Evidence map›Paper›PMID 42247414›Full record

ArticleJMIR human factors2026

Exploring Factors Influencing Nursing Task Prioritization for Supportive Information System Design: Qualitative Study With Thematic Analysis.

Kodai Iwamoto, Mayumi Toyama, Goshiro Yamamoto, Chang Liu, Kazumasa Kishimoto, Yukiko Mori, Tomohiro Kuroda

Abstract read
In one paragraph

Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Kodai IwamotoDepartment of Informatics, Graduate School of Informatics, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, Japan, 81 75 366 7701.ORCID 0009-0002-2250-5862
Mayumi Toyama *Department of Health Informatics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID 0000-0001-8614-4870
Goshiro Yamamoto *Preemptive Medicine and Lifestyle Related Disease Research Center, Kyoto University Hospital, Kyoto, Japan.ORCID 0000-0002-2014-7195
Chang LiuDepartment of Real World Data R&D, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID 0000-0002-0515-5226
Kazumasa KishimotoDepartment of Informatics, Graduate School of Informatics, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, Japan, 81 75 366 7701.ORCID 0000-0002-1674-6363
Yukiko MoriDepartment of Informatics, Graduate School of Informatics, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, Japan, 81 75 366 7701.ORCID 0000-0001-9486-9505
Tomohiro KurodaDepartment of Informatics, Graduate School of Informatics, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, Japan, 81 75 366 7701.ORCID 0000-0003-1472-7203

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nurses are required to perform multiple tasks concurrently, which leads to multitasking situations, and they have to continuously determine which tasks should be prioritized. This is particularly challenging for novice nurses. Although IT-based systems supporting prioritization have begun to emerge, research on the types of information required when prioritization is processed computationally is scant. Despite the clear need for a supportive information system to assist nursing task prioritization, such systems are not yet sufficiently developed. Objective: This study aimed to explore the appropriate granularity and structure of information that should be provided to computational systems to support decision-making based on the influencing factors of nursing task prioritization. Methods: Semistructured interviews were conducted with 10 nurses working in general wards to examine the factors they consider when determining task prioritization during clinical practice. Data were analyzed using an inductive, semantic approach based on a thematic analysis framework. Results: Three themes and nine categories including (1) medical condition assessment factors (signs of acute physiological changes and indicators of clinical status and conditions), (2) patient-related nursing care factors (physical status, psychological condition, and personal characteristics; care needs during hospitalization; and treatment goals and care preferences), and (3) organizational and operational work factors (temporally structured tasks, requiring collaboration partners for task execution, environmental factors affecting task performance, and institutional- and ward-level policies) were identified. Conclusions: Analysis of computational tractability of the identified factors indicated that medical condition assessment factors are relatively quantifiable. In contrast, patient-centered care and organizational and operational work factors rely on contextual and experiential judgment, limiting standardization and formalization. Regarding such ambiguous and context-dependent elements, flexible information-processing approaches, such as large language models, in addition to conventional rule-based methods, may be effective. Furthermore, the appropriate level of information granularity should be determined by the nature of the prioritization outputs required in actual nursing practice rather than the degree of abstraction itself.

Indexed as

Task Performance and AnalysisAdultFemaleHumansMaleQualitative Researchcomputational tractabilitynovice nursesnursing task prioritizationsemistructured interviewthematic analysis

Identifiers

PMID42247414
PMCPMC13240637

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.