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A total of 223 individual task evaluations were carried out, where the workload was measured (112 and 111, respectively, in cycles 1 and 2); 165 (74%) were the patient role, 40 (17.9%) clinical staff, and 18 (8.1%) administrative personnel. Multimedia Appendix 1 contains the comparative changes between test cycles 1 and 2 for the scenarios evaluated for all types of users. Multimedia Appendix 1 shows the comparative summary of workload (NASA-TLX) by cycles, functionalities, and types of users.
JMIR Form Res 2025;9:e58909
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In the context of nursing workload estimation, there are initial publications on experiences with the use of historical workload data for workload estimation tools, but while initial studies indicate potential to address the shortcomings of traditional methods, evidence remains inconclusive [10,12].
J Med Internet Res 2025;27:e66667
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Nursing workload management is complex and involves the processes of forecasting, scheduling, staffing, and monitoring [1]. Monitoring a ward’s nursing workload in real time or near time is difficult because of patient movements and because of changes in patient acuity and care needs. This is important because there is a body of evidence that shows nurse understaffing is associated with adverse outcomes for patients and for staff [2,3].
JMIR Med Inform 2025;13:e71666
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In doing so, the study aims to assess the device’s usability, its impact on nursing workload, and how effectively it can be embedded in standard clinical practice.
This prospective implementation study with retrospective data analysis (the REQUEST [Remote Monitoring by vi Qtor Upon Implementation on a Surgical Department] trial) will be conducted at a large teaching hospital.
JMIR Res Protoc 2025;14:e70707
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Kang et al [21] performed nursing time statistics based on the hospital information platform, and despite the slightly improved efficiency, the statistical workload was still a lot due to the extraction of data from multiple systems. Meanwhile, Zhang et al [22] calculated nursing time using the load weighting method. The assignment of load weights mainly depended on the subjective assessment of experts, with a large influence from human factors.
JMIR Med Inform 2025;13:e65549
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We refer to such behaviors as adverse events (AEs) in the context of nursing care when they affect the health or well-being of patients or increase the workload of nurses (see the Adverse Events in the Context of Nursing Care section). A particular challenge for nurses in hospitals is motor agitation in and around the bed, as well as related incidents such as falls or the disconnection of catheters or vascular access [6].
JMIR Form Res 2025;9:e64444
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The figure highlights several key challenges that hinder the efficient management of the digital information ecosystem, such as interoperability issues, information silos, increased workload and clinician burden, coordination and communication gaps, patient care journey mapping, and the complexities of complying with privacy regulations.
J Med Internet Res 2024;26:e60258
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Calculating Optimal Patient to Nursing Capacity: Comparative Analysis of Traditional and New Methods
Additionally, the calculation includes minutes of care required for admissions and discharges, ensuring that the dynamic bed count reflects the unit’s actual workload every hour. A more detailed description and example of the dynamic bed count calculation is available in Multimedia Appendix 1.
JMIR Nursing 2024;7:e59619
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Above- or below-average resources are recorded with the median of differences (pretest-posttest estimates of resources), leading to other hypotheses: (1) above-average resources are associated with lower workload, and below-average resources are associated with higher workload (hypothesis 2); (2) above-average resources are associated with higher work engagement (hypothesis 3a); (3) when resources increase, work engagement increases (hypothesis 3b); (4) if resources increase (exclusion of technical competence
JMIR Res Protoc 2024;13:e57820
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In addition, although many studies have focused on the workload of physicians and staff, no study has focused on the workload of patients with cancer. In this qualitative study, we explored the impact of work system factors on newly diagnosed patients with cancer’s perceptions of PCC and QOC and the impact of PCC on the QOC outcomes among newly diagnosed patients with cancer following a suggested conceptual framework.
JMIR Hum Factors 2024;11:e53053
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