implementing the models, the preliminary correlation study showed that LOS was more dependent on the type of endarterectomy performed. Among the regression algorithms, the best was the multiple linear regression model with an R² value of 0.854, while among the classification algorithms for LOS divided into classes, the best was decision tree, with an accuracy of 80%. The best performance was obtained in the third class, which identifies patients with prolonged LOS, with a precision of 95%. Among the independent variables, the most influential on LOS was type of endarterectomy, followed by diabetes and kidney disorders. Conclusion: The resulting forecast model demonstrates its effectiveness in predicting the value of LOS that could be used to improve the endarterectomy surgery planning.

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.