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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.
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Errors In Hypothesis Tests01:14

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When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Errors occurring during blood pressure monitoring01:25

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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.
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Types of Errors: Detection and Minimization01:12

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Related Experiment Video

Updated: Aug 25, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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[DIAGNOSTIC ERROR: PREDICTING THE SEVERITY OF HARM].

Poriya Shahaf1, Tsipi Imber-Shahar1, Ranit Djarasi1

  • 1Hospital Division, General Headquarters, Clalit Health Services, Tel Aviv.

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Diagnostic errors in malpractice claims frequently lead to severe harm or death. Advanced age, cancer, cardiovascular disease, and specific medical specialties are linked to increased risk, highlighting areas for improved patient safety.

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Area of Science:

  • Medical malpractice litigation
  • Diagnostic errors
  • Patient safety

Context:

  • Diagnostic errors are a leading cause of malpractice claims.
  • Delayed, missed, or wrong diagnoses contribute significantly to patient harm.
  • Understanding these errors is crucial for healthcare improvement.

Purpose:

  • To identify factors associated with severe harm and mortality in malpractice claims due to delayed or wrong diagnoses.
  • To analyze the impact of patient demographics, diagnosis, and medical specialty on outcomes.
  • To inform strategies for reducing diagnostic errors.

Summary:

  • A review of 354 malpractice claims (2010-2019) revealed diagnostic errors led to severe harm in 24% and mortality in 25%.
  • Factors associated with severe harm included advanced age, cancer, cardiovascular disease, and involvement of pediatrics, internal medicine, or primary care departments.
  • Mortality was linked to advanced age, cancer, cardiovascular disease, internal medicine, and specific physician specialties.

Impact:

  • Findings underscore the significant patient safety risks posed by diagnostic errors.
  • Identifies specific patient groups and medical specialties requiring targeted interventions.
  • Emphasizes the need for greater awareness and systematic reviews to mitigate diagnostic failures and associated harm.