Browsing by Subject "Human–computer interaction"

Now showing 1 - 3 of 3
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    Does mind perception explain the uncanny valley effect? A meta-regression analysis and (de)humanization experiment.
    (Elsevier, 2024) MacDorman, Karl F.
    Gray and Wegner (2012) proposed that when robots look human, their appearance prompts attributions of experience, including sensations and feelings, which is uncanny. This theory, confusingly termed mind perception, differs from perceptual theories of the uncanny valley in that the robots' eeriness is not stimulus-driven. To explore this seminal theory, we conducted a meta-regression analysis of 10 experiments and a (de)humanization experiment. In the first part, experiments were identified in the literature that manipulated artificial entity's experience using descriptions. However, experiments with no observable stimuli yielded larger effects for experience and eeriness than those with robots and virtual reality characters. This finding undermines a theory that purports to explain how a robot's human likeness causes eeriness. Further, a second issue concerns Gray and Wegner's protocol based on a vignette design. Reading about an entity with experience activates thoughts that may not be activated when encountering it, and these thoughts may increase its eeriness. Therefore, the paper's second part focuses on an experiment we conducted with a novel humanization–dehumanization protocol. Participants' attitudes on robots' similarity to humans were gradually shifted to manipulate robots' perceived humanness, experience, and agency. However, the manipulation's effect on eeriness and coldness was mostly nonsignificant or counter to prediction. Differences in the robots' physical appearance had a much larger effect on their eeriness and coldness. In fact, as a mediator, experience mitigated the stimulus's overall effect of increasing eeriness. These results favor perceptual theories, rather than mind perception, in explaining the uncanny valley.
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    Providers' assessment of a novel interactive health information technology in a pediatric intensive care unit
    (Oxford University Press, 2018-07) Asan, Onur; Holden, Richard J.; Flynn, Kathryn E.; Murkowski, Kathy; Scanlon, Matthew C.; Medicine, School of Medicine
    Objective: To explore perceptions of critical care providers about a novel collaborative inpatient health information technology (HIT) in a pediatric intensive care unit (PICU) setting. Methods: This cross-sectional, concurrent mixed methods study was conducted in the PICU of a large midwestern children's hospital. The technology, the Large Customizable Interactive Monitor (LCIM), is a flat panel touch screen monitor that displays validated patient information from the electronic health record. It does not require a password to login and is available in each patient's room for viewing and interactive use by physicians, nurses, and families. Quantitative data were collected via self-administered, standardized surveys, and qualitative data via in-person, semistructured interviews between January and April 2015. Data were analyzed using descriptive statistics and inductive thematic analysis. Results: The qualitative analysis showed positive impacts of the LCIM on providers' workflow, team interactions, and interactions with families. Providers reported concerns regarding perceived patient information overload and associated anxiety and burden for families. Sixty percent of providers thought that LCIM was useful for their jobs at different levels, and almost 70% of providers reported that LCIM improved information sharing and communication with families. The average overall satisfaction score was 3.4 on a 0 to 6 scale, between "a moderate amount" and "pretty much." Discussion and Conclusion: This study provides new insight into collaborative HIT in the inpatient pediatric setting and demonstrates that using such technology has the potential to improve providers' experiences with families and just-in-time access to EHR information in a format more easily shared with families.
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    Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review
    (Thieme, 2021-05) Hill, Jordan R.; Visweswaran, Shyam; Ning, Xia; Schleyer, Titus K.; Medicine, School of Medicine
    Objective: Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient's record in clinical settings to characterize the current state of research on the topic and identify areas for future study. Methods: We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme. Results: Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient's record. Conclusion: Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering.