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Public reports of provider-specific patient outcomes aim to help consumers select suppliers of medical services. Yet, in an environment of rapidly changing medical technology and increasingly heterogeneous patient populations, and because they necessarily reflect the experience of other patients who received care in the past, such reports may be of limited value in helping patients forecast the probability of an adverse outcome for each provider they are considering. I propose that providers underwrite insurance policies that promptly pay patients a predetermined sum after an adverse outcome. Patients can use such outcome warranties to infer quality differences among providers easily and reliably. In addition, outcome warranties efficiently reward both providers and patients for reducing the risk of adverse outcomes and thereby improve the safety and affordability of health care. As such, outcome warranties help advance four important goals of health care management: reduction of financial risk, recruitment and retention of physicians, remediation of adverse outcomes, and raising the provider's reputation.

Over the last few decades, information technology (IT) has significantly altered the nature of work and organizational structures in many industries, including health care. The purpose of this analysis is to compare how system-level differences affect IT implementation in health care (HIT) and the implications of these differences for health care equity.

We critically analyzed selected claims concerning the capacity of HIT to provide better care to more individuals at lower costs, thus contributing to health care equity, in the context of current health care reform efforts in the United States. We used the case of HIT implementation in Taiwan’s National Health Insurance system as a contrasting case.

We argue that however much HIT may yield in quality improvements or savings in the context of a universal and publicly financed single payer system, such savings simply cannot be accrued by a system of multiple health plans competing for better customers (i.e., less costly patients) and driven by profit.

It is important to define the level of analysis in debates about the potential of HIT to produce better health care at lower costs and the equity implications of this potential. In these debates, US policy makers should consider the commitment to health care equity that informed the design of Taiwan’s health care system and of HIT implementation in that country. HIT merely provides enabling tools that are of little value without major systemic changes

To our knowledge, the health IT expert literature has overlooked when not ignored the ethical principles informing health care systems, an omission which makes it difficult if not impossible to evaluate the potential of HIT to increase equity in health care.

We examine whether self-stigmatization may affect the everyday social interactions of individuals with a diagnosed, affective mental health disorder. Past research demonstrates self-stigmatization lowers self-esteem, efficacy, and personal agency, leading to the likely adoption of role-identities that are at the periphery of major social institutions. We advance research on self-stigma by examining the likely interactional and emotional consequences of enacting either a highly stigmatized self-identity or a weakly stigmatized self-identity.

Using affect control theory (ACT), we form predictions related to the interactional and emotional consequences of self-stigmatization. We use the Indianapolis Mental Health Study and Interact, a computerized instantiation of ACT, to generate empirically based simulation results for patients with an affective disorder (e.g., major depression and bipolar disorder), comparing simulations where the focal actor is a person with a mental illness who exhibits either high or low levels of self-stigma.

Self-stigma is predicted to negatively influence patients’ behavioral expression, leading the highly self-stigmatized to enact behaviors that are lower in goodness, power, and liveliness than the weakly self-stigmatized. Their corresponding emotional expressions during these types of interactions are similarly negatively impacted. Even though these likely interactions are the most confirmatory for people with high levels of self-stigma, they lead to interactions that are behaviorally and emotionally more negative than those who have been better able to resist internalizing stigmatizing beliefs.

This piece has implications for the literature on the interactional and life course challenges faced by psychiatric patients and contributes to the self-stigma literature more broadly. This work will hopefully inform future research involving the collection of non-simulation-based data on the everyday interactional experiences of people with mental health problems.

Recent reports by the Institute of Medicine (IOM) signal a substantial yet unrealized deficit in patient safety innovation and improvement. With the aim of reducing this dilemma, we provide an introductory account of clinical error resulting from poorly designed systems by reviewing the relevant health care, management, psychology, and organizational accident sciences literature. First, we discuss the concept of health care error and describe two approaches to analyze error proliferation and causation. Next, by applying transdisciplinary evidence and knowledge to health care, we detail the attributes fundamental to constructing safer health care systems as embedded components within the complex adaptive environment. Then, the Health Care Error Proliferation Model explains the sequence of events typically leading to adverse outcomes, emphasizing the role that organizational and external cultures contribute to error identification, prevention, mitigation, and defense construction. Subsequently, we discuss the critical contribution health care leaders can make to address error as they strive to position their institution as a high reliability organization (HRO). Finally, we conclude that the future of patient safety depends on health care leaders adopting a system philosophy of error management, investigation, mitigation, and prevention. This change is accomplished when leaders apply the basic organizational accident and health care safety principles within their respective organizations.

Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.

Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.

Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.

Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

In this chapter organizational theory is used to clarify and synthesize the large and diverse literature on the relationship between managed care (MC) and ethnic differences in access to health services. MC practices are classified by whether they are used by health care organizations to define their boundaries or to coordinate care. MC practices used to coordinate care are further categorized as one of five types: rules and programs, authority, goal setting, culture, or client coordination. This review also presents hypotheses derived from this literature that specify the predicted effects of MC practices on ethnic differences in access to health services. It was found that few of these hypotheses had been empirically investigated and although some evidence was found that MC boundary-setting practices disadvantage minorities, there were not consistent findings with respect to those practices used to coordinate care.

The goal of this contribution is to shed light on the benefits for research in health care coming from the use of administrative data, especially in terms of measuring hospitals’ outcomes. The main approaches to health outcome evaluation are reviewed and the possible improvements deriving from the use of administrative data are highlighted. Administrative data may be an essential element in the process of gathering to the public true rankings of health care organizations, reducing the degree of asymmetric information that typically arises in health care. Patients will be more aware of the best institutions, which will induce most of them to demand to be admitted in them, taking into account the costs associated with distance and with the severity of the illness. This in turn may ask for a reorganization of the sector, closing some organizations and expanding others, having as final goal to improve the health status of the population, without income barriers. This is one of the first attempts to provide an overview of the advantages that administrative data may gather in health care.

For health services researchers and health services management educators, chronicling the unfolding of a country's implementation of national health insurance (NHI) is once in a lifetime opportunity. Rarely, do researchers have the opportunity to observe the macro and micro changes associated with turning a country's health care delivery system 180 degrees. Accordingly, we report on the first decade of Taiwan's changing delivery system and selected adaptations of health care management, providers and patients.

We argue that self-stigma places patients on a path of marginalization throughout their life course leading to a negative cycle of opportunity and advancement. Mental health patients with higher levels of self-stigma tend to have much lower self-esteem, efficacy, and personal agency; therefore, they will be more inclined to adopt role-identities at the periphery of major social institutions, like those of work, family, and academia. Similarly, the emotions felt when enacting such roles may be similarly dampened.

Utilizing principles from affect control theory (ACT) and the affect control theory of selves (ACTS), we generate predictions related to self-stigmatized patients’ role-identity adoption and emotions. We use the Indianapolis Mental Health Study and Interact, a computerized version of ACT and ACTS, to generate empirically based simulation results for patients with an affective disorder (e.g., major depression and bipolar disorder) with comparably high or low levels of self-stigmatization.

Self-stigma among affective patients reduces the tendency to adopt major life course identities. Self-stigma also affects patients’ emotional expression by compelling patients to seek out interactions that make them feel anxious or affectively neutral.

This piece has implications for the self-stigma and stigma literatures. It is also one of the first pieces to utilize ACTS, thereby offering a new framework for understanding the self-stigma process. We offer new hypotheses for future research to test with non-simulation-based data and suggest some policy implications.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.