Real‐time daily assessment of work interference on healthcare professionals' restroom use: A pilot study

Hartigan, Siobhán M.; Finn, Michael T. M.; Dmochowski, Roger R.; Reynolds, W. Stuart

doi:10.1002/nau.24603

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…The continuous development of Internet technology has enabled the medical industry to enter the digital age, and Internet technology has also begun to be used in clinical nursing, which has simplified and upgraded the work of medical staff. With the continuous development of the Internet, the medical IoTs have begun to enter the clinical urinary real-time nursing system, which has brought more convenience to the hospital staff [ 5 ]. The hospital's information system can be connected to and transformed with the clinical information system, making the hospital's work process more convenient, improving the overall work efficiency of the hospital, and making the hospital's service satisfaction better [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of Urinary Real-Time Nursing Model Based on Medical Internet of Things

Hao

Liu

2022

Computational Intelligence and Neuroscience

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The medical Internet of Things (IoTs) can bind intelligent sensing devices with urinary nursing recipients and integrate information into various hospital information systems through network communication, so as to realize the intelligent perception, data collection, remote monitoring, information sharing, and other functions of urinary real-time nursing recipients. The urinary real-time nursing model can complete the expansion of hospital information system data to the bedside and the instant exchange of terminal data with the system through the medical IoTs and wireless local area network. Based on the summary and analysis of previous research results, this paper expounds on the research status and significance of the urinary real-time nursing model, elaborates on the development background, current status, and future challenges of medical IoTs, introduces the methods and principles of medical IoTs level evaluation system and transformation rules-based optimization algorithm, conducts urinary real-time nursing model design based on medical IoTs, analyzes model hardware design based on medical IoTs, performs model software design based on medical IoTs, proposes the urinary real-time nursing model optimization based on medical IoTs, explores the front-end function optimization of the urinary real-time nursing, implements the system program optimization of the urinary real-time nursing, discusses the hierarchical architecture of the urinary real-time nursing model, and finally carries out the role function analysis of the medical IoTs in the urinary real-time nursing model. The study results show that the urinary real-time nursing model based on medical IoTs can accurately and efficiently identify, manage, and integrate clinical nursing procedures and data such as patients, diagnoses, drugs, and can optimize nursing workflow, strengthen quality control, and improve nursing efficiency and provide patients with more convenient nursing services. The research results of this paper provide a reference for further research on the design and optimization of urinary real-time nursing model based on medical IoTs.

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Section: Introductionmentioning

confidence: 99%

Design and Optimization of Urinary Real-Time Nursing Model Based on Medical Internet of Things

Hao

Liu

2022

Computational Intelligence and Neuroscience

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“…Given that these participants are likely to have fewer risk factors for renal impairment apart from their working hours, it suggests a more pronounced hazardous effect of long working hours on renal health. A previous cohort study among adult HCW indicated that increased work burden correlated with a decreased frequency of voiding daily (30). Moreover, unhealthy behaviors such as inadequate water consumption and delayed voiding are known to contribute to kidney problems (31).…”

Section: Discussionmentioning

confidence: 99%

Impaired kidney function among young healthcare workers with long working hours and night work

Chen,

Yang

2024

Scand J Work Environ Health

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Objectives We aimed to evaluate the association between long working hours, night work, and estimated glomerular filtration rate (eGFR) among young healthcare workers. MethodsWe conducted a retrospective cohort study among healthcare workers in a tertiary medical center in Taiwan from 2002 to 2021. Other than physicians, all hospital employees aged 20-65 years with documented yearly working hours and an annual blood test including creatinine were eligible. We excluded participants with eGFR <60 ml/min/1.73 m 2 and proteinuria at enrollment to focus on early renal impairment. Total working hours, night working hours, and eGFR in each year were collected. We assessed the relationship of total working hours and night and non-night working hours with eGFR using the generalized linear mixed model, adjusting for demographic, comorbidities, and laboratory profiles. ResultsThe study included 10 677 participants with a mean age of 27.2 (standard deviation 7.1) years. The mean follow-up duration was 6.2 years. For every 10-hour increase in total weekly working hours, the eGFR decreased by 0.86 [95% confidence interval (CI) 0.61-1.11] ml/min/1.73 m 2 . For every 10-hour increase in weekly night working hours, the eGFR decreased by 0.25 (95% CI 0.07-0.42) ml/min/1.73 m 2 . In stratified analysis, the negative associations between total working hours and eGFR remained in the subgroups of individuals aged <40 years and those without hypertension or diabetes, with a P-value for interaction of <0.05. ConclusionsLonger working hours and night work were associated with lower eGFR among healthcare workers. Key terms early renal dysfunction; glomerular filtration rate.

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“…In Xu’s study [ 18 ], nurses often engage in unhealthy toileting behaviors such as delayed urination to adapt to occupational stress. Increased work burden was in turn found to be associated with the number of breaks and limitations to restroom use, which led to increased delayed urination behavior [ 19 ]. Strong evidence for the relationship between sleep, delayed urination, and transition shock is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Health characteristics and factors associated with transition shock in newly graduated registered nurses: a latent class analysis

Wang,

Zhou,

Shen

et al. 2024

BMC Nurs

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Background Transition shock occurs at a vulnerable time in newly graduated registered nurses’ careers and has a clear impact on both newly graduated registered nurses’ productivity and patient recovery outcomes. Identifying classification features of transition shock and targeting interventions to support newly graduated registered nurses is imperative. The study aimed to explore potential transition shock subgroups of newly graduated registered nurses and further explore the impact of population characteristics and two indices of health on transition shock. Methods A descriptive, cross-sectional design was conducted. An online questionnaire was sent via WeChat to newly graduated registered nurses who started work in 2021 at seven hospitals between August and November 2021, and 331 nurses filled out the questionnaire. Latent class analysis was used to identify the potential class of the transition shock of newly graduated registered nurses, and multinomial logistic regression analyses were used to determine the factors of potential classification. Results The study identified four classes of transition shock in newly graduated registered nurses, namely, “high transition shock”, “physical fatigue-lack of knowledge”, “development adaptation” and “low transition shock-worry” groups. Newly graduated registered nurses who urinated less than 4 times per day (OR = 0.051, 95% CI = 0.005–0.502) were likely to be in the “high transition shock” group. Newly graduated registered nurses who did not delay urination (OR = 4.267, 95% CI = 1.162–11.236) were more likely to belong to the “low transition shock-worry” group. Newly graduated registered nurses without sleep disturbance were more likely to be in the “physical fatigue - lack of knowledge” (OR = 3.109, 95% CI = 1.283–7.532), “development adaptation” (OR = 8.183, 95% CI = 2.447–27.066), and “low transition shock-worry” (OR = 8.749, 95% CI = 1.619–47.288) groups than in the ‘high transition shock’ group. Conclusions This study highlights potential patterns of transition shock among newly graduated registered nurses. Two indices of health, namely, delayed urination and sleep disturbance, can predict the subgroups of newly graduated registered nurses with transition shock.

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Real‐time daily assessment of work interference on healthcare professionals' restroom use: A pilot study

Cited by 4 publications

References 22 publications

Design and Optimization of Urinary Real-Time Nursing Model Based on Medical Internet of Things

Design and Optimization of Urinary Real-Time Nursing Model Based on Medical Internet of Things

Impaired kidney function among young healthcare workers with long working hours and night work

Health characteristics and factors associated with transition shock in newly graduated registered nurses: a latent class analysis

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