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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:

Data Collection I

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...

Introduction to Documentation and Reporting

Introduction to Documentation and Reporting

Documentation is the systematic process of formally recording, maintaining, and communicating information.
Nursing documentation records essential information and details regarding a patient's care and treatment in written or electronic form. It is a critical aspect of nursing practice that involves documenting assessments, interventions, outcomes, and other relevant details about a patient's health status.
Documentation maps the patient's health journey by creating a comprehensive and precise...

Documentation of Nursing Diagnosis

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...

Data Collection II

Data Collection II

The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and family,...

Data Reporting and Recording

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

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Related Experiment Video

Updated: Jun 28, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Automated knowledge acquisition from clinical narrative reports.

Xiaoyan Wang¹, Amy Chused, Noémie Elhadad

¹Department of Biomedical Informatics,Columbia University, New York, NY, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|November 13, 2008

Summary

This study introduces an automated method using Natural Language Processing (NLP) to discover disease-symptom relationships in clinical reports. The approach achieved high accuracy, demonstrating its effectiveness for medical knowledge acquisition.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

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Last Updated: Jun 28, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Natural Language Processing
Clinical Data Mining

Background:

Accurate knowledge of disease-symptom associations is crucial for automated biomedical applications.
Extracting this knowledge from unstructured clinical narrative reports presents a significant challenge.

Purpose of the Study:

To develop and evaluate automated methods for acquiring medical knowledge, specifically disease-symptom associations, from clinical narrative reports.
To assess the effectiveness and generalizability of a Natural Language Processing (NLP) system for this task.

Main Methods:

Utilized the MedLEE Natural Language Processing (NLP) system to extract and encode clinical entities from narrative reports.
Applied statistical methods, adjusted by volume tests, to determine associations between extracted clinical entities, focusing on disease-symptom pairs.
Evaluated the performance using a random sample of disease-symptom associations.

Main Results:

The automated method demonstrated high performance in identifying disease-symptom associations.
Achieved an overall recall of 90% and a precision of 92% in the evaluation.
The NLP-based approach proved effective for knowledge acquisition from clinical reports.

Conclusions:

The developed automated method for acquiring disease-symptom pairs from clinical reports is effective.
The methodology is generalizable and can be extended to detect other clinical associations, such as disease-medication relationships.
This work highlights the potential of NLP in advancing automated biomedical knowledge discovery.