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The Models of Child Health Appraised (MOCHA) project identified the different models of primary care that exist for children, examined the particular attributes that might be different from those directed at adults and considered how these models might be appraised. The project took the multiple and interrelated dimensions of primary care and simplified them into a conceptual framework for appraisal. A general description of the models in existence in all 30 countries of the EU and EEA countries, focusing on lead practitioner, financial and regulatory and service provision classifications, was created. We then used the WHO ‘building blocks’ for high-performing health systems as a starting point for identifying a good system for children. The building blocks encompass safe and good quality services from an educated and empowered workforce, providing good data systems, access to all necessary medical products, prevention and treatments, and a service that is adequately financed and well led. An extensive search of the literature failed to identify a suitable appraisal framework for MOCHA, because none of the frameworks focused on child primary care in its own right. This led the research team to devise an alternative conceptualisation, at the heart of which is the core theme of child centricity and ecology, and the need to focus on delivery to the child through the life course. The MOCHA model also focuses on the primary care team and the societal and environmental context of the primary care system.

While there is established research that explores individual innovations across countries or developments in a specific health area, there is less work that attempts to match national innovations to specific systems of health governance to uncover themes across nations.

We used a cross-comparison design that employed content analysis of health governance models and innovation patterns in eight OECD nations (Australia, Britain, Canada, France, Germany, the Netherlands, Switzerland, and the United States).

Country-level model of health governance may impact the focus of health innovation within the eight jurisdictions studied. Innovation across all governance models has targeted consumer engagement in health systems, the integration of health services across the continuum of care, access to care in the community, and financial models that drive competition.

Improving our understanding of the linkage between health governance and innovation in health systems may heighten awareness of potential enablers and barriers to innovation success.

A dynamic model of the evolution of health for those over the age of 50 is embedded in a structural, econometric model of retirement and saving. Effects of smoking, obesity, alcohol consumption, depression, and other proclivities on medical conditions are analyzed, including hypertension, diabetes, cancer, lung disease, heart problems, stroke, psychiatric problems, and arthritis. Compared to a population in good health, the current health of the population reduces retirement age by about one year. Including detailed health dynamics in a retirement model does not influence estimates of the marginal effects of economic incentives on retirement.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Empirical studies show that years of schooling are positively correlated with good health. The implication may go from education to health, from health to education, or from factors that influence both variables. We formalize a model that determines an individual’s demand for knowledge and health based on the causal effects, and study the impacts on the individual’s decisions of policy instruments such as subsidies on medical care, subsidizing schooling, income tax reduction, lump-sum transfers, and improving health at young age. Our results indicate that income redistribution policies may be the best instrument to improve welfare, while a medical care subsidy is the best instrument for longevity. Subsidies to medical care or education would require large imperfections in these markets to be more welfare improving than distributional policies.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

Since health behaviors of elderly Asian Americans are often underreported, the study, based on the health behavioral model (Andersen’s model), was to examine if there is a reciprocal relationship between healthcare utilization and health outcomes, and how social characteristics play their role in this relationship between US-born and foreign-born elderly Asian Americans.

Using structural equation modeling, this research examined the proposed hypotheses which consisted of direct and indirect effects among health outcomes, healthcare utilization and social characteristics, as well as the disparities of the effects between US-born and foreign-born elderly Asian Americans (65 + ). A sample size of elderly Asian Americans was divided into a US-born group (N = 1,305) and a foreign-born group (N = 4,902) from the National Health Interview Survey (NHIS) 1998–2012. Health outcomes consisted of current health status and health change. Healthcare utilization included general doctor visit, ER, and mental health professional visit. Social characteristics of population included predisposing characteristics (such as age, sex, marital status, and region of residency) and enabling resources (such as education, family size, and family income).

Results from the study indicated that first, there was a reciprocal relationship between health outcomes and healthcare utilization for both groups. Second, predisposing characteristics had a direct effect on health outcomes, and enabling resources had an indirect effect on health outcomes via healthcare utilization. In addition, living in the West had both direct and indirect effects on health outcomes. Third, regarding disparities of the effects between both groups, the US-born elderly are more likely to attain health benefits from healthcare utilization and their social characteristics than the foreign-born. As a result, the interactive relationship between health outcomes, healthcare utilization, and social characteristics, as well as disparities of healthcare outcomes through health utilization and social characteristics for elderly Asian Americans is highlighted.

First, due to the design of NHIS, this research was limited to fully present the needs and more characteristics of elderly Asian Americans. This shows the great need for a large scale, representative study for health behaviors of elderly Asian Americans. Second, in the dataset, the study was limited to explore health behaviors of elderly Asian Americans into each Asian ethnic subgroup. Since the culture of Asian Americans is heterogeneous, it is recommended that future research can explore differences and commonalities of the health behaviors between Asian subgroups. Third, based on the health behavioral model and the design of the dataset, this study was limited to illustrate variations of life experiences between both groups. These differences regarding their needs and desires for healthcare services and health outcomes can become an important foundation for service providers and policy makers to provide appropriate services that improve the quality of the later lives of elderly Asian Americans.

First, the study applied the health behavioral model and proved that the effect of healthcare utilization and social characteristics on health outcomes is greater for the US-born elderly Asian Americans than for the foreign-born. Second, this study confirmed that the healthcare services in US society are still beneficial for the health outcomes of elderly Asian Americans. Third, the study found that when family is used to explain the social behaviors of elderly Asian Americans, researchers need to be more careful to identify various family factors in accordance to its dynamics, such as interpersonal relationship, material supply, and emotional support.

We explore what health-capital theory has to offer in terms of informing and directing research into health inequality. We argue that economic theory can help in identifying mechanisms through which specific socioeconomic indicators and health interact. Our reading of the literature, and our own work, leads us to conclude that non-degenerate versions of the Grossman (1972a, 1972b) model and its extensions can explain many salient stylized facts on health inequalities. Yet, further development is required in at least two directions. First, a childhood phase needs to be incorporated, in recognition of the importance of childhood endowments and investments in the determination of later-life socioeconomic and health outcomes. Second, a unified theory of joint investment in skill (or human) capital and in health capital could provide a basis for a theory of the relationship between education and health.