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The future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.

In addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.

This study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.

The present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

Acute and chronic pain affects more Americans than heart disease, diabetes, and cancer combined. Conservative estimates suggest the total economic cost of pain in the United States is $600 billion, and more than half of this cost is due to lost productivity, such as absenteeism, presenteeism, and turnover. In addition, an escalating opioid epidemic in the United States and abroad spurred by a lack of safe and effective pain management has magnified challenges to address pain in the workforce, particularly the military. Thus, it is imperative to investigate the organizational antecedents and consequences of pain and prescription opioid misuse (POM). This chapter provides a brief introduction to pain processing and the biopsychosocial model of pain, emphasizing the relationship between stress, emotional well-being, and pain in the military workforce. We review personal and organizational risk and protective factors for pain, such as post-traumatic stress disorder, optimism, perceived organizational support, and job strain. Further, we discuss the potential adverse impact of pain on organizational outcomes, the rise of POM in military personnel, and risk factors for POM in civilian and military populations. Lastly, we propose potential organizational interventions to mitigate pain and provide the future directions for work, stress, and pain research.

This study examines chronic illness, disability and social inequality within an exposure-vulnerabilities theoretical framework.

Using the National Survey of Drug Use and Health (NSDUH), a preeminent source of national behavioral health estimates of chronic medical illness, stress and disability, for selected sample years 2005–2014, we construct and analyze two foundational hypotheses underlying the exposure-vulnerabilities model: (1) greater exposure to stressors (i.e., chronic medical illness) among racial/ethnic minority populations yields higher levels of serious psychological distress, which in turn increases the likelihood of medical disability; (2) greater vulnerability among minority populations to stressors such as chronic medical illness exacerbates the impact of these conditions on mental health as well as the impact of mental health on medical disability.

Results of our analyses provided mixed support for the vulnerability (moderator) hypothesis, but not for the exposure (mediation) hypothesis. In the exposure models, while Blacks were more likely than Whites to have a long-term disability, the pathway to disability through chronic illness and serious psychological distress did not emerge. Rather, Whites were more likely than Blacks and Latinx to have a chronic illness and to have experienced severe psychological distress (both of which themselves were related to disability). In the vulnerability models, both Blacks and Latinx with chronic medical illness were more likely than Whites to experience serious psychological distress, although Whites with serious psychological distress were more likely than these groups to have a long-term disability.

Several possibilities for understanding the failure to uncover an exposure dynamic in the model turn on the potential intersectional effects of age and gender, as well as several other covariates that seem to confound the linkages in the model (e.g., issues of stigma, social support, education).

This study (1) extends the racial/ethnic disparities in exposure-vulnerability framework by including factors measuring chronic medical illness and disability which: (2) explicitly test exposure and vulnerability hypotheses in minority populations; (3) develop and test the causal linkages in the hypothesized processes, based on innovations in general structural equation models, and lastly; (4) use national population estimates of these conditions which are rarely, if ever, investigated in this kind of causal framework.

In this chapter, I explore connections between institutional and personal narratives of treating chronic pain with prescription opioids.

I explore how stories told in a Food and Drug Administration public hearing construct moral boundaries around different kinds of pain patients and justify a label change intended to reduce prescribing of opioids to people with chronic pain. I then examine how personal narratives, acquired through interviews with chronic pain patients who rely on opioids, both conform to and challenge the institutional narratives told in the hearing and work as subversive stories. Additionally, I consider how institutional and personal narratives of chronic pain shed light on intersections and conflicts between the medical and social models of disability.

The “invisible disability” experience of chronic pain highlights the complex entanglement between the struggles associated with impairment emphasized by the medical model, and those stemming from cultural and structural barriers emphasized by the social model.

I conclude with a discussion of the methodological value of examining narratives such as those of chronic pain and disability at multiple levels of social life. This study contributes to efforts to broaden disability discourse to include experiences such as chronic pain that are poorly represented in disability scholarship.

Purpose: This report analyzes relationships between chronic inflammation and quality of life (QoL). It assesses the potential role of intimate relationships in associations between inflammation, medical sequelae such as pain and disability, and QoL.

Design/Methodology/Approach: Limited longitudinal data from the National Social Life, Health, and Aging Project (NSHAP) were analyzed. Inflammation was assessed via the biomarker C-reactive protein (CRP). The authors examined pathwise associations between CRP levels and general happiness. The authors used ordinal logistic regression with companion OLS models, and conducted a variety of sensitivity analyses.

Findings: Intimate partnerships – especially marriage – appear to impact associations between inflammation and QoL. When QoL is measured using general happiness, intimate relationships appear to shape directional associations between inflammation and QoL along with medical consequences of inflammation.

Research Limitations/Implications: Inflammation and its clinical consequences may substantially alter intimate relationship involvement and quality that in turn impact QoL. These relationships should not be viewed as unilateral explanations of the tremendous variability observed in QoL among people with inflammatory conditions, but rather as possible elements of complex causal frameworks. Further investigation using advanced methods for longitudinal modeling from limited waves of data can help to shed additional light on the nuances of happiness and QoL among people living with chronic inflammation.

Originality/Value: This chapter provides an overview of possible causal relationships between chronic inflammation, associated clinical and social experiences, and QoL. Researchers interested in advanced causal modeling of relationships between chronic inflammation and QoL can build on this work using novel methods and data sources.

The purpose of this chapter is to use sociological theory and research to develop an explanation for how chronic illnesses are managed at home and to thereby suggest some ways in which a sociological perspective can be applied to improve health care for persons with chronic illnesses. Self-care illness management is crucial to the prevention of and reduction of morbidity and mortality from chronic illness.

Review and synthesis of research literature.

Sociological research and theory suggest two important insights that should inform health care services aimed at improving self-care; chronic illness care occurs in the context of the household, neighborhood, and community and, therefore, the “patient” (i.e., the object of health services) is really the caregiving social network around the patient, and because the risk of chronic illness and the resources available to deal with it are socially (and unequally) distributed, “health care” interventions need to take account of disparities in risks and resources that will affect the patient’s ability to successfully comply with self-care regimens.

The review does not include an examination of the clinical research literature. It does, however, suggest that sociologists need to explicitly study chronic illness and health care related to it.

The chapter links the long history of research on family caregiving to the concern with the success of self-management of chronic illness. It also links concerns about that success to social disparities in the distribution of social resources and hence to morbidity and mortality disparities.

This paper aims to explore the lived experience of people with a chronic non-healing wound and to explore what it means to live with a chronic wound.

A descriptive phenomenological study design was adopted to explore the living experience of person with chronic wound. A sample of 15 individuals of both genders was selected using a purposive sampling technique. To collect data, in-depth interviews were conducted, and all the interviews were audio-taped and transcribed verbatim. Data were analysed using the seven-step process described by Colaizzi (1978).

The findings were organized into 6 themes clusters and 12 themes. The six themes clusters were limiting mobility; receiving care; explaining causes of wounds; contending with chronic illnesses; adapting and mal-adapting; and economic burden of the wound.

Chronic wound had a profound impact on participants’ lives by affecting their activities of daily living, their mobility, their income and their personal relationships.

Understanding the lived experiences of people with chronic wounds is crucial for health-care providers, including nurses. Investigating the chronic wound experience has become even more pressing given the projected increase in the number of elderly individuals and those with chronic illnesses such as diabetes mellitus. In Jordan, for example, the prevalence of diabetes mellitus is 17.1%, and it is projected to increase by 2050.

The purpose of this paper is to examine the linkages of employment, chronic disease prevalence and medical care of the older population in India.

This study used the India Human Development Survey data for the analysis. Bivariate, multinomial logit regression and multiple classification analysis are used as methods for the study.

The findings suggest a bi-directional relationship between employment and chronic diseases: the older population who are engaged in regular paid work has lower likelihood to the risk of chronic diseases compared to those who are not working. Conversely, the older population with chronic diseases may be unable to work in regular paid jobs. The greater proportions of not-working older population with savings and retirement pensions are more likely to seek modern treatment for the chronic diseases. Overall, the results foster that employment determines and is determined by chronic disease prevalence among the older population in India.

This paper for the first time presents evidence on the linkages of employment, chronic disease prevalence and medical care of the older population in India by using a unique and comprehensive data source.

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

Objective. To evaluate a health and fitness programme conducted within a New South Wales, Australia correctional facility for male inmates with a chronic illness. Design. A randomised control trial. Sample. Twenty male inmate participants with a chronic illness, two risk factors for developing a chronic illness or who were over the age of 40 years. Measurements. Pre and post programme health assessments that included resting blood pressure and heart rate, weight, body mass index, waist girth, peak flow measures, peripheral saturation of oxygen, blood glucose levels and 6 minute walk test. Intervention. A 12‐week structured exercise programme focusing on cardio respiratory endurance, strength and flexibility training. Results. Statistically significant improvements in resting heart rate and endurance were found. Conclusions. The health and fitness programme positively impacts on the health of inmates with a chronic illness. A further study with a larger sample size would be productive.