Incorporating Nurse Input and Evidence Into a Newly Designed Unit to Improve Patient and Nursing Outcomes

Tafelmeyer, Jennifer; Wicks, Robin; Brant, Jeannine M.; Smith, Laurie

doi:10.1097/nna.0000000000000554

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…When this expertise is incorporated into the development of patient care approaches, outcomes are optimized. For example, one study examined the inclusion of nursing leadership and staff input into the architectural design process of a hospital unit (Tafelmeyer, Wicks, Brant, & Smith, ). Results included (a) decreased noise levels in both patient rooms and the nursing station, (b) decreased patient falls and methicillin‐resistant Staphylococcus aureus transmissions, (c) increased patient satisfaction, (d) improved workflow, and (e) enhanced nursing teamwork.…”

Section: Discussionmentioning

confidence: 99%

What makes a good health ‘app’? Identifying the strengths and limitations of existing mobile application evaluation tools

et al. 2019

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As over 81% of Americans own a smartphone (Pew Research Center, 2019), mobile health applications (mHealth apps) are becoming part of novel and innovative research strategies with potential to positively impact health care management and outcomes. In 2018, the average cost of developing and launching an mHealth app was $425,000 USD (Muoio, 2018). To conserve costs, health researchers may opt to use one of over 325,000 commercially available mHealth apps (Research2Guidance, 2017). However, the wide variety of mHealth apps presents challenges when choosing an appropriate one for research purposes. These apps may target health care providers (e.g., an app that details medication information and side effects) or consumers/patients (e.g., an app that helps the user track daily steps taken). There is also a distinction between mobile health and wellness apps, and mobile medical apps. Health and wellness apps are used to facilitate healthy lifestyle management and enhance well-being.Through a combination of attachable or Bluetooth-connected accessories (e.g., sensors), medical apps transform the built-in mobile device features (e.g., camera, speaker, light) into diagnostic tools, which can then be used to diagnose, track, treat, and facilitate communication about health and disease (U.S. Food and Drug Administration, 2018a). AbstractResearch using mHealth apps has the potential to positively impact health care management and outcomes. However, choosing an appropriate mHealth app may be challenging for the health researcher. The author team used existing evaluation tools, checklists, and guidelines to assess selected mHealth apps to identify strengths, challenges, and potential gaps within existing evaluation tools. They identified specific evaluation tool components, questions, and items most effective in examining app content, usability, and features, including literacy demand and cultural appropriateness; technical information; practical aspects of app functionality; and evolving capabilities of mobile medical apps. Challenges included the subjective nature of the results, time required to complete the evaluation, lack of emphasis on evidence-based content, and inadequate tool flexibility. Health researchers considering the integration of mobile apps into research will benefit from evaluation tools that assess both evidence-based content and the ability of the mobile app to securely integrate with other digital technologies involved in patient care. Next steps will include the involvement of health care providers and professionals, including nurses a wide range of expertise, to develop an mHealth evaluation tool that focuses on identifying quality, evidence-based mobile apps into patient outcomes research. K E Y W O R D Sevaluation, mHealth, mobile health, mobile phone

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Section: Discussionmentioning

confidence: 99%

What makes a good health ‘app’? Identifying the strengths and limitations of existing mobile application evaluation tools

et al. 2019

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“…After the theory of business process reengineering was put forward in 1990, it was widely used all over the world, not only in nursing process but also in the experience model of all walks of life [7,8]. Nursing defect refers to the violation of health laws, regulations, and nursing regulations and norms in nursing activities, resulting in errors in nursing technology, service, and management [9]. Nursing defect management has an important environment in nursing safety management.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization

Liu

2021

Computational and Mathematical Methods in Medicine

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It was to explore the application of nursing defect management evaluation and deep learning in nursing process reengineering optimization. This study first selects the root cause analysis method to analyse the nursing defect management, then realizes the classification of data features according to the convolution neural network (CNN) in deep learning (DL) and uses the constructed training set and verification set to obtain the required plates and feature extraction. Based on statistical analysis and data mining, this study makes statistical analysis of nursing data from a macroperspective, improves Apriori algorithm through simulation, and analyses nursing data mining from a microperspective. The constructed deep learning model is used, CNN network training is conducted on the selected SVHN dataset, the required data types are classified, the data are analysed by using the improved Apriori algorithm, and nurses’ knowledge of nursing process rules is investigated and analysed. The cognition of nursing staff on process optimization and their participation in training were analyzed, the defects in the nursing process were summarized, and the nursing process reengineering was analyzed. The results show that compared with Apriori algorithm, the running time difference of the improved Apriori algorithm is relatively small. With the increase of data recording times, the line trend of the improved algorithm gradually eases, the advantages gradually appear, and the efficiency of data processing is more obvious. The results showed that after the optimization of nursing process, the effect of long-term specialized nursing was significantly higher than that of long-term nursing. Health education was improved by 7.57%, clinical nursing was improved by 6.55%, ward management was improved by 9.85%, and service humanization was improved by 8.97%. In summary, the reoptimization of nursing process is conducive to reduce the defects in nursing. In the data analysis and rule generation based on deep learning network, the reoptimization of nursing process can provide reference for decision-making departments to improve long-term nursing, improve the quality and work efficiency of clinical nurses, and is worthy of clinical promotion.

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Application of Human Factors in Neonatal Intensive Care Unit Redesign

Grome¹,

Papautsky²,

Crandall³

et al. 2019

Advances in Health Care Management

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Incorporating Nurse Input and Evidence Into a Newly Designed Unit to Improve Patient and Nursing Outcomes

Abstract: This hospital found nursing engagement in the unit design process to be imperative to promote positive patient outcomes and stable or improved teamwork. Nursing leaders should be involved at the onset of the design process to facilitate optimal outcomes.

Cited by 3 publications

References 40 publications

What makes a good health ‘app’? Identifying the strengths and limitations of existing mobile application evaluation tools

What makes a good health ‘app’? Identifying the strengths and limitations of existing mobile application evaluation tools

Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization

Application of Human Factors in Neonatal Intensive Care Unit Redesign

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