Improving patient care in hospital in India by monitoring influential parameters

A case study of lean digital transformation through robotic process automation in healthcare

Under Taiwan's National Health Insurance (NHI) system, it's crucial for all healthcare providers to accurately submit medical expense claims to the National Health Insurance Administration (NHIA) to avoid incorrect deductions. With changes in healthcare policies and adjustments in hospital management strategies, the complexity of claiming rules has resulted in hospitals expending significant manpower and time on the medical expense claims process. Therefore, this study utilizes the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) management approach to identify wasteful and non-value-added steps in the process. Simultaneously, it introduces Robotic Process Automation (RPA) tools to replace manual operations. After implementation, the study effectively reduces the process time by 380 min and enhances Process Cycle Efficiency (PCE) from 69.07 to 95.54%. This research validates a real-world case of Lean digital transformation in healthcare institutions. It enables human resources to be allocated to more valuable and creative tasks while assisting hospitals in providing more comprehensive and patient-centric services.

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.

S. Impact of AI regulation and governance on online personal data sharing: from sociolegal, technology and policy perspective

Purpose

The purpose of this study is to investigate the impacts of regulations and governance of artificial intelligence (AI) on personal data sharing (PDS) in the context of sociolegal, technology and policy perspective.

Design/methodology/approach

With the help of theories and literature review, some hypotheses have been formulated and a conceptual model has been developed. These are statistically validated. The validated model has been compared again using impact of regulation and governance of AI as a moderator. The validation has been done using survey by PLS analysis.

Findings

The study found that there is a high level of positive impact of regulation and governance of AI on the online PDS by the users.

Research limitations/implications

This study has provided a statistical model which can provide the antecedents of PDS by the online users with the impact of AI regulation and governance as a moderator. The proposed model has explanative power of 92%.

Practical implications

The study highlighted that there is a necessity of having appropriate AI regulations so that users could share their personal data online without any hesitation. Policymakers and legal fraternity should work together to formulate a comprehensive AI regulation and governance framework.

Originality/value

To the best of the authors’ knowledge, there is no study on the impact of AI regulation and governance towards PDS and how it impacts on the security, privacy and trust of the online users.