This study explores the phenomenon of the informal medical consultation, a communication event in which an individual asks for medical information, advice, or care from an off-duty health professional with whom the individual has no formal patient-provider relationship. Using surveys and interviews, the study describes these consultations from the perspective of the health care professional and the informal patient. The study explores foundational theories that offer explanations for the phenomenon. The theories considered include social support, decision-making, social exchange, perceived partner responsiveness to needs, and uncertainty management. This study suggests health care providers perceive informal medical consultations to be more problematic than do the informal patients who consult them. The problematic nature of informal consultations increases as the type of request moves from purely informational to a request for treatment. Informal patients do not perceive this distinction. The informal patient’s motivation to pursue an informal consultation instead of a formal consult is affected by the relationship with, trust in, and access to the informal consultant. The willingness of the informal consultant to engage in an informal consultation is affected by the relationship with the informal patient, the type of request made, and perception of risk/benefit for both the provider and the patient. The study supports the idea that informal medical consultations are potentially problematic within the current medico-legal-ethical environment. Alternately, these consultations may be viewed as offering positive contributions to the health and well-being of informal patients. The study suggests translational research is needed to guide health professionals in considering requests for informal medical consultations.

Over the past few years, the rate of elective interventions in pregnancy in the United States, including elective inductions of labor and elective cesarean deliveries, has increased dramatically. While scholars attribute some of the increase in elective interventions to the female patients who request elective procedures from obstetricians, some literature contradicts that notion and suggests physicians are actually the primary perpetuators of the growth in elective procedures. Although pregnant women may seek elective interventions because of desired convenience, physicians can also claim the benefit of convenience in scheduling deliveries. In addition, elective procedures provide physicians greater monetary compensation than labor and deliveries which evolve without intervention. The current dissertation investigates the communicative role of obstetricians in women’s delivery decisions through in-depth interviews with obstetricians practicing in the state of Kentucky (N=28). Guided by the framework of Message Convergence, the study assesses how obstetricians manage uncertainty surrounding patient care and make clinical decisions in the midst of either unclear evidence or competing messages. The study also reveals the ways that physicians utilize their medical expertise to engage in decision-making with patients. In addition, specific scenarios of decision-making regarding delivery are discussed, including patients’ requests and physicians’ provisions of requests; patients’ requests and physicians’ refusals of requests; and physicians’ recommendations for treatment and patients’ refusals of recommendations for treatment. Finally, the internal tensions and conflicts experienced by physicians in the decision-making process with patients are also examined.

La prise de décision médicale se définit par le choix du traitement de la maladie, dans l’attente d’un résultat probable tentant de maximiser les bénéfices sur la santé du patient. Ce choix de traitement doit donc reposer sur les preuves scientifiques de son efficacité, ce qui renvoie à une problématique d’estimation de l’effet-traitement. Dans une première partie, nous présentons, proposons et discutons des méthodes d’inférence causale, permettant d’estimer cet effet-traitement par des approches expérimentales ou observationnelles. Toutefois, les preuves obtenues par ces méthodes fournissent une information sur l’effet-traitement uniquement à l’échelle de la population globale, et non à l’échelle de l’individu. Connaître le devenir probable du patient est essentiel pour adapter une décision clinique. Nous présentons donc, dans une deuxième partie, l’approche par modélisation prédictive, qui a permis une avancée en médecine personnalisée. Les modèles prédictifs fournissent au clinicien une information pronostique pour son patient, lui permettant ensuite le choix d’adapter le traitement. Cependant, cette approche a ses limites, puisque ce choix de traitement repose encore une fois sur des preuves établies en population globale. Dans une troisième partie, nous proposons donc une méthode originale d’estimation de l’effet-traitement individuel, en combinant inférence causale et modélisation prédictive. Dans le cas où un traitement est envisagé, notre approche permettra au clinicien de connaître et de comparer d’emblée le pronostic de son patient « avant traitement » et son pronostic « après traitement ». Huit articles étayent ces approches. / Medical decision-making is defined by the choice of treatment of illness, which attempts to maximize the healthcare benefit, given a probable outcome. The choice of a treatment must be therefore based on a scientific evidence. It refers to a problem of estimating the treatment effect. In a first part, we present, discuss and propose causal inference methods for estimating the treatment effect using experimental or observational designs. However, the evidences provided by these approaches are established at the population level, not at the individual level. Foreknowing the patient’s probability of outcome is essential for adapting a clinical decision. In a second part, we present the approach of predictive modeling, which provided a leap forward in personalized medicine. Predictive models give the patient’s prognosis at baseline and then let the clinician decide on treatment. This approach is therefore limited, as the choice of treatment is still based on evidences stated at the overall population level. In a third part, we propose an original method for estimating the individual treatment effect, by combining causal inference and predictive modeling. Whether a treatment is foreseen, our approach allows the clinician to foreknow and compare both the patient’s prognosis without treatment and the patient’s prognosis with treatment. Within this thesis, we present a series of eight articles.

No description available.

No description available.

No description available.

No description available.

No description available.

The disconnect between physician religious belief and experience and that of their patients is a growing problem in medicine. As physicians shy away from important discussions of their patients’ values, patients begin to drown in the growing medical complexity and feel less respected. Patient ability to drive their own care decisions is reduced when important topics like religious and spiritual beliefs are avoided. In light of the evidence of how religion and spirituality can influence health and medical decision making, physicians have a moral imperative to pursue an understanding of their patients’ belief system based on principle and pragmatism. There are easy tools available to help physicians streamline these patient interactions even when a physician is inexperienced in religious topics. For these reasons and more, it is of critical importance for physicians to no longer shy away from uncomfortable conversations but to pursue human flourishing through more deeply understanding their patients. While several objections may exist to this imperative, none are sufficient to outweigh the importance of this, and there are several tools in the toolbox that can make this process targeted, brief, and valuable. Ultimately, having a basic understanding of the religious views of patients enables physicians to more adequately assess barriers to health, understand key principles at play in medical decision making, and communicate effectively with patients and their families during stressful, life-altering times. Modern medical education allocates very few resources to training medical students to address patient beliefs, resulting in provider avoidance of religious conversations that are fundamental to patient health. / Urban Bioethics

Master of Science / Department of Psychology / Gary L. Brase / In 2006, the Center for Disease Control and Prevention recommended the revision of state HIV testing laws. With these recommendations, more low-risk individuals are tested regardless of their risk group. However, there is a greater chance of a false positive test result for low-risk individuals than for high-risk individuals. Additionally, previous research found that doctors and HIV counselors in Germany did not accurately communicate the relationship between risk factors and false positive tests (Gigerenzer, Hoffrage, & Ebert, 1998). This study aimed to (1) compare the findings of the 1998 German sample to HIV hotline counselors in the United States in 2011; and (2) to investigate the ability of students to calculate the conditional probability of HIV for a low-risk individual after receiving a positive test, based on idealized transcripts of conversations with HIV hotline counselors. The first study found that HIV hotline counselors use both verbal expressions of risk and percentages to communicate HIV testing statistics. Additionally, 2011 American counselors were more aware of the chance of false positives and false negatives than compared to the 1998 German sample. However, no 2011 American counselors were able to provide an accurate positive predictive value for a low-risk woman. The second study found low performance among students in the calculation of the positive predictive value. Performance was facilitated by a natural frequency format for high numerate individuals. There were different patterns of results for the General Numeracy Scale and the Subjective Numeracy Scale. This would suggest that these two scales might be measuring different constructs. These findings are consistent with the two theories supporting the Frequency Effect, namely the Frequentist Hypothesis and the Nested Sets Hypothesis. Additionally, this research suggests computation of the conditional risk of HIV is facilitated by a natural frequency format. Teaching techniques have been developed and demonstrate long lasting improvement in health related computations. If a few hours of training is all that it takes to communicate these life and death statistics in a manner that is consistent with reasoning, health practitioners and students should be required to have more education in communicating and computing probabilities.