Technology Management for Accelerated Recovery during COVID-19: A Data-Driven Machine Learning Approach

Objective- The research looks forward to extracting strategies for accelerated recovery during the ongoing Covid-19 pandemic. Design - Research design considers quantitative methodology and evaluates significant factors from 170 countries to deploy supervised and unsupervised Machine Learning techniques to generate non-trivial predictions. Findings - Findings presented by the research reflect on data-driven observation applicable at the macro level and provide healthcare-oriented insights for governing authorities. Policy Implications - Research provides interpretability of Machine Learning models regarding several aspects of the pandemic that can be leveraged for optimizing treatment protocols. Originality - Research makes use of curated near-time data to identify significant correlations keeping emerging economies at the center stage. Considering the current state of clinical trial research reflects on parallel non-clinical strategies to co-exist with the Coronavirus

Application of Artificial Intelligence and Blockchain in healthcare management - donor organ transplant system

Purpose: Research ventures to expand the reach of organ transplant mechanisms to improve the abysmally low organ transplant rate in the country. The research deploys state of the art technologies to promote deceased organ donation using the donor organ transplant system. Research methodology: The exploratory study focuses on addressing the limitation of resources using a Socio-material view.  The research utilizes qualitative content analysis to reflect on the knowledge drawn from the artifacts. Results: The presented study leverages the capabilities of Artificial Intelligence and Blockchain technologies to benefit from the convergence. In line with the concept of 'Texture of Practices,' research provides recommendations to augment the organ transplant system in terms of procurement, coordination, and transplantation. Limitations: Drawing the knowledge from the case studies, research strives to understand the reality and interaction of actors in a healthcare context. Considering the complex nature of the organ transplant process, the study is limited to the Indian scenario and cannot be generalized. Contribution: Research identifies the requirement of a unified digital interface and encourages the integration of emergency health services to facilitate operational processes during organ transplants. Keywords: Healthcare, Organ transplant, A.I., Blockchain, Texture of practice

Reinforcing Positive Cognitive States with Machine Learning: An Experimental Modeling for Preventive Healthcare

Societal evolution has resulted in a complex lifestyle where we give most attention to our physical health leaving psychological health less prioritized. Considering the complex relationship between stress and psychological well-being, this study bases itself on the cognitive states experienced by us. The presented research offers insight into how state-of-the-art technologies can be used to support positive cognitive states. It makes use of the brain-computer interface (BCI) that drives the data collection using electroencephalography (EEG). The study leverages data science to devise machine learning (ML) model to predict the corresponding stress levels of an individual. A feedback loop using “Self Quantification” and “Nudging” offer real-time insights about an individual. Such a mechanism can also support the psychological conditioning of an individual where it does not only offer spatial flexibility and cognitive assistance but also results in enhanced self-efficacy. Being part of quantified self-movement, such an experimental approach could showcase personalized indicators to reflect a positive cognitive state. Although ML modeling in such a data-driven approach might experience reduced diagnostic sensitivity and suffer from observer variability, it can complement psychosomatic treatments for preventive healthcare

Augmenting Psychological Well-being using Artificial Intelligence: Reflections on the Workplace Productivity

The presented doctoral thesis is titled “Augmenting Psychological Well-being using Artificial Intelligence: Reflections on Workplace Productivity”. It offers insight into how technology can be used to support psychological health. This study makes use of Healthcare IoT (IoMT) and Artificial Intelligence (A.I.) to fulfill the same. The study, with its interdisciplinary approach, focuses on the augmentation of psychosomatic health using A.I. and considers its impact on an individual to extend reflections on organizational performance. Health is an essential component of life. We take care of physical health, but mental health is usually taken for granted where it must be given the same care and importance. Psychosomatic health is nothing but a holistic reflection of both the physical and mental health of an individual. As per the pilot study, the root causes of the same are related to events relating to workplaces, finances, and relationships. As studies indicate, stress, anxiety, and depression are the signs of degrading mental health; considering service research priorities, the presented research empirically explores the impact of positive emotions on psychological well-being. Observing the complexity of neural constructs, Artificial Intelligence is deployed to be able to gain valuable insights. At the same time, keeping a managerial point of view in research reflects on co-created value in an organization achieved through a person’s well-being. Literature suggests that seeking therapy may be the only option while dealing with psychological issues, but it could be a time-consuming, expensive process with limited access to society. We do have technologies that have advanced over the last few decades but are mainly focused on supporting physical health. The presented study offers insight into how it can be used to support psychological health in the form of Machine Learning. The study reflects on EEG retrieved in the form of brain signals. Based on the adaption of research design termed as ‘Sequential Mixed Method,’ the study extends its application from the personal to a professional arena for enhancing workplace productivity. Research design includes experiments with predictive analytics and drives discussions using Qualitative and Quantitative data. Based on the information retrieved from the subjects - captured through a BCI and a survey questionnaire, a Machine Learning (ML) model was developed. In this study, we hypothesize that such treatment protocol can accelerate treatments by therapists for the betterment of Psychosomatic health. Not only that, but the use of the ML model can also offer greater scalability in reaching out to the masses for greater access. The well-being achieved can positively reflect on the individual. Through a comprehensive view, it would support a person in improving their personal and professional life. Ultimately given study suggests that the well-being achieved could further impact organizations, enhancing their overall performance as validated in the presented thesis. The contribution of this study was the interlinking of interdisciplinary domains such as Management - Technology and Healthcare. Also, this study uniquely utilized Data science due to the large size of the dataset that is collected in this study

Digital Innovations in Healthcare Startups: Transforming Service Ecosystem

Healthcare is a critical part ofourlives. Technological developments in the modern era surmount geographical limitations topresent greater opportunities. Theproposed research focuses on current innovations in healthcare start-ups and evaluates the same using‘socio-material view'. The research elicitspatterns using a qualitative methodology thatcould push technological evolution a bit further. Through proposed research, a service ecosystem view has been presented by studying a set of innovative entrepreneurial ventures in the fieldof healthcare.Innovations result from the 'Texture of Practices'that are woven through pairs of intertwining practices of resource integrating and co-creating. The study follows up on the state-of-the-art technologies in the healthcare ecosystem to present its findings. Technology does simplify not only the existing healthcare practices but also has the potential to go beyond by co-creating the value. Hence the research looks forward to reflecting on groundbreaking technologies and technological innovation in the healthcare sector. The research has been carried keeping an Italian context in mind. To achieve thesamecontentanalysisand data mining frameworkis applied. The outcome presents a significant insight into what technologies can offer to the healthcare industry. It includes the use of software applications as a gateway to access medical products on-demand.Further, it puts a patient at the center of the service ecosystem using E-commerce. Digital Innovations in Healthcare Start-ups should include efficient 'track & trace' feature and additionally, offer customized processes based on the aggregated data. With the active interactiontaking place between consumer & service providers, it can transform the healthcare industry by offering the patientwith both preventive and targeted healthcare activities

Digital Innovations in Healthcare Startups: Transforming Service Ecosystem

Healthcare is a critical part of our lives. Technological developments in the modern era surmount geographical limitations to present greater opportunities. The proposed research focuses on current innovations in healthcare start-ups and evaluates the same using  ‘socio-material view'. The research elicits patterns using a qualitative methodology that could push technological evolution a bit further. Through proposed research, a service ecosystem view has been presented by studying a set of innovative entrepreneurial ventures in the field of healthcare. Innovations result from the 'Texture of Practices' that are woven through pairs of intertwining practices of resource integrating and co-creating. The study follows up on the state-of-the-art technologies in the healthcare ecosystem to present its findings. Technology does simplify not only the existing healthcare practices but also has the potential to go beyond by co-creating the value. Hence the research looks forward to reflecting on groundbreaking technologies and technological innovation in the healthcare sector. The research has been carried keeping an Italian context in mind. To achieve the same content analysis and data mining framework is applied. The outcome presents a significant insight into what technologies can offer to the healthcare industry. It includes the use of software applications as a gateway to access medical products on-demand. Further, it puts a patient at the center of the service ecosystem using E-commerce. Digital Innovations in Healthcare Start-ups should include efficient 'track & trace' feature and additionally, offer customized processes based on the aggregated data. With the active interaction taking place between consumer & service providers, it can transform the healthcare industry by offering the patient with both preventive and targeted healthcare activities