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In Chapter 4 on evidence-based medicine and ethics, we presented the example of breast cancer screening and reported that more than 1000 women must be screened with mammography to prevent only one death from breast cancer. In addition, screening generates several false-positive and some false-negative results. Furthermore, 13 women would be diagnosed with breast cancer and would probably be treated. However, these 13 women would not benefit from the treatment because they have a “pseudodisease” (see Chapter 11). Pseudodisease is a result of breast screening in that it cannot be diagnosed in individual patients but only concluded statistically by comparing populations of women who have or have not been screened.

Evidence-based health care (EBHC) and its approach to the practice of medicine has gained considerable acceptance among health care professionals. The Association of American Medical Colleges (AAMC) advocates integration of the principles of evidence-based medicine (EBM) into undergraduate training. Promoted as a tool to further learning by inquiry, to steer clear of opinion-based medicine (Sackett et al., 2000), and to help students at all levels of training to assess conscientiously the current best evidence, an increasing number of medical schools in the United States have incorporated it into their curriculum.

No goal-oriented action in health care would be possible without a reliable diagnosis. There are distinct demands on the test procedures leading to a diagnosis and the diagnosis itself.

Life expectancy in the United States continues to lengthen (Buttler, 2003; Lubitz et al., 2003). There are many explanations for the increasing life expectancy; evidence suggests that an increasing portion of the population lives to be older than 85 years (Tuljapurkar et al., 2000; Centers for Disease Control, 2003). Some estimates suggest that by 2050 about 1 of every 43 persons will be 90 years of age or older (Day, 1996).

Meta-analyses have become an instrument that is fundamental to the idea of best medical care. Meta-analyses combine the results of a large number of randomized controlled trials (RCTs) on a certain topic to gain more significant results. Should the reported effect size of RCTs change with time, the result of a meta-analysis would depend on when it was performed. Thus, the validity of a meta-analysis could be impaired.

With rapid changes and new challenges in global health care, there is increasing awareness of the limits with current measures of effectiveness. Promoting the use and reimbursement of a certain health technology requires evidence of its value for patients by means of improved quality or extended quantity of life. The term “outcomes research” has been introduced as a catch-all phrase for scientific approaches of determining the impact of health care interventions on the population level. However, because there is still debate as to the definition of outcome, the goals of outcomes research remain to be defined. We herein propose concepts and criteria that may suit the principle of outcomes research, and we sketch the major differences to clinical research.

The randomized controlled trial (RCT), presently accepted as the undisputed gold standard of study formats to prove the effectiveness of a treatment over its control, has experienced criticism during the last decade (Kramer & Shapiro, 1984; Jack et al., 1990; Kotwall et al., 1992; Plaisier et al., 1994; Andrews, 1999; Prescott et al., 1999; Ross et al., 1999; McCormack & Greenhalgh, 2000; Kaptchuk, 2001; McCulloch et al., 2002). Some arguments raised against RCTs are scientifically founded, whereas others emanate from reasons such as lack of understanding the theoretical background or emotional conflicts. Clinicians who believe in the need of controlled trials for scientific progress but are uncomfortable with prescribing their patients a treatment by chance have earned support from statisticians and epidemiologists.

It is assumed that shared decision making and respecting patients’ preferences increases the comfort of both providers and consumers of health services. It is also plausible to expect enhanced effect sizes and improved outcomes with treatment options that match patients’ demands.

This book is about gaining value for patients in health care. We have covered a lot of territory, ranging from ethical and philosophical issues to the contributions of psychology and clinical medicine. Portions of the book consider problems in clinical practice, such as overdiagnosis, patient preferences, and assessments of quality and safety in health care. We have also considered methods in economic analysis and clinical epidemiology. Considering the collection of chapters, what general lessons have we learned? In this final chapter we summarize some of these issues.