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In the context of Employment Assistance Programs (EAP), personalizing treatment means distinguishing those who could benefit from psychotherapy versus medication, and using the review of neurocognitive markers to facilitate therapeutic conversations between the client and clinician, thereby building the treatment alliance. This chapter outlines this context and illustrates an exemplar to achieve this using WebNeuro as a clinical decision support tool for EAP practitioners. Clinical Decision Support Systems (CDSS) are an existing prelude to personalized medicine in clinical practice and delivery of overall healthcare programs that best match each individual.

Attention Deficit Hyperactivity Disorder (ADHD) is the most common disorder of childhood-adolescent brain health. Here, we take an integrative neuroscience approach to personalized medicine. The goal of this approach is: 1) to identify promising markers that bridge cognitive and brain measures, as well as genomics; these currently have value for supporting diagnostic and personalized treatment decisions in clinical practice. These markers are sensitive and specific for distinguishing ADHD, focusing on the triad of inattention, hyperactivity and impulsivity. These markers are assessed with straightforward cognitive tests that relate to brain function and genes affecting dopamine. The same markers are also implicated in predicting response to stimulants. A different set of markers capturing alterations in emotion, feeling and their regulation shows promise in identifying comorbid internalizing conditions, such as anxiety, and externalizing conditions, such as conduct disorder.

This chapter examines the specific issues connected to clinical decision-making concerning whether or not to treat a venous thromboembolism (VTE) patient with advanced cancer in the palliative care setting. It discusses how strategies from the broader literature on decision-making in healthcare could be applied to the palliative care setting. It recommends the use of a tool called clinical decision analysis which can integrate both research evidence and measures of patient preferences in order to suggest what the optimum option may be for that patient.

In the emerging new paradigm of “Personalized Medicine,” the goal is to shift the treatment for brain-related illness from trial and error into a bull’s-eye. To date, much of the research in Personalized Medicine has focused on genetic “markers” employed as predictors of individual treatment response. The complexity of the brain requires a shift in focus from genetic marker(s) to an integrated approach, in which a wider scope of molecular plus brain-related functional, structural and cognitive information is used in a complementary manner. This chapter provides a brief summary of the current evidence for markers that predict brain-related treatment response in depression, schizophrenia and ADHD. The chapter focuses on the current status of Personalized Medicine, including the principles of an integrative approach to personalized medicine for the brain, and building a new taxonomy for incorporating the most clinically effective markers, is also outlined.

We are in one of the most exciting periods in the evolution of health care. Never before have so many variables (that directly impact the basis of patient care) simultaneously been in transition. This confluence of changes, including technology, policy, legislation and consumer attitudes, involves and impacts all major health care stakeholder groups. Personalized Medicine is at the core of change and is the utilization of technology to deliver better health care outcomes. It places the consumer at the center, recognizing that individual needs differ and it addresses the full spectrum of personal needs, from disability to well being, and prevention. Health care is the latest in a long line of industries to be transformed by the digital wave. The turning point has already occurred and as seen from the previous waves the extent of change will be transformative. Solutions that fill a consumer need will need to have solid, reproducible effects, be easy to use, and cost effective. This will also require new business case models to ensure ongoing resourcing of marker discovery and implementation of the personalization of the “continuum of care.”

This chapter begins by examining responsibility for outcomes, and then deals mainly with the process of decision making. It clarifies the moral issues that arise in the traditional methods of decision making and explores the moral difficulties inherent in the use of some more recently developed formal systems, such as flow charts and clinical guidelines, which have been proposed as improvements on the traditional process. In palliative care, clinical workers have to make decisions in circumstances that entail factual complexities, uncertainties, and difficult moral choices. Nevertheless, a decision has to be made. Letting someone die is permitted in certain circumstances whereas killing is prohibited. The doctrine of double effect, which relies on a moral distinction between intended and foreseen events, allows the use of measures to relieve suffering even those events carry a significant risk of shortening life.

This chapter discusses a consensus-based approach to decision making in palliative care for patients with severe dementia. It relates the author's experience in caring for a 73-year-old woman with Alzheimer's disease. The chapter explains the core principles with which to plan palliative care and outlines the steps in providing palliative care for patients who lack decision-making capacity. It suggests that a physician's investment of time in dialogue and consensus building with the patient may minimize the time and effort needed for future decisions.

Utility can be used to rank treatments based on their effect and to calculate cost-utility ratios. Change in utility is difficult to show for palliative care interventions because the effects of the interventions are small, the effects of the disease are large, and current global instruments do not capture the benefit. This chapter suggests that the most practical use is to quantify the benefits and toxicities of various interventions and to illustrate the future health states that the patient is likely to experience. This may be useful in patient decision making but requires an informed, knowledge-seeking patient, who is willing to make choices informed by data. At present, utility and cost-utility are not useful in resource allocation, except as an implicit way to illustrate benefit for comparative cost and clinical decision making.

In the chapter “Affect and Clinical Decision-Making,” theoretical and empirical literature from within and outside healthcare are drawn on to understand the role of affect in clinical decision-making at the individual and team level. Theories of individual decision-making are summarized and psychological models of decision-making and current knowledge of thought processes are presented to explain the role of affect in judgment and behavior in healthcare settings. Three types of affect: anticipatory affect, incidental affect, and anticipated affect are discussed in detail and used to illustrate how affective states may play a role in different clinical contexts and settings. Gaps in the existing evidence base are identified, and suggestions are made for interventions that might support health professionals to make better decisions.

This chapter examines patient choice and consent and its current status in clinical decision-making in end of life care. The findings indicate that the idea of patient choice can exist comfortably in the consensus view of health care and a stress on choice is simply a stress on the patient's right to choose between treatment options offered in the process of joint decision-making. This chapter also suggests the possibility of providing an end of life care service run on consumerist lines.

This chapter explores clinical decision making in the field of adult nursing practice. It draws upon the actual experiences of a third-year student nurse prior to qualifying as a nurse, to provide real-world examples of the types of decision that you, as a student nurse, will face whilst out on placement and once qualified. The case studies include learning exercises for you to complete, which aim to explore how you might have addressed the situation and made decisions. This will help you to develop skills for safer professional practice and to put the patient at the centre of your decision making. The role of the nurse has grown significantly over recent years as a result of changes in health policy, enabling the nurse to take on far greater levels of responsibility, such as independent nurse prescribing and advanced levels of nursing practice. This has resulted in nurses working in an increasingly complex clinical environment. On a daily basis, nurses are required to make decisions in relation to the care that they provide and how they manage their individual workloads (Banning 2005). As such, clinical judgement is considered to be an essential skill (Tanner 2006). Nurses now have far greater independence over their decisions in clinical practice owing to the influence of the changing policy context (Thompson 2001). However, this level of independence brings increased responsibility to the nurse, who will be judged and held accountable for his or her actions. Nurses are frequently required to make decisions in practice, often with limited information available to them (Ellis 1997). This requires critical thinking and problem-solving abilities, which are important elements of student nurse education (Garrett 2005). Clinical decision making is a highly complicated process, not yet fully understood, and there is considerable debate relating to its constructs and definitions evident in the literature (Shabban 2005). Furthermore, a variety of terms are used interchangeably within the literature referring to clinical decision making, which demonstrates a lack of consensus and may cause confusion: ‘clinical judgement’, ‘clinical decision making’, and ‘clinical reasoning’ are phrases used interchangeably to discuss and describe similar activity (Maharmeh 2011).

This chapter examines the issue of end of life care patients' ‘best interests’ in its various extended forms. It analyses the controversial view of specialist palliative care that the unit of care is the patient and relatives and that the best interests of the relatives must also be considered in making clinical decisions. Whole person or holistic care is acceptable as an aim of end of life care, but the term must be used with caution because a patient's wholeness extends well beyond anything that health care interventions at the end of life can encompass. The aims of end of life care should include the sensitive provision to patients of information about their illness in order to enable them to take part in decisions and lessen emotional distress.

This chapter examines quality of life (QoL) models and measures in relation to palliative care. It compares QoL gains between two treatments and evaluates the use of QoL as part of decision making for patient management. The chapter discusses several proposed definitions of QoL and different ways of obtaining information about the patient's QoL. The findings suggest that the management of a patient's QoL requires an individualised approach to treatment and that the clinician needs to find a way of managing the patient wherein multiple objectives are affected differently by different courses of action.

This chapter focuses on some of the mainstream and circumscribed examples of rethinking agency. Once one starts seeing and treating people as healthcare actors—as having something to contribute to their own care and to health systems and environments—many possibilities emerge. Some of these are already absorbed into mainstream thinking and others are more challenging or radical. The former includes the expectation that patients should play an active role in clinical decision making that affects them. The latter extend much more widely—questioning why ‘lay people’ are often allowed to be influential only in circumscribed instances, when their agency and perspectives could be equally influential in agenda setting and design decisions in all aspects of service planning, care provision, research, resource allocation and so on. This question highlights the potential to move beyond an individualist or consumerist conception of agency and towards more civic, social and democratic conceptions of social action.

This chapter defines the “medical guesswork” problem and explains how the poor integration of evidence into clinical decision making harms the performance of the health care sector. The lack of an effective and politically sustainable response to the medical evidence problem—and this is a sad indictment of the U.S. political system—would be somewhat less surprising if the medical evidence problem harmed “only” politically marginalized groups, such as the poor. But all Americans, including wealthy patients covered by generous insurance plans, suffer if doctors do not follow best practices or if evidence does not exist about, for example, the best way to treat prostate cancer or back pain. The pathologies of the U.S. medical system thus cannot be attributed to distributional bias in an otherwise high-performance system. These inefficiencies and performance breakdowns are widespread and systemic.

Clinical decision making might be based on moral principles focusing exclusively on the individual patient, such as the principles of beneficence, autonomy, and nonmaleficence. Alternatively, clinical decisions might include considerations in which the individual patient is seen in relation to others. Such considerations draw upon the moral principles of justice or solidarity and knowledge from health economics. The dilemmas of priority are confined to the latter alternative. This chapter, however, elaborates the negative side effects of a clinical practice that pays increased attention to prioritizing and less to the classical, individually focused virtues. Three fundamental premises constitute a starting point for the discussion: (a) that prioritizing always implies general moral considerations, (b) that prioritizing usually concerns the total usage of resources in society, not only the health budget; and (c) that prioritizing, given points (a) and (b), principally belongs to the political sphere.

The overall purpose of this chapter will be to broadly explore both the existing and possible implementations of artificial intelligence (AI) in healthcare. The scope of this chapter will be explored from the unique perspectives of various stakeholders in the healthcare industry, namely the healthcare providers, patients, pharmaceutical companies, healthcare financial institutions, and policymakers. The chapter will seek to identify the potential benefits and pitfalls that faced by these stakeholders in implementing the use of AI, from the molecular level to a macroeconomics level; as well as seeking to understand the legal, professional, and ethical boundaries of the medical domain that are challenged as AI increasingly becomes irreversibly intertwined with the practice of medicine.