Economic Evaluation of Telemedicine Consultations to Reduce Unnecessary Neonatal Care Transfers

Improving Efficiency in Healthcare: Lessons from Successful Health Policies in Chile

BACKGROUND

Chile has made significant progress in recent decades in implementing policies to improve the efficiency of its health system with an impact on population health.

AIM

To present five case studies of successful policies whose impact has been documented.

METHODS

Case study report.

RESULTS

First, we present a summary of the evidence supporting the policy that is changing the Chilean care model from a problem-based to a patient-centered care model. Second, we show how tele-nephrology and advanced renal care units have demonstrated significant impact on chronic kidney disease in Chile. This internationally recognized successful Chilean policy is contributing to address one of the conditions that explains the highest financial burden on the health system. Third, we present recent evidence on the effectiveness of teleoncology care in Chile. Fourth, we highlight the most recent system of epidemiological surveillance implemented in Chile, the EPIVIGILA system, which was essential to support decisions throughout the pandemic. Finally, we underline the health benefit plans implemented in recent decades to improve access to services and financial protection.

CONCLUSIONS

Chile has successfully implemented policies in its health system that have an impact on efficiency and population health. These experiences can be replicated in countries facing similar challenges, using the Chilean experience as a benchmark.

Benefits of Neurosurgical Teleconsults in the Management of Intracerebral Hemorrhage: Transfers and Transportation Cost Reduction

BACKGROUND

Our study explores the efficacy and economic benefits of neurosurgical teleconsultations in managing intracerebral hemorrhage (ICH), focusing on reducing unnecessary patient transfers and associated costs.

METHODS

We conducted a cost-savings analysis at our institution of a previously published pilot study involving a cohort of patients with ICH who were potential candidates for airlift to our tertiary care center but instead received neurosurgical consultation via teleconsultation to avoid the transfer. Data on patient demographics, distances, and costs were collected and analyzed to assess the economic impact of teleconsultations.

RESULTS

The cohort comprised 14 patients; we noted significant cost savings from avoiding interhospital transfers, ranging from $84,346.52 to $120,495.03 per patient. Teleconsultations facilitated immediate, collaborative decision-making between healthcare providers at community hospitals and a tertiary care center, reducing the need for expensive air transportation and unnecessary hospital transfers.

CONCLUSIONS

Neurosurgical teleconsultations offer a cost-effective alternative to traditional patient transfer methods for ICH management, providing substantial economic benefits while maintaining high physician and patient-family satisfaction levels. This study underscores the potential of our teleneurosurgery program to significantly reduce costs by reducing unnecessary financial burdens on patients' families and healthcare systems.

Interfacility Emergency Department Transfer for Midface Fractures in the United States

PURPOSE

Interfacility hospital transfer for isolated midfacial fractures is common but rarely clinically necessary. The purpose of this study was to generate nationally representative estimates regarding the incidence, risk factors, and cost of transfer for isolated midface fractures.

METHODS

This was a retrospective cohort study using the Nationwide Emergency Department Sample 2018 to identify patients with isolated midface fractures. The primary predictor variable was hospital trauma center designation (Level I, Level II, Level III, and nontrauma center). The primary outcome variable was hospital transfer. Total emergency department (ED) charges were also assessed. Covariates were demographic, medical, injury-related, and hospital characteristics. Descriptive, bivariate, and multiple logistic regression statistics were used to evaluate the incidence and predictors of interfacility transfer.

RESULTS

During the study period, there were 161,022 ED encounters with a midface fracture as primary diagnosis, of which 5,680 were transferred (3.53%). In an unadjusted analysis, evaluation at a nontrauma center, level III trauma center, nonteaching hospital, and numerous demographic, medical, and injury-related variables were associated with transfer (P ≤ .001). In the adjusted model, the strongest independent predictors for hospital transfer were evaluation at a nontrauma center (odds ratio [OR] = 16.2, 95% confidence interval [CI] = 13.6-19.4), level III trauma center (OR = 13.4, 95% CI = 11.1-16.1) or level II trauma center (OR = 3.25, 95% CI = 2.66-3.98), any Le Fort fracture (OR = 12.0, 95% CI = 10.4-14.0), orbital floor fracture (OR = 3.73, 95% CI = 3.48-4.00), history of cerebrovascular event (OR = 2.74, 95% CI = 2.18-3.45), and cervical spine injury (OR = 5.87, 95% CI = 4.79-7.20) (P ≤ .001). The average ED charge per encounter was $7,206 ± 9,294 for a total nationwide charge of approximately 1.16 billion dollars. Transferred subjects had total ED charges of $97 million, not including additional charges at the recipient hospital.

CONCLUSION

Isolated midface fractures are transferred infrequently, but given the high incidence have substantial healthcare costs. Predictors of transfer were mixed rather than clustered within one variable type, although it is likely that transfers are driven in part by lack of access to maxillofacial specialists given the predominance of hospital covariates. Programs evaluating necessity of transfer and facilitating specialist evaluation in the outpatient setting may reduce healthcare expenditures for these injuries.

S. A machine learning approach on analysing the sentiments in the adoption of telemedicine application during COVID-19

Purpose

The purpose of this paper is to understand the customer sentiment towards telemedicine apps and also to apply machine learning algorithms to analyse the sentiments in the adoption during the COVID-19 pandemic.

Design/methodology/approach

Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like support vector machine (SVM) and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix.

Findings

The paper explores the customer sentiment towards telemedicine apps and their adoption during the COVID-19 pandemic. Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like SVM and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix. The customers who used telemedicine apps have positive sentiment as well as negative sentiment towards the telemedicine apps. Some of the customers have concerns about the medicines delivered, their delivery time, the quality of service and other technical difficulties. Even a small percentage of doctors feel uncomfortable in online consultation through the application.

Originality/value

The primary value of this paper lies in providing an overview of the customers’ approach towards the telemedicine apps, especially during the COVID-19 pandemic.