AI Can Help Us Live More Deliberately

Our rapidly increasing reliance on frictionless AI interactions may increase cognitive and emotional distance, thereby letting our adaptive resilience slacken and our ethical virtues atrophy from disuse. Many trends already well underway involve the offloading of cognitive, emotional, and ethical labor to AI software in myriad social, civil, personal, and professional contexts. Gradually, we may lose the inclination and capacity to engage in critically reflective thought, making us more cognitively and emotionally vulnerable and thus more anxious and prone to manipulation from false news, deceptive advertising, and political rhetoric. In this article, I consider the overarching features of this problem and provide a framework to help AI designers tackle it through system enhancements in smartphones and other products and services in the burgeoning internet of things (IoT) marketplace. The framework is informed by two ideas: psychologist Daniel Kahneman’s cognitive dual process theory and moral self-awareness theory, a four-level model of moral identity that I developed with Benjamin M. Cole

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Brain network mechanisms in learning behavior

The study of learning has been a central focus of psychology and neuroscience since their inception. Cognitive neuroscience’s traditional approach to understanding learn-ing has been to decompose it into discrete cognitive processes with separable and localized underlying neural systems. While this focus on modular cognitive functions for individual brain areas has led to considerable progress, there is increasing evidence that much of learn-ing behavior relies on overlapping cognitive and neural systems, which may be harder to disentangle than previously envisioned. This is not surprising, as the processes underlying learning must involve widespread integration of information from sensory, affective, and motor sources. The standard tools of cognitive neuroscience limit our ability to describe processes that rely on widespread coordination of brain activity. To understand learning, it will be necessary to characterize dynamic co-activation at the circuit level. In this dissertation, I present three studies that seek to describe the roles of distrib-uted brain networks in learning. I begin by giving an overview of our current understand-ing of multiple forms of learning, describing the neural and computational mechanisms thought to underlie incremental feedback-based learning and flexible episodic memory. I will focus in particular on the difficulties in separating these processes at the cognitive level and in localizing them to individual regions at the neural level. I will then describe recent findings that have begun to characterize the brain’s large-scale network structure, emphasiz-ing the potential roles that distributed networks could play in understanding learning and cognition more generally. I will end the introduction by reviewing current attempts to char-acterize the dynamics of large-scale brain networks, which will be essential for providing a mechanistic link to learning behavior. Chapter 2 is a study demonstrating that intrinsic connectivity between the hippo-campus and the ventromedial prefrontal cortex, as well as between these regions and dis-tributed brain networks, is related to individual differences in the transfer of learning on a sensory preconditioning task. The hippocampus and ventromedial prefrontal cortex have both been shown to be involved in this type of learning, and this study represents an early attempt to link connectivity between individual regions and broader networks to learning processes. Chapter 3 is a study that takes advantage of recent developments in mathematical modeling of temporal networks to demonstrate a relationship between large-scale network dynamics and reinforcement learning within individuals. This study shows that the flexibil-ity of network connectivity in the striatum is related to learning performance over time, as well as to individual differences in parameters estimated from computational models of re-inforcement learning. Notably, connectivity between the striatum and visual as well as or-bitofrontal regions increased over the course of the task, which is consistent with an inte-grative role for the region in learning value-based associations. Network flexibility in a dis-tinct set of regions is associated with episodic memory for object images presented during the learning task. Chapter 4 examines the role of dopamine, a neurotransmitter strongly linked to val-ue updating in reinforcement learning, in the dynamic network changes occurring during learning. Patients with Parkinson’s disease, who experience a loss of dopaminergic neu-rons in the substantia nigra, performed a reversal-learning task while undergoing functional magnetic resonance imaging. Patients were scanned on and off of a dopamine precursor medication (levodopa) in a within-subject design in order to examine the impact of dopa-mine on brain network dynamics during learning. The reversal provided an experimental manipulation of dynamic connectivity, and patients on medication showed greater modula-tion of striatal-cortical connectivity. Similar results were found in a number of regions re-ceiving midbrain projections including the prefrontal cortex and medial temporal lobe. This study indicates that dopamine inputs from the midbrain modulate large-scale network dy-namics during learning, providing a direct link between reinforcement learning theories of value updating and network neuroscience accounts of dynamic connectivity. Together, these results indicate that large-scale networks play a critical role in multi-ple forms of learning behavior. Each highlights the potential importance of understanding dynamic routing and integration of information across large-scale circuits for our concep-tion of learning and other cognitive processes. Understanding the when, where, and how of this information flow in the brain may provide an alternative or compliment to traditional theories of distinct learning systems. These studies also illustrate challenges in integrating this perspective with established theories in cognitive neuroscience. Chapter 5 will situate the studies in a broader discussion of how brain activity relates to cognition in general, while pointing out current roadblocks and potential ways forward for a cognitive network neuroscience of learning

Quality of Health Care for Children and Adolescents: A Chartbook

Contains 40 charts and analyses that represent the current state of pediatric health care. Provides practical guidance and recommendations for policymakers, health care professionals, and patient advocates

Aging and Technology Perspectives of Web-Based Chronic Disease Self-Management

Many people suffer from chronic disease; however, older adults are at greatest risk of chronic conditions. Although social workers regularly engage with chronically ill older adults, they are not noticeably involved with the research and development of chronic disease management. As such, with recent movements toward health information technology, the efficacy of technology-based chronic disease management is not well established for older adults. Informed by theories of self-management, human development, and technology design, this research investigated lifespan differences of web-based chronic disease self-management. Using a sequential mixed methods design, a secondary data analysis of a diabetes specific web-based self-management intervention (n=462) was performed, followed by qualitative focus groups with 40 older intervention participants, and then mixed for overall interpretation. Results indicated that social workers must take a leadership role in the evaluation and implementation of web-based self-management for older adults to address identified lifespan differences

A Multi-Method Evaluation Of A Guideline Based Clinical Decision Support Intervention On Provider Ordering Behavior, System Acceptance And Inter-Professional Communication

Background and aims: Unnecessary variation in the delivery of patient care is well documented in the medical literature; evidence-based clinical practice is critical for improving the quality of care. Clinical decision support systems (CDSS) are promising tools for improving the systematic integration of evidence into clinical practice. This study evaluated a CDSS in a domain of care that had not yet been explored—namely, decision support for venous catheter selection. This dissertation study aimed to (1) evaluate the effect of this CDSS on provider ordering behavior before and after implementation and explore the differential impact of this tool by provider type and service and (2) identify organizational, individual, usability, and workflow factors that impact CDSS acceptance by physicians and advanced practice nurses and to elicit information about the impact of this system on communication between providers and the nurse-led vascular access team. Methods: This was a multi-method study. Aim one was single group pre-post analysis of longitudinal data. Variables included those related to patient and provider level factors. The main analysis was conducted with linear regression models with random effects to account for clustering of data. We conducted semi-structured interviews for aim two and use conventional qualitative content analysis to identify themes. Results: We found mixed results in the impact of the CDSS on provider ordering behavior. While the CDSS did not have an impact on the number of venous catheters ordered, we saw a statistically and clinically significant decrease in the proportion of double lumen catheters ordered. Findings for the qualitative aim showed that the CDSS improved process efficiency and inter-professional communication. We found that it also facilitated education for evidence based practice for novice providers. Discussion: This dissertation study showed a clear impact of the CDSS on double lumen catheter ordering, which has implications for patient outcomes. Furthermore, we found impacts by provider type. Additional work is needed to evaluate this CDSS in other settings and to further assess differential impacts by provider type

Utilizing Goal Attaimnent Concepts To Empower Nursing Students For Effective Interprofessional Communication

Ineffective interprofessional communication is an identified issue affecting patient safety in health care. A multitude of baniers can impact interactions between and among disciplines leading to relationship strain and adverse patient outcomes. Nursing and other health care professions have limited curricular focus on interdisciplinary communication and collaboration. Interventions to improve interprofessional teamwork and collaboration are important foci for students in the health care profession. A learning event with the goal of sh1dent development of effective interdisciplinary conununication was de] iverecl to a group of nursing students in the classroom setting. King\u27s Theory of Goal Attaimnent (1981) and interacting systems framed the event. An evidence-based lecture underpi1med importance of effective communication and the potential baniers. A case study and the SBAR tool assisted sh1dents to organize and plan for interactions with physicians. A period of reflection following the case study allowed for expression of perceptions related to the interactions within personal, interpersonal, and social systems required for effective health care teams. Students engaged learning within the affective domain through discussion about emotional responses related to communication strategies and received formative feedback from faculty. Dialog s·upported acknowledgments of heightened awareness regarding the importance of interdisciplinary communication and tactics to improve effectiveness of interactions. Discussion among participants of the learning exercise substantiated the need for increased emphasis and incorporation of interprofessional communication training in health care cunicula and practice. Further research is necessary to determine additional interventions to enhance interprofessional communication for the ultimate goal of patient safety

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers