Process Analytics Model Approach in Healthcare

Process analytics involves the relationship between the doctor, diagnostic centers and patient. The primary advantages of using process analytics in healthcare are expert guidance, global medical assistance, and possible alternate treatment mechanisms. The secondary advantages are the analysis of the same type of disease complications and the creation of a disease-based healthcare data repository. This chapter focuses on the process model-based approach for healthcare analytics. The two emerging techniques Big data and IoT are needed to be incorporated with the process model for storing and analyzing the healthcare data. The first category assists administrators with identifying areas to streamline operations and concretely increase savings. Research and development are crucial aspects of healthcare, providing new innovative solutions and treatments that can be properly tracked, measured, and analyzed.

Digital Transformation Leadership for Smart Healthcare Organizations

The digital transformation has revolutionized the contexts of healthcare organizations in all aspects, and those hospitals that tend to create a smart digital environment are require to perceive vital factors of digital transformation. The author has created a framework called “House of Success” Model to elaborate on the significant organizational factors contributing to developing a smart healthcare organization in the digital era. This model consists of digital transformation, as the foundation of the house; quadruple aims, as the roof of the house and the ultimate goal of the transformation; and leadership and management, as the pillars of the house. Moreover, the house of success model has four rooms including change management and change leadership, people leadership, digital technology leadership, and the global partnership that resulted in building up a prosperous digital transformation outlook toward sustainable effectiveness development.

Workforce Readiness and Digital Health Integration

The benefits that digital health may offer include clinical, administrative, research, and educational. Research shows that if used in the right circumstances, digital health may increase access to healthcare services, improve clinical outcomes, safety, and quality of care. Digital health also has the potential to improve organisational efficiencies by reducing duplication and unnecessary diagnostic testing. From a healthcare consumer perspective, there is an expectation that healthcare services need to be provided in a more flexible and cost-effective way as in other spheres such as banking, commerce, and media. This is another important driver for consideration to integrate digital health in healthcare services. As digital health continues to be used in routine healthcare services, practitioners may require new knowledge, skills, and competencies to make the best use of this innovative method. Education and training relating to digital health have been recognised as a priority for developing the future healthcare workforce.

An Affordance Perspective on the Enabling and Disruptive Effects of Social Media Tools on Self-Management of Chronic Illness

This chapter examines the appropriation of social media tools by chronically ill adults to better understand and manage their illness using an affordance perspective. Despite the continued attention that information systems scholars have directed to studies on affordances and social media, there seems to be limited discussion on the negative, disruptive effects that social media could have on accomplishment of illness management goals. Accordingly, the authors argue that social media affordances could have both positive, enabling effects on illness management outcomes or negative, disruptive effects.

Investigating Diabetic Subjects on Their Correlation With TTH and CAD

Digital technology is modernizing healthcare. Large volumes of data refer to big data by digitising health information that can quickly be processed by machines. Digital healthcare analysis is the ability to diagnose and suggest ways to reduce costs; provide quality patient care and outcomes, available 24/7; reach to patients located in vast distant geographical areas; and avert preventable diseases. Artificial intelligence (AI) is an autonomous real-time machine system in comparison to natural information analyzed by humans. Diabetes is a serious, under-reported, life-threatening disease affecting millions of people of all ages, and researchers have identified it to be a major public health problem that is approaching epidemic proportions globally. The purpose of this study is to investigate diabetes analysis from CAD and other diseases using the latest advanced digital technologies to analyze information extracted from IoT and big data and stress correlation (TTH) on human health.

Digital Health Innovation Enhancing Patient Experience in Medical Travel

Digital health technological innovations are disrupting every sector of the economy, including medical travel/tourism. Global patients as medical tourists are using patient-centric digital health technologies, enhancing patient/medical tourists experience and making it more transparent and engaging with healthcare providers and medical tourists. Digital communication tools such as e-mail, online appointments, smartphones, instant messaging applications, social media tools, user-generated content by online patient communities, tele-medicine, tele-radiology, my-Health records, Skype consultation, WhatsApp, health video, electronic health records, health data analytics tools, and artificial intelligence-enabled health technologies enhance the medical travel decision-making process, reduce cost, improve patient care and transparency of communication, and engage the relationship between the patient and the healthcare provider with positive outcomes, medical tourist experience, and empowerment.

Digital Employability Skills and Training Needs for the Indian Healthcare Industry

The utilization of computerized innovation in wellbeing applications is encountering a huge blast in huge part because of growing human services costs and the impediments of one-on-one treatment to meet the psychological well-being needs of the populace. Specifically, cell phone applications for wellbeing have become popular, which gives chances to grow current consideration past the customary facility setting. The objectives of this study were to identify the employability skills and training needs in the digital healthcare industry. Results showed that all the eight skills are important for the training of line service staff (e.g., communication, ICT, work engagement, teamwork, cognitive). As per the response, handling situations is one of the most critical skills required to perform the job followed by self-management, planning and organizing, and analytical skills. However, the type and requirement of skills vary from person to person.

Supporting a Value-Based Healthcare Paradigm With Digital Health and Wellness Personalised Monitoring Solutions

Especially in the US, many are advocating for the incorporation of a value-based system for healthcare delivery including bundled payments for services in an attempt to address escalating healthcare costs. The following proffers the role for digital health solutions to support health and wellness management and the need to develop suitable sustainable business models. However, this approach brings a focus onto comorbidities and chronic conditions, which often need to be addressed or at least better managed before surgery can take place. This opens up the opportunity to examine the potential for digital health solutions such as mobile apps and serious games to provide an enabling or support role for individuals to better manage their chronic conditions. It also brings up the need for better, flexible models to assist health and wellness solution development.

Advances for Artificial Intelligence in Health Data Analytics to Drive Digital Systems Innovation

Artificial intelligence in health (AIH) and health data has become a focus of attention for customers of health services, organizations providing health services, and the government organization monitoring the performance and outcome for health services. These three groups have vested interests in how, where, and when the health data can be used and delivered to facilitate and streamline the delivery and process for health services from adopting AIH. The driving force in AIH for health data analytics stems from the discovery of new information, analysis that seeks to provide a clear understanding of a problem, interpretation in making clear sense of the problem, and communication of meaningful data patterns that can be effectively used in finding solutions to drive digital systems innovation. Modern technology provides an important platform for the health data transformation at different stages of the process to deliver different kinds of health services adopting artificial intelligence.

Digital Recognition of Breast Cancer Using TakhisisNet

In this chapter, the authors evaluate several basic image processing and advanced image pattern recognition techniques for automatically analyzing bioimages, with the aim of designing different ensembles of canonical and deep classifiers for breast lesion classification in ultrasound images. The analysis starts from convolutional neural networks (CNNs) in a square matrix that is used to feed other CNNs. The novel ensemble, named TakhisisNet, is the combination by sum rule of the whole set of the modified CNNs and the original one. Moreover, the performance of the system is further improved by combining it with some handcrafted features. Experimental results obtained on the well-known OASBUD breast cancer dataset (i.e., the open access series of breast ultrasonic data) and on a large set of bioimage classification problems show that TakhisisNet obtains very valuable results and outperforms other approaches previously tested in the same datasets.