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Related Concept Videos

Data Collection III01:05

Data Collection III

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Data Reporting and Recording01:24

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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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Guidelines For Measuring Vital Signs01:19

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Following these guidelines can help nurses accurately measure vital signs, assess changes in patient conditions, and provide timely treatment when necessary. Adhering closely to the guidelines ensures the accuracy and reliability of the results.
Before taking a patient's vital signs, a nurse would consider and assess the patient's comfort level and ensure appropriate equipment is available.
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Introduction to Vital Signs01:25

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Vital signs are physiological measurements that help key into the status of the body's essential functions. These include body temperature, pulse rate, respiratory rate, and blood pressure, commonly abbreviated as T, P, R, and BP. Some healthcare settings also consider oxygen saturation (SpO2) and, in specific contexts, pain and level of consciousness as additional vital signs.
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Nursing Assessment01:29

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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.
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Standardizing Physiologic Assessment Data to Enable Big Data Analytics.

Susan A Matney1,2, Theresa Tess Settergren3, Jane M Carrington4

  • 11 Intermountain Healthcare, Salt Lake City, UT, USA.

Western Journal of Nursing Research
|July 21, 2016
PubMed
Summary
This summary is machine-generated.

Standardizing nursing assessment data enables Big Data science. This study mapped key elements to Logical Observations Identifiers Names and Codes (LOINC) and SNOMED CT, creating a framework for multi-institutional research.

Keywords:
LOINCSNOMED CTdata exchange standardsmedical surgical nursingmulti-institutional researchnursing assessmentnursing informatics

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Area of Science:

  • Health Informatics
  • Nursing Informatics
  • Big Data Science

Background:

  • Nursing assessments are rich data sources but lack standardized terminology for Big Data analysis.
  • Interoperability challenges hinder data comparison across institutions.
  • A common data format is crucial for multi-institutional research and Big Data initiatives.

Purpose of the Study:

  • To align a minimum set of physiological nursing assessment data elements with national standardized coding systems.
  • To establish a common format for nursing assessment data to facilitate Big Data science.
  • To support the exchange of nursing information and multi-site research.

Main Methods:

  • Six institutions provided their top 100 electronic health record nursing assessment data elements.
  • Distinct data elements were mapped to Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT®).
  • A reference set of standardized nursing data elements was developed.

Main Results:

  • 137 observation names were identified, with 55% new to LOINC®.
  • 348 observation values were identified, with 20% new to SNOMED CT®.
  • The data elements were organized into 16 panels, with 72% new to LOINC®.

Conclusions:

  • The developed reference set supports nursing information exchange and multi-site research.
  • This standardization provides a framework for nursing data analysis.
  • Alignment with LOINC® and SNOMED CT® is essential for leveraging nursing data in Big Data science.