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Current Trends in Nursing I

Current Trends in Nursing I

Current trends in nursing include:

Current Trends in Nursing II

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...

Planning Nursing Care I

Planning Nursing Care I

The planning phase of the nursing process helps nurses set priorities, outline patient-centered goals and expected outcomes, and tailor nursing interventions to align with the aligned care plan. Through the planning phase, the nurse applies critical thinking skills to align and develop interventions according to the patient's needs. It provides continuity of care allowing patients to receive the maximum benefit from treatment. It serves as a pilot plan for allocating individual staff to a...

Nursing Assessment

Nursing Assessment

The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
The nurse collects all aspects of the patient's health in the initial assessment, establishing priorities for ongoing focused assessments...

Nursing Implementation

Nursing Implementation

Implementation is the execution of the nursing care plan developed during the planning phase.
The five steps to implementing effective nursing care include reassessing the patient, reviewing and revising the existing nursing care plan, organizing the resources and care delivery, anticipating and preventing complications, and implementing nursing interventions.

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:

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Staff Management with AI: Predicting the Nursing Workload.

Dirk Hunstein¹, Madlen Fiebig²

¹CEO, ePA-CC GmbH, Wiesbaden.

Studies in Health Technology and Informatics

|July 25, 2024

Summary

Artificial intelligence and machine learning can predict nursing workload using clinical data. The Self Care Index (SPI) is a key predictor, enabling data-driven staffing decisions for better healthcare.

Keywords:

Clinical Decision Support Machine Learning Nursing Workload Prediction Model Self-Care Index SPI Staff Management epaAC

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Area of Science:

Nursing
Healthcare Management
Artificial Intelligence

Background:

Effective human resource management in nursing is crucial for high-quality patient care.
Staffing levels are typically determined by patient health status, but objective prediction models are needed.

Purpose of the Study:

To develop and validate an AI/ML model for predicting nursing workload.
To identify key clinical predictors of nursing workload.
To provide a foundation for data-driven personnel management in nursing.

Main Methods:

A multi-center study utilizing routine clinical data from three hospitals.
Application of AI and ML algorithms to identify workload predictors.
Statistical analysis including adjusted R-squared to assess model performance.

Main Results:

The Self Care Index (SPI) was identified as a significant predictor of nursing workload, explaining 40-66% of variance in workload minutes.
Incorporating additional predictors like fatigue and pain intensity increased explanatory power by up to 17%.
A predictive model for nursing workload was successfully developed.

Conclusions:

AI and ML can effectively predict nursing workload based on routine clinical data.
The SPI is a strong, validated predictor of nursing workload.
The developed model supports data-based personnel controlling and optimized staffing in nursing.