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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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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...
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Formulating and Validating Nursing Diagnosis II01:25

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Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
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Formulating and Validating Nursing Diagnosis I01:26

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A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
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Pre-Procedural Guidelines for Assessing Blood Pressure01:10

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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Guidelines and Strategies for Safe Computer Charting01:18

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The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
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Myasthenia gravis is an autoimmune condition affecting neuromuscular transmission, causing generalized weakness in skeletal muscles. Initial diagnoses rely on patients' signs, symptoms, and medical history. The challenge lies in distinguishing myasthenia from other muscular dystrophies. An important diagnostic feature is the significant improvement of symptoms after administering anticholinesterase inhibitors.
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Automatic Generation of Conditional Diagnostic Guidelines.

Tyler Baldwin1, Yufan Guo1, Tanveer Syeda-Mahmood1

  • 1IBM Almaden Research, San Jose, CA, USA.

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|March 9, 2017
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Summary
This summary is machine-generated.

This study developed a three-task pipeline to extract conditions for medical procedures from diagnostic texts. The system accurately identifies recommendations and their conditions, creating concise summaries for clinical decision support.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Decision Support

Background:

  • Diagnostic workups involve nuanced decisions on procedure appropriateness.
  • Reference materials contain extensive, complex information on medical procedures.

Purpose of the Study:

  • To develop an automated pipeline for extracting conditional statements regarding medical procedure recommendations.
  • To create concise summaries of procedural recommendations based on suspected diagnoses.

Main Methods:

  • A three-task pipeline was employed: procedure recommendation identification, conditional statement extraction, and coreference resolution.
  • The system analyzes diagnostic text to link procedures with their specific conditions.

Main Results:

  • Each task in the pipeline achieved above-baseline performance.
  • The system successfully produced concise recommendation summaries.

Conclusions:

  • Automated extraction of procedural conditions from medical texts is feasible.
  • The developed pipeline can enhance clinical decision-making by providing clear recommendation summaries.