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More than a million patient safety incidents occur every year, and medical error is the third leading cause of death in the United States. Illuminating the experiences of those affected by medical error—patients, their loved ones, and physicians and other medical professionals—Talking with Patients and Families about Medical Error delves deeply into the challenges of communicating honestly and openly about mistakes in medical practice. cc Based on guidelines from the Institute for Professional and Ethical Practice and the authors' own experiences, the practice-based approaches outlined here offer concrete guidance on • initiating discussions • dealing professionally and compassionately with patients' reactions • who should be included in the conversation • what information should be documented in the medical record • how to respond to questions about financial compensation Aimed at promoting resolution and healing, this book stresses the importance of clear, empathetic communication that will improve clinical and organizational responses to medical missteps and mismanagement. It emphasizes five features of the physician-patient relationship deserving of special attention: transparency, respect, accountability, continuity, and kindness (TRACK). Narrative examples of common situations demonstrate how conversations about medical error can lead to healing.

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.

In this article we investigate origins of several cases of failure of Artificial Intelligence (AI) systems employing machine learning and deep learning. We focus on omission and commission errors in (a) the inputs to the AI system, (b) the processing logic, and (c) the outputs from the AI system. Our framework yields a set of 28 factors that can be used for reconstructing the path of AI failures and for determining corrective action. Our research helps identify emerging themes of inquiry necessary for developing more robust AI-ML systems. We are hopeful that our work will help strengthen the use of machine-learning AI by enhancing the rates of true positive and true negative judgements from AI systems, and by lowering the probabilities of false positive and false negative judgements.

A diagnostic decision support system (DDSS) is expected to reduce diagnostic errors. However, its effect on physicians’ diagnostic decisions remains unclear. Our study aimed to assess the prevalence of diagnoses from artificial intelligence (AI) in physicians’ differential diagnoses when using AI-driven DDSS that generates a differential diagnosis from the information entered by the patient before the clinical encounter on physicians’ differential diagnoses. In this randomized controlled study, an exploratory analysis was performed. Twenty-two physicians were required to generate up to three differential diagnoses per case by reading 16 clinical vignettes. The participants were divided into two groups, an intervention group, and a control group, with and without a differential diagnosis list of AI, respectively. The prevalence of physician diagnosis identical with the differential diagnosis of AI (primary outcome) was significantly higher in the intervention group than in the control group (70.2% vs. 55.1%, p < 0.001). The primary outcome was significantly >10% higher in the intervention group than in the control group, except for attending physicians, and physicians who did not trust AI. This study suggests that at least 15% of physicians’ differential diagnoses were affected by the differential diagnosis list in the AI-driven DDSS.

This paper develops a parsimonious process-level theory that connects organizational structure to exploration and exploitation. Toward this end, it develops a mathematical model of organizational decision making that combines an information processing approach in the spirit of Sah and Stiglitz [Sah RK, Stiglitz JE (1986) The architecture of economic systems: Hierarchies and polyarchies. Amer. Econom. Rev. 76(4):716–727] with elements from signal detection theory. The model is first used to explore a “design space” of organizations and identify trade-offs and dominance relationships among alternative organization designs. The paper then studies open questions in the organization design literature, such as the extent to which exploration and exploitation can be produced by one organization and what is the effect of organization size on exploration. More broadly, this research speaks to calls for the introduction of more process-level explanations in the organizations literature. The paper concludes with testable hypotheses and managerially relevant insights.

1. Range maps represent the geographic distribution of species, and they are commonly used to determine species coverage within protected areas and to find additional places needing protection. However, range maps are characterized by commission errors, where species are thought to be present in locations where they are not. When available, habitat suitability models can reduce commission errors in range maps, but these models are not always available. Adopting a coarse spatial resolution is often seen as an alternative approach for reducing the effect of commission errors, but this comes with poorly explored conservation trade-offs. 2. Here, we characterize these trade-offs by identifying scenarios of protected area expansion for the world's threatened terrestrial mammals under different resolutions (10-200 km) and distribution data deriving from range maps and habitat suitability models. 3. We found that planning new protected areas using range maps results in an overestimation of the species protection level when compared with habitat suitability models (which are more closely related to species presence). This overestimation increases when more area is selected for protection and is higher when higher spatial resolutions are employed. 4. Adopting coarse resolutions reduced the overestimation of species protection and also halved the spatial incongruence between protected areas prioritized from range maps or habitat suitability models. However, this came at a very high cost, with an area of up to four times greater (12 M km² vs. 3 M km²) needed to adequately protect all species. 5. Synthesis and applications. Our findings demonstrate that adopting coarse resolutions in protected area planning results in unsustainable increases in costs, with limited benefits in terms of reducing the effect of commission errors in species range maps. We recommend that, if some level of uncertainty is acceptable to practitioners, using range maps at resolutions of 20-30 km is the best compromise for reducing the effect of commission errors while maintaining cost-efficiency in conservation analyses.

This article develops and tests a model of how organizational structure influences organizational performance. Organizational structure, conceptualized as the decision-making structure among a group of individuals, is shown to affect the number of initiatives pursued by organizations and the omission and commission errors (Type I and II errors, respectively) made by organizations. The empirical setting is more than 150,000 stock-picking decisions made by 609 mutual funds. Mutual funds offer an ideal and rare setting to test the theory, since there are detailed records on the projects they face, the decisions they make, and the outcomes of these decisions. The study's independent variable, organizational structure, is coded based on fund management descriptions made by Morningstar, and estimates of the omission and commission errors are computed by a novel technique that uses bootstrapping to create measures that are comparable across funds. The findings suggest that organizational structure has relevant and predictable effects on a wide range of organizations. In particular, the article shows empirically that increasing the consensus threshold required by a committee in charge of selecting projects leads to more omission errors, fewer commission errors, and fewer approved projects. Applications include designing organizations that achieve a given mix of exploration and exploitation, as well as predicting the consequences of centralization and decentralization. This work constitutes the first large-sample empirical test of the model by Sah and Stiglitz (1986).

Background: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy was shown. However, considering the negative effects of AI-driven differential-diagnosis lists such as omission (physicians reject a correct diagnosis suggested by AI) and commission (physicians accept an incorrect diagnosis suggested by AI) errors, the efficacy of AI-driven automated medical-history-taking systems without AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy should be evaluated. Objective: The present study was conducted to evaluate the efficacy of AI-driven automated medical-history-taking systems with or without AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy. Methods: This randomized controlled study was conducted in January 2021 and included 22 physicians working at a university hospital. Participants were required to read 16 clinical vignettes in which the AI-driven medical history of real patients generated up to three differential diagnoses per case. Participants were divided into two groups: with and without an AI-driven differential-diagnosis list. Results: There was no significant difference in diagnostic accuracy between the two groups (57.4% vs. 56.3%, respectively; p = 0.91). Vignettes that included a correct diagnosis in the AI-generated list showed the greatest positive effect on physicians’ diagnostic accuracy (adjusted odds ratio 7.68; 95% CI 4.68–12.58; p < 0.001). In the group with AI-driven differential-diagnosis lists, 15.9% of diagnoses were omission errors and 14.8% were commission errors. Conclusions: Physicians’ diagnostic accuracy using AI-driven automated medical history did not differ between the groups with and without AI-driven differential-diagnosis lists.

Failures of sustained attention are pervasive in everyday life. In the lab, these states of attention can be tracked unobtrusively using response times (RTs) in monotonous, continuous performance tasks requiring frequent responses. Here, we used a RT-based real-time triggering procedure to investigate the interplay between fluctuations of attentional state and another attentional phenomenon: experience-dependent capture. We investigated predictions from two related accounts. One proposes attentional lapses could reduce one’s ability to suppress capture by the distractor (capture-suppression account). The other proposes capture could “snap” attention back into focus (perceptual-recoupling account). Participants first completed a training task that differentiated colored targets by reward and then performed a sustained attention task that required they execute visual search on each trial. On rare trials, the colors relevant during training served as distractors inserted during lapsing and focused states. The results aligned with predictions from the perceptual-recoupling account: Experiment 1 showed that both novel and reward-associated distractors enhanced accuracy, whereas Experiment 2 showed a moderation of the reward-driven effect during fast-triggered moments. Pooled analyses confirmed a reliable reward-driven reduction in errors that diminished over successive blocks and was most pronounced early on for participants with stronger color–reward associations during fast-triggered trials. This study shows the potential of using experience-dependent capture to mitigate performance decrements associated with lapsing attention.

Successful prospective remembering involves formation of a stimulus (e.g., bottle of medication and/or place where the bottle is kept)–response (e.g., taking a medication) link. We investigated the role of this link in the deactivation of no-longer-relevant prospective memory intentions, as evidenced by commission error risk. Experiment 1a contrasted two hypotheses of intention deactivation ( degree of fulfillment and response frequency ) by holding constant the degree of intention fulfillment (e.g., participants responded to one of two target words) while manipulating the number of times the intention was performed. Findings supported the response frequency hypothesis. Experiment 1b employed novel lure trials to examine what “stimulus” participants link the prospective memory response to—target words and/or the salient contextual cue—and compared commission errors to Experiment 1a . Findings suggested the salient context alone does not always function as the stimulus. Collectively these findings, in conjunction with those of Experiment 2 (a within-experiment replication) and a combined analysis, suggest that (a) intention deactivation is facilitated by prior responding (formation/strengthening of stimulus–response links), but additional research is needed to establish the robustness of this effect, and (b) when responding frequently to targets, participants are more likely to bind the response to the context alone than to the target or target/context combination, possibly because they learn to rely on context to predict target occurrence. The latter finding was robust and indicates that deactivation of the appropriate stimulus (target and/or context)–response link may be a critical component of reducing commission errors.