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Related Concept Videos

Documentation of Nursing Diagnosis01:10

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...
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

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...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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.
For example, a patient with a chronic illness...
Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

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:
Maintain Confidentiality and Security:
Data Validation01:03

Data Validation

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.
Nursing assessment guides are generally based on holistic models rather than medical...

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Related Experiment Video

Updated: May 18, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Identifying early warning signs for diagnostic errors in primary care: a qualitative study.

John Balla1, Carl Heneghan, Clare Goyder

  • 1Centre for Evidence-based Medicine, University of Oxford, Oxford, UK.

BMJ Open
|September 18, 2012
PubMed
Summary

Cognitive biases early in diagnosis can lead to errors later. Recognizing these biases and knowledge gaps as warning signs can help reduce diagnostic errors in primary care.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: May 18, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Medical error analysis
  • Cognitive psychology in healthcare
  • Primary care diagnostics

Background:

  • Diagnostic errors pose a significant risk in primary care.
  • Understanding the mechanisms of error is crucial for prevention.

Purpose of the Study:

  • To investigate the mechanisms of diagnostic error in primary care.
  • To identify warning signs for diagnostic error within the reasoning process.

Main Methods:

  • Qualitative study utilizing semistructured interviews with 15 experienced general practitioners in Oxfordshire, UK.
  • Analysis of 45 cases of diagnostic error from interview transcripts to identify themes and risk factors.

Main Results:

  • The initiation and closure phases of cognitive processing are most vulnerable to error.
  • Early cognitive biases can lead to errors later in the diagnostic process.
  • Ignoring critical cues or 'red flags' is linked to biased initial framing or knowledge gaps.

Conclusions:

  • Cognitive biases at the initial problem framing stage are significant predictors of later diagnostic errors.
  • Lack of knowledge is a key contributing factor to diagnostic errors.
  • Reducing errors requires early recognition of errors and near misses, and continuous professional development focused on reflective practice to address process biases and knowledge deficits.