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Related Concept Videos

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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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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.
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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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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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The High-Value Care Rounding Tool: Development and Validity Evidence.

Corrie E McDaniel1, Andrew A White, Miranda C Bradford

  • 1C.E. McDaniel is clinical assistant professor, Department of Pediatrics, University of Washington, Seattle, Washington. A.A. White is associate professor, Department of Medicine, University of Washington, Seattle, Washington. M.C. Bradford is a biostatistician, Center for Clinical and Translational Research, Seattle Children's Research Institute, Seattle, Washington. C.D. Sy is clinical instructor, Department of Medicine, University of Washington, Seattle, Washington. T. Chen is clinical instructor, Department of Medicine, University of Washington, Seattle, Washington. D. Brock is associate professor, Department of Family Medicine, University of Washington, Seattle, Washington. J. Foti is clinical associate professor, Department of Pediatrics, University of Washington, Seattle, Washington. J.B. Beck is assistant professor, Department of Pediatrics, University of Washington, Seattle, Washington.

Academic Medicine : Journal of the Association of American Medical Colleges
|September 1, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed a new tool to measure high-value care (HVC) discussions during bedside teaching. This instrument helps assess HVC practices and educational interventions effectively.

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Area of Science:

  • Medical Education
  • Health Services Research
  • Quality Improvement

Background:

  • Current practices in high-value care (HVC) bedside teaching are poorly understood.
  • A lack of validated instruments hinders assessment of HVC educational interventions.
  • Measuring HVC behaviors at the bedside is crucial for improving healthcare value.

Purpose of the Study:

  • To define observable HVC concepts for bedside teaching.
  • To develop and validate an instrument measuring HVC discussion content and frequency.
  • To address the gap in assessing HVC translation into clinical practice.

Main Methods:

  • Developed the HVC Rounding Tool through four iterative phases based on Messick's validity framework.
  • Utilized literature review and a modified Delphi approach with expert consensus to define HVC items.
  • Piloted the tool with trained raters observing 148 patient encounters, assessing inter-rater reliability and agreement.

Main Results:

  • The HVC Rounding Tool includes 11 observable items across quality, cost, and patient values domains.
  • Inter-rater reliability (weighted kappa) improved significantly across all domains after iterative piloting.
  • Percent positive agreement increased from 65.3% to 98.1%, demonstrating robust validity evidence.

Conclusions:

  • The HVC Rounding Tool is the first instrument with established validity evidence for measuring bedside HVC discussions.
  • This tool provides a reliable method to assess the integration of HVC principles into clinical education.
  • It facilitates the evaluation of educational initiatives aimed at promoting high-value care practices.