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Partizipative Entscheidungsfindung und ärztliche Haftung - ein Spannungsfeld? Zahnmedizin und Gesellschaft 02/2017
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Increasing patient engagement in healthcare has become a health policy priority. However, there has been concern that promoting supported shared decision-making could increase health inequalities.
To evaluate the impact of SDM interventions on disadvantaged groups and health inequalities.
Systematic review and meta-analysis of randomised controlled trials and observational studies.
CINAHL, the Cochrane Register of Controlled Trials, the Cochrane Database of Systematic Reviews, EMBASE, HMIC, MEDLINE, the NHS Economic Evaluation Database, Open SIGLE, PsycINFO and Web of Knowledge were searched from inception until June 2012.
We included all studies, without language restriction, that met the following two criteria: (1) assess the effect of shared decision-making interventions on disadvantaged groups and/or health inequalities, (2) include at least 50% of people from disadvantaged groups, except if a separate analysis was conducted for this group.
We included 19 studies and pooled 10 in a meta-analysis. The meta-analyses showed a moderate positive effect of shared decision-making interventions on disadvantaged patients. The narrative synthesis suggested that, overall, SDM interventions increased knowledge, informed choice, participation in decision-making, decision self-efficacy, preference for collaborative decision making and reduced decisional conflict among disadvantaged patients. Further, 7 out of 19 studies compared the intervention's effect between high and low literacy groups. Overall, SDM interventions seemed to benefit disadvantaged groups (e.g. lower literacy) more than those with higher literacy, education and socioeconomic status. Interventions that were tailored to disadvantaged groups' needs appeared most effective.
Results indicate that shared decision-making interventions significantly improve outcomes for disadvantaged patients. According to the narrative synthesis, SDM interventions may be more beneficial to disadvantaged groups than higher literacy/socioeconomic status patients. However, given the small sample sizes and variety in the intervention types, study design and quality, those findings should be interpreted with caution.
Die Arzt-Patienten-Beziehung ist wesentlich für die zahnärztliche Behandlung. Kommt es zwischen Zahnarzt und Patient zu einer gerichtlichen Auseinandersetzung, kann die Arzt- Patienten-Beziehung als gescheitert gelten. Erstmals wird untersucht, wie beklagte oder klagende Zahnärzte ein gerichtliches Verfahren erleben und wie sich aus ihrer Perspektive ein solches Verfahren auf die Arzt-Patienten-Beziehung auswirkt. In einem Pilotprojekt wurde anhand von 8 narrativen und leitfadengestützten Interviews das Erleben von Zahnärzten rekonstruiert, die infolge einer gescheiterten zahnärztlichen Behandlung in einen Gerichtsprozess verwickelt waren. Der narrative Interviewteil wurde mit der Narrationsanalyse ausgewertet. Der leitfadengestützte Interviewabschnitt umfasste Fragen zum Erleben des Verfahrens und dessen Verarbeitung und wurde inhaltsanalytisch ausgewertet. Aus den Daten wurde ein Prozessmodell zum Scheitern der Arzt-Patienten-Beziehung abgeleitet. Mitgeteilte Belastungen waren: Zeitaufwand, Verfahrensdauer, finanzielle Belastungen, fehlende Unterstützung und das Gefühl, ungerecht behandelt worden zu sein. Konsequenzen aus dem Rechtsstreit sind ausführlichere Dokumentation und verbesserte Patientenaufklärung. Der Konflikt in der Arzt-Patienten-Beziehung wird nach Einschaltung der juristischen Profession auf eine juristische Ebene verlagert, deren kontradiktorische Konstellation zum Bruch der Arzt-Patienten-Beziehung führt. Als Rat für betroffene Kollegen wird genannt: Ruhe bewahren, Ausgleich anstreben, Kollegenrat einholen, selbst aktiv werden, juristische Hilfe heranziehen, außergerichtliche Lösung anstreben, Prozesse vermeiden und nach Abschluss des Verfahrens Bilanz ziehen. Gefordert werden eine bessere Qualifikation der Gutachter und eine „Anlaufstelle“ der Profession als kollegialer Gesprächspartner.
Shared decision-making is increasingly advocated as an ideal model of treatment decision-making in the medical encounter. To date, the concept has been rather poorly and loosely defined. This paper attempts to provide greater conceptual clarity about shared treatment decision-making, identify some key characteristics of this model, and discuss measurement issues. The particular decision-making context that we focus on is potentially life threatening illnesses, where there are important decisions to be made at key points in the disease process, and several treatment options exist with different possible outcomes and substantial uncertainty. We suggest as key characteristics of shared decision-making (1) that at least two participants--physician and patient be involved; (2) that both parties share information; (3) that both parties take steps to build a consensus about the preferred treatment; and (4) that an agreement is reached on the treatment to implement. Some challenges to measuring shared decision-making are discussed as well as potential benefits of a shared decision-making model for both physicians and patients.
The discourse of the patient as an active agent in managing illness and health care has become very important in medicine. It is seen in the significance attached to patient empowerment and participation, and in the burgeoning research into patients' coping with illness. The discourse cannot be fully understood from within conventional scientific frameworks because it is part of those frameworks. Instead, its current prominence can be understood by examining how it meets the needs of those who use it. Specifically, it has combined with earlier discourses of disease in a way that allows clinicians to withdraw from responsibility for areas of patient need that are problematic for medicine, such as unexplained symptoms, chronic disease and pain. This view is supported by evidence about how the discourse of patient as agent has been used in clinical consultation to constrain doctors' responsibility for patients' suffering. This discourse and other ways in which doctors and patients influence the boundaries of medical responsibility should be subjects for, rather than constraints on, empirical research.
Bad presentation of medical statistics such as the risks associated with a particular intervention can lead to patients making poor decisions on treatment. Particularly confusing are single event probabilities, conditional probabilities (such as sensitivity and specificity), and relative risks. How can doctors improve the presentation of statistical information so that patients can make well-informed decisions? The science fiction writer H. G. Wells predicted that in modern technological societies statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write. How far have we got, a hundred or so years later? A glance at the literature shows a shocking lack of statistical understanding of the outcomes of modern technologies, from standard screening tests for HIV infection to DNA evidence. For instance, doctors, with an average of 14 years of profes- sional experience were asked to imagine using the Haemoccult test to screen for colorectal cancer. 1 2 The prevalence of cancer was 0.3%, the sensitivity of the test was 50%, and the false positive rate was 3%. The doctors were asked: What is the probability that someone who tests positive actually has colorectal cancer? The correct answer is about 5%. However, the doctors' answers ranged from 1% to 99%, with about half of them estimating the probability as 50% (the sensitivity) or 47% (sensitivity minus false positive rate). If patients knew about this degree of variability and statistical innumeracy they would be justly alarmed. Statistical innumeracy is often attributed to problems inside our minds. We disagree: The problem is not simply internal but lies in the external representation of information, and hence a solution exists. Every piece of statistical information needs a representation—that is, a form. Some forms tend to cloud minds, while others foster insight. We know of no medical institution that leaches the power of statistical representations; even worse, writers of information brochures for the public seem to prefer confusing representations. 2 3 Here we deal with three numerical representations that foster confusion: single event proba- bilities, conditional probabilities, and relative risks. In each case we show alternative representa- tions that promote insight (Table 1). These "mind tools" are simple to learn. Finally, we address questions of the framing (expression) and manipulation of information and how to minimise these effects.
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Differences in Health Literacy of Adolescents with Lower Educational Attainment, Older People and Migrants
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