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Nursing Clinical Information System

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:

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 Evaluation

Nursing Evaluation

The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
Section...

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 and...

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:

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,...

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Related Experiment Video

Updated: Jun 20, 2026

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Published on: August 9, 2024

A data-integrated simulation model to evaluate nurse-patient assignments.

Durai Sundaramoorthi¹, Victoria C P Chen, Jay M Rosenberger

¹Steven L. Craig School of Business, Missouri Western State University, Saint Joseph, USA. dsundaramoorthi@missouriwestern.edu

Health Care Management Science

|September 11, 2009

Summary

This summary is machine-generated.

This study introduces a novel simulation (SIMNA) for optimizing nurse-patient assignments using real hospital data. It employs tree-based models and kernel density estimation to improve healthcare operational efficiency.

Related Experiment Videos

Last Updated: Jun 20, 2026

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Published on: August 9, 2024

Area of Science:

Healthcare Operations Research
Data Mining in Healthcare
Simulation Modeling

Background:

Effective nurse-patient assignment is crucial for hospital efficiency and patient care quality.
Existing methods may not fully leverage real-time data for dynamic assignment optimization.
Northeast Texas hospital data provides a realistic basis for simulation development.

Purpose of the Study:

To develop and evaluate a novel data-integrated simulation (SIMNA) for nurse-patient assignments.
To utilize advanced data mining techniques for predicting nurse movement and time allocation.
To assess the impact of SIMNA on operational efficiency within a hospital setting.

Main Methods:

Development of a data-integrated simulation (SIMNA) using real hospital data.
Application of Classification and Regression Trees (CART) for predictive modeling.
Utilizing Kernel Density Estimation (KDE) for continuous time distribution analysis.
Building five tree structures to determine nurse movement probabilities and time spent.

Main Results:

SIMNA successfully integrated real-world data for simulation purposes.
Tree-based models accurately predicted nurse time allocation based on patient diagnosis and nurse type.
KDE provided continuous distribution estimates for nurse location time.
Simulation results offer insights into optimizing nurse-patient assignments in Medical/Surgical units.

Conclusions:

The developed SIMNA provides a robust framework for data-driven nurse-patient assignment optimization.
The integration of tree-based models and KDE enhances the accuracy of simulation predictions.
Findings suggest SIMNA can improve operational efficiency in hospital nursing units.