Thomas R. Clancy

The Internet of Things (IoT), defined as the networking capability that allows information to be sent to and received from objects and devices on the Internet (Merriam-Webster Dictionary 2021), is rapidly changing how and where care is provided. Advances in sensor technology, wired and wireless networks and commercial acceptance of standards are accelerating medical health systems adoption of the IoT. Data captured by sensors on medical devices and sent via networks to large databases in the Cloud are being analyzed to discover new knowledge as well as to create artificial intelligence for a variety of clinical activities. The IoT is enabling the transformation of health systems from a hospital-centric model to patient-centered care through its capacity to provide access anywhere along the continuum of care. Nurses play a key role in this transformation as they mediate information flow between patients and providers and their various levels of care. This chapter discusses the technological building blocks of the IoT and how it provides value through the ‘information value loop.’ The chapter also provides examples of how nurses are utilizing the IoT in practice and how the IoT is transforming healthcare delivery system models of care.

Over the next 10 years, the World Health Organization estimates that there will be global shortage of 18 000 000 health care workers. A perfect storm of an aging demographic, long-term drop in birth rate, and a retiring workforce has all the factors contributing to this impending crisis. In nursing, efforts to narrow the shortage gap through strategies that increase the number of admissions to nursing schools, such as increasing faculty members, clinical sites, preceptors, and scholarships, will likely not be enough to offset the shortfall. Solutions, in part, will be to make nurses more productive by reducing waste through the use of technology. This article evaluates how various types of technology such as electronic health records, data analytics, predictive modeling, artificial intelligence, speech recognition, natural language processing, robotics, the Internet of Things, and others can improve nurse productivity by using a Lean framework to eliminate waste and create value.

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on the application of management strategies in health systems. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. Since writing my 1st article for Managing Organizational Complexity in 2004, much has happened to further our understanding of complexity in healthcare systems. The growth of new computational methods in the fields of data science and data analytics has allowed scientists to identify signals or patterns in large complex data sets (big data) that in the past were seemingly hidden. Rather than relying on historical statistical methods to infer outcomes, these advanced methods combined with increased computer processing power allow machines to learn the structure of data and create artificial intelligence (AI). In our ongoing efforts to find solutions for complex healthcare problems, AI is becoming more and more an accepted method. The purpose of this edition of Managing Organizational Complexity is to define AI and machine learning, discuss the recent resurgence of AI, and then provide examples of how AI can provide value to healthcare with an emphasis on nursing.

Emerging Technologies, Medical Devices, Apps, Sensors, and Informatics to Help People with Diabetes

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on the application of management strategies in health systems. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This article is the 5th in a series of articles that focuses on why technological complexity is increasing and strategies nurse administrators can use to successfully implement change in the face of it.

BACKGROUND More than 1100 diabetes mobile apps are available, but app usage by patients is low. App usability may be influenced by patient factors such as age, sex, and psychological needs. OBJECTIVE Guided by Self-Determination Theory, the purposes of this study were to (1) assess the effect of patient characteristics on app usability, and (2) determine whether patient characteristics and psychological needs (competence, autonomy, and connectivity)—important for motivation in diabetes care—are associated with app usability. METHODS Using a crossover randomized design, 92 adults with type 1 or 2 diabetes tested two Android apps (mySugr and OnTrack) for seven tasks including data entry, blood glucose (BG) reporting, and data sharing. We used multivariable linear regression models to examine associations between patient characteristics, psychological needs, user satisfaction, and user performance (task time, success, and accuracy). RESULTS Participants had a mean age of 54 (range 19-74) years, and were predominantly white (62%, 57/92), female (59%, 54/92), with type 2 diabetes (70%, 64/92), and had education beyond high school (67%, 61/92). Participants rated an overall user satisfaction score of 62 (SD 18), which is considered marginally acceptable. The satisfaction mean score for each app was 55 (SD 18) for mySugr and 68 (SD 15) for OnTrack. The mean task completion time for all seven tasks was 7 minutes, with a mean task success of 82% and an accuracy rate of 68%. Higher user satisfaction was observed for patients with less education (P=.04) and those reporting more competence (P=.02), autonomy (P=.006), or connectivity with a health care provider (P=.03). User performance was associated with age, sex, education, diabetes duration, and autonomy. Older patients required more time (95% CI 1.1-3.2) and had less successful task completion (95% CI 3.5-14.3%). Men needed more time (P=.01) and more technical support than women (P=.04). High school education or less was associated with lower task success (P=.003). Diabetes duration of ≥10 years was associated with lower task accuracy (P=.02). Patients who desired greater autonomy and were interested in learning their patterns of BG and carbohydrates had greater task success (P=.049). CONCLUSIONS Diabetes app usability was associated with psychological needs that are important for motivation. To enhance patient motivation to use diabetes apps for self-management, clinicians should address competence, autonomy, and connectivity by teaching BG pattern recognition and lifestyle planning, customizing BG targets, and reviewing home-monitored data via email. App usability could be improved for older male users and those with less education and greater diabetes duration by tailoring app training and providing ongoing technical support.

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on the application of management strategies in health systems. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This article is the 3rd in a series of articles that focus on why technological complexity is increasing and strategies nurse administrators can use to successfully implement change in the face of it.

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies on complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 18th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, I discuss methods to optimize complex healthcare processes through learning, adaptation, and evolutionary planning.