Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making

Binder, Karin; Krauss, Stefan; Bruckmaier, Georg; Marienhagen, Jörg

doi:10.1371/journal.pone.0195029

Cited by 26 publications

(39 citation statements)

References 60 publications

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“…The results of the study could reveal that the developed visualization software allows verifying the patient's case in an appropriate timeframe and reducing the probability of inexact (non-helpful) data due to an improved transparency and verifiability [12,48]. Overall, this approach presented the technical feasibility and also the possible clinical integration of digital patient models for supporting therapy decision making in (preoperative) interdisciplinary tumor boards based on Bayesian Networks, which is also confirmed in the literature [62][63][64].…”

Section: Digital Patient and Process Modelssupporting

confidence: 53%

Der intelligente HNO-Operationssaal der Zukunft

et al. 2019

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ZuSAMMeNfASSuNg Die zunehmende Pluralität und Komplexität technischer Assistenzsysteme stellen eine Herausforderung für den klinisch täti-gen Mediziner dar. Insbesondere im OP Saal steigt der Bedarf medizintechnische Systeme und Softwarelösungen in eine gesamtheitliche Infrastruktur zu integrieren. Primäres Ziel des Aufbaus eines "digitalen (HNO-) OP Saals der Zukunft" ist nicht nur die reine technische Verbesserung der einzelnen computergestützten Geräte und Instrumente, sondern vielmehr deren dynamische Vernetzung und Systemintegration in einem offenen modularen System. Vielversprechende wissenschaftliche Projekte gehen dabei den Fragen nach, wie die Qualität, die Sicherheit und die Anwenderfreundlichkeit technischer Systeme im Gesundheitswesen des 21. Jahrhunderts verbessert werden können. Am Beispiel der Arbeiten zu SCOT, MD PnP und OR.NET werden die verschiedenen Komponenten sichtbar, die im Gesamtkontext die Vision des HNO-Operationssaals der Zukunft erfahrbar und realistisch erscheinen lassen. AbSTrAcTThe increasing plurality and complexity of technical assistance systems pose a challenge for clinically active physicians. Particularly in the operating theater, there is a growing need to integrate medical systems and software solutions into a holistic clinical infrastructure. The primary goal of building a "digital (ENT) operating room of the future" is not just the pure technical improvement of the individual computer-aided equipment and instruments, but rather their dynamic networking and system integration in an open modular system. Promising scientific projects address the question of how to improve the quality, safety and user-friendliness of technical systems in the health care system of the 21st century. The work on SCOT, MD PnP and OR.NET show the various components that make the vision of the ENT operating room of the future tangible and realistic in the overall context. Abstract 5Abkürzungsverzeichnis 2 1.

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Section: Digital Patient and Process Modelssupporting

confidence: 53%

Der intelligente HNO-Operationssaal der Zukunft

et al. 2019

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“…In the meta-analysis by McDowell and Jacobs (2017), frequency versions of Bayesian reasoning problems can be solved on average by 24% of participants across studies and contexts. Even in more complex Bayesian problems, such as in situations involving more than one medical test or unclear test results, frequencies help people in their decision-making processes (Hoffrage et al, 2015b; Binder et al, 2018). In the last 20 years, an abundance of studies has shown the facilitating effect of frequencies for many different kinds of populations: physicians, patients, judges in court, managers, university and high school students, and even young children (Gigerenzer and Hoffrage, 1995; Hoffrage et al, 2000; Zhu and Gigerenzer, 2006; Siegrist and Keller, 2011; Hoffrage et al, 2015a; McDowell and Jacobs, 2017).…”

Section: Statistical Thinkingmentioning

confidence: 99%

An Eye-Tracking Study of Statistical Reasoning With Tree Diagrams and 2 × 2 Tables

et al. 2019

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Changing the information format from probabilities into frequencies as well as employing appropriate visualizations such as tree diagrams or 2 × 2 tables are important tools that can facilitate people’s statistical reasoning. Previous studies have shown that despite their widespread use in statistical textbooks, both of those visualization types are only of restricted help when they are provided with probabilities, but that they can foster insight when presented with frequencies instead. In the present study, we attempt to replicate this effect and also examine, by the method of eye tracking, why probabilistic 2 × 2 tables and tree diagrams do not facilitate reasoning with regard to Bayesian inferences (i.e., determining what errors occur and whether they can be explained by scan paths), and why the same visualizations are of great help to an individual when they are combined with frequencies. All ten inferences of N = 24 participants were based solely on tree diagrams or 2 × 2 tables that presented either the famous “mammography context” or an “economics context” (without additional textual wording). We first asked participants for marginal, conjoint, and (non-inverted) conditional probabilities (or frequencies), followed by related Bayesian tasks. While solution rates were higher for natural frequency questions as compared to probability versions, eye-tracking analyses indeed yielded noticeable differences regarding eye movements between correct and incorrect solutions. For instance, heat maps (aggregated scan paths) of distinct results differed remarkably, thereby making correct and faulty strategies visible in the line of theoretical classifications. Moreover, the inherent structure of 2 × 2 tables seems to help participants avoid certain Bayesian mistakes (e.g., “Fisherian” error) while tree diagrams seem to help steer them away from others (e.g., “joint occurrence”). We will discuss resulting educational consequences at the end of the paper.

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“…The tasks are difficult for medical students and easy for math students. The task itself has a certain degree of complexity (1-test-case=simple/non-complex and 2-test-cases=complex), the probability of solution and thus the difficulty depends on the recipient [8]. The pre-knowledge of the person trying to solve the task influences the level of difficulty much more than the complexity does.…”

Section: Introductionmentioning

confidence: 99%