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Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in the proposed analysis) and a set of branch DAGs (alternative hidden assumptions to the root DAG). All branch DAGs share a common ruleset, and must 1) change the root DAG, 2) be a valid DAG, and either 3a) change the minimally sufficient adjustment set or 3b) change the number of frontdoor paths. Branch DAGs comprise a list of assumptions which must be justified as negligible. We define two types of branch DAGs: exclusion branch DAGs add a single- or bidirectional pathway between two nodes in the root DAG (e.g., direct pathways and colliders), while misdirection branch DAGs represent alternative pathways that could be drawn between objects (e.g., creating a collider by reversing the direction of causation for a controlled confounder). The DAGWOOD framework 1) organizes causal model assumptions, 2) reinforces best DAG practices, 3) provides a framework for evaluation of causal models, and 4) can be used for generating causal models., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Background: US hospitals typically provide a set of code status options that includes Full Code and Do Not Resuscitate (DNR) but often includes additional options. Although US hospitals differ in the design of code status options, this variation and its impacts have not been empirically studied., Design and Methods: Multi-institutional qualitative study at 7 US hospitals selected for variability in geographical location, type of institution and design of code status options. We triangulated across three data sources (policy documents, code status ordering menus and in-depth physician interviews) to characterise the code status options available at each hospital. Using inductive qualitative methods, we investigated design differences in hospital code status options and the perceived impacts of these differences., Results: The code status options at each hospital varied widely with regard to the number of code status options, the names and definitions of code status options, and the formatting and capabilities of code status ordering menus. DNR orders were named and defined differently at each hospital studied. We identified five key design characteristics that impact the function of a code status order. Each hospital's code status options were unique with respect to these characteristics, indicating that code status plays differing roles in each hospital. Physician participants perceived that the design of code status options shapes communication and decision-making practices about resuscitation and life-sustaining treatments, especially at the end of life. We identified four potential mechanisms through which this may occur: framing conversations, prompting decisions, shaping inferences and creating categories., Conclusions: There are substantive differences in the design of hospital code status options that may contribute to known variability in end-of-life care and treatment intensity among US hospitals. Our framework can be used to design hospital code status options or evaluate their function., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Digital contact tracing, in combination with widespread testing, has been a focal point for many plans to "reopen" economies while containing the spread of Covid-19. Most digital contact tracing projects in the United States and Europe have prioritized privacy protections in the form of local storage of data on smartphones and the deidentification of information. However, in the prioritization of privacy in this narrow form, there is not sufficient attention given to weighing ethical trade-offs within the context of a public health pandemic or to the need to evaluate safety and effectiveness of software-based technology applied to public health., (© 2020 The Hastings Center.)

Background: Expertise has been a contentious concept in Evidence-Based Medicine (EBM). Especially in the early days of the movement, expertise was taken to be exactly what EBM was rebelling against-the authoritarian pronouncements about "best" interventions dutifully learned in medical schools, sometimes with dire consequences. Since then, some proponents of EBM have tried various ways of reincorporating the idea of expertise into EBM, with mixed results. However, questions remain. Is expertise evidence? If not, what is it good for, if anything?, Methods: In this article, I describe and analyze the three historical models of expertise integration in EBM and discuss the difficulties in putting each into practice. I also examine accounts of expertise from disciplines outside of medicine, including philosophy, sociology, psychology, and science and technology studies to see if these accounts can strengthen and clarify what EBM has to say about expertise., Results: Of the accounts of expertise discussed here, the Collins and Evans account can do most to clarify the concept of expertise in EBM., Conclusions: With some additional clarification from EBM proper, theoretical resources from other disciplines might augment the current EBM account of expertise.

Evidence-Based Medicine (EBM), Values-Based Practice (VBP) and Person-Centered Healthcare (PCH) are all concerned with the values in play in the clinical encounter. However, these recent movements are not in agreement about how to discover these relevant values. In some parts of EBM textbooks, the prescribed method for discovering values is through social science research on the average values in a particular population. VBP by contrast always investigates the individually held values of the different stakeholders in the particular clinical encounter, although the account has some other difficulties. I argue that although average values for populations might be very useful in informing questions of resource distribution and policy making, their use cannot replace the individual solicitation of patient (and other stakeholder) values in the clinical encounter. Because of the inconsistency of the EBM stance on values, the incompatibility of some versions of the EBM treatment of values with PCH, and EBM's attempt to transplant research methods from science into the realm of values, I must recommend the use of the VBP account of values discovery., (© 2015 John Wiley & Sons, Ltd.)

Background: Institutional Review Board (IRB) members have a duty to protect the integrity of the research process, but little is known about their basic knowledge of clinical research study designs., Methods: A nationwide sample of IRB members from major US research universities completed a web-based questionnaire consisting of 11 questions focusing on basic knowledge about clinical research study designs. It included questions about randomized controlled trials (RCTs) and other observational research study designs. Potential predictors (age, gender, educational attainment, type of IRB, current IRB membership, years of IRB service, clinical research experience, and self-identification as a scientist) of incorrect answers were evaluated using multivariate logistic regression models., Results: 148 individuals from 36 universities participated. The majority of participants, 68.9% (102/148), were holding a medical or doctoral degree. Overall, only 26.5% (39/148) of participants achieved a perfect score of 11. On the six-question subset addressing RCTs, 46.6% (69/148) had a perfect score. Most individual questions, and the summary model of overall quiz score (perfect vs. not perfect), revealed no significant predictors - indicating that knowledge deficits were not limited to specific subgroups of IRB members. For the RCT knowledge score there was one significant predictor: compared with MDs, IRB members without a doctoral degree were three times as likely to answer at least one RCT question incorrectly (Odds Ratio: 3.00, 95% CI 1.10-8.20). However, even among MD IRB members, 34.1% (14/41) did not achieve a perfect score on the six RCT questions., Conclusions: This first nationwide study of IRB member knowledge about clinical research study designs found significant knowledge deficits. Knowledge deficits were not limited to laypersons or community advocate members of IRBs, as previously suggested. Akin to widespread ethical training requirements for clinical researchers, IRB members should undergo systematic training on clinical research designs.

Values-based practice (VBP), developed as a partner theory to evidence-based medicine (EBM), takes into explicit consideration patients' and clinicians' values, preferences, concerns and expectations during the clinical encounter in order to make decisions about proper interventions. VBP takes seriously the importance of life narratives, as well as how such narratives fundamentally shape patients' and clinicians' values. It also helps to explain difficulties in the clinical encounter as conflicts of values. While we believe that VBP adds an important dimension to the clinician's reasoning and decision-making procedures, we argue that it ignores the degree to which values can shift and change, especially in the case of psychiatric disorders. VBP does this in three respects. First, it does not appropriately engage with the fact that a person's values can change dramatically in light of major life events. Second, it does not acknowledge certain changes in the way people value, or in their modes of valuing, that occur in cases of severe psychiatric disorder. And third, it does not acknowledge the fact that certain disorders can even alter the degree to which one is capable of valuing anything at all. We believe that ignoring such changes limits the degree to which VBP can be effectively applied to clinical treatment and care. We conclude by considering a number of possible remedies to this issue, including the use of proxies and written statements of value generated through interviews and discussions between patient and clinician., (© 2015 John Wiley & Sons, Ltd.)