Improving clinical records: their role in decision-making and healthcare management – COVID-19 perspectives

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.

Reconfigure rehabilitation services during the Covid-19 pandemic: best practices from Southwest China

PURPOSE

The healthcare system and service capacity are overwhelmed by the Covid-19 pandemic. There is a substantial unmet need for rehabilitation service to all patients affected by Covid-19 directly or indirectly. This article aims to describe how to rapidly reconfigure and bring rehabilitation services back during the pandemic.

METHOD

The Steer committee meeting was held for the development of a strategic preparedness plan and safety management based on principles and evidence of rehabilitation, which will lead to effective mitigation of consequences resulted from Covid-19.

RESULTS

Five measures were taken in Southwest China during the pandemic, which includes the "First contact responsibility" and management system; the full-coverage system for nosocomial infection control; the "Closed-off management" system; the full-coverage system for body temperature monitoring; the adoption of 5G-based telerehabilitation. With the implementation of these measures, the capacity and capability were enhanced to safely reopen and operate rehabilitation facilities in Southwest China.

CONCLUSION

Further measurement of quality of care and outcomes during and beyond the pandemic is needed in transforming the healthcare system and improving rehabilitation services. Hopefully, the positive message conveyed by this paper could encourage and support communities and the society of physical medicine and rehabilitation worldwide during this challenging time. IMPLICATIONS FOR REHABILITATION Rehabilitation services are essential and there is an unmet need posed by the Covid-19 pandemic. A feasible strategic plan and safety management measures are critical to reconfigure the capacity and capability of rehabilitation services suspended by Covid-19. The adoption of tele-rehabilitation technology has the potential to reshape public health emergency responses and the delivery of care. Measurement of quality and outcomes is of great importance to inform transformation and adaptation of rehabilitation services during and after the Covid-19 pandemic.