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Guidelines for Nursing Documentation I

Guidelines for Nursing Documentation I

Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.

Formats for Nursing Documentation

Formats for Nursing Documentation

Nursing documentation encompasses various formats designed to capture precise patient data, facilitate communication among healthcare team members, and ensure comprehensive and accurate patient records. Let's explore each of these formats in detail:
Nursing Assessment Form:
• A nursing assessment form is a foundational document that captures detailed patient data from physical assessments and nursing histories.
• It includes patient demographics, medical history,...

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...

Guidelines for Nursing Documentation II

Guidelines for Nursing Documentation II

Effective documentation is an integral part of nursing practice. Here are some essential guidelines to follow when documenting patient care:
Timely documentation is crucial to ensure continuity of care for patients. Any delays in recording or reporting medical information can result in medical errors and even adverse patient outcomes. From medication administration to diagnostic test results, every detail must be accurately and promptly documented to provide the best possible care for patients.

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...

Methods of Documentation V: CBE

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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...

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Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation.

Eliezer Bose¹, Sasank Maganti, Kathryn H Bowles

¹Eliezer Bose, PhD, BEng, APRN, ACNP-BC, is Assistant Professor, University of Texas at Austin School of Nursing. Sagank Maganti, MS, B-Tech, is Research Assistant, University of Minnesota Carlson School of Computer Science and Engineering, Minneapolis. Kathryn H. Bowles, PhD, RN, FAAN, FACMI, is Professor, University of Pennsylvania School of Nursing, Philadelphia. Bonnie L. Brueshoff, DNP, RN, PHN, is Public Health Director at Dakota County, Minneapolis, Minnesota. Karen A. Monsen, PhD, RN, FAAN, is Associate Professor, University of Minnesota School of Nursing, Minneapolis.

Nursing Research

|August 29, 2018

Summary

Machine learning efficiently identifies key data for public health nurses (PHNs) by reducing documentation burden. This helps PHNs focus on at-risk clients and improve care outcomes.

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Area of Science:

Public Health
Nursing Informatics
Machine Learning in Healthcare

Background:

Public health nurses (PHNs) provide home visiting services and document care for at-risk clients.
Reducing documentation burden is crucial for PHN efficiency.
Identifying critical data elements linked to patient outcomes can enhance care.

Purpose of the Study:

To apply machine learning techniques for identifying critical data elements in public health nursing.
To reduce the documentation burden for PHNs by pinpointing essential data points.
To improve the efficiency and effectiveness of care for at-risk clients.

Main Methods:

Utilized minimum redundancy-maximum relevance (mRMR) and LASSO/elastic net (glmnet) feature selection techniques.
Applied methods to the Omaha System database (205 data elements) from 756 family home visiting clients.
Used a dichotomous maternal risk index as the outcome for feature selection.

Main Results:

mRMR selected 50 features, achieving 86.2% accuracy with a generalized linear model.
glmnet identified 63 features, resulting in a highest accuracy of 95.5% with a generalized linear model.
Both techniques demonstrated the ability to select important data elements.

Conclusions:

Feature selection techniques effectively reduce public health nursing documentation burden.
Identifying critical data elements aids in predicting client risk status.
Further refinement can inform targeted interventions and improve client care.