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Evidence-Based Decision Making and Diagnostic Testing in Clinical Practice, Study notes of Nursing

This chapter provides a comprehensive overview of evidence-based decision making and diagnostic testing in clinical practice. It explores how clinicians utilize elements of patient history, physical examination, and diagnostic tests to inform their decisions. The chapter emphasizes the importance of evaluating and applying clinical evidence, understanding key statistical concepts, and effectively communicating findings to patients. It covers topics such as sensitivity, specificity, likelihood ratios, fagan nomogram, reproducibility, bias, generalizability, framing effects, and communication strategies.

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2024/2025

Uploaded on 01/13/2025

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Chapter 7: Evidence-Based Decision Making and Diagnostic Testing
This chapter explores how clinicians use elements of the history, physical
examination, and diagnostic tests to inform clinical decisions. It emphasizes
evaluating and applying clinical evidence, understanding key statistical
concepts, and communicating findings effectively to patients.
Using Elements of the History and Physical Examination as
Diagnostic Tools
1. Diagnostic Value:
oHistory and physical examination findings often act as "tests."
oExample: The presence of chest pain radiating to the arm in a
55-year-old smoker increases the likelihood of myocardial
infarction (heart attack).
2. Pre-Test Probability:
oThe likelihood a patient has a condition before additional tests.
oClinicians use population data, risk factors, and symptoms to
estimate this.
Evaluating Diagnostic Tests
1. Sensitivity and Specificity:
oSensitivity: The ability of a test to correctly identify those with
the disease (true positives).
Example: A highly sensitive COVID-19 test identifies most
infected individuals but may have some false positives.
Mnemonic: SeNsitive = rule iN (SnNout) for disease
screening.
oSpecificity: The ability to correctly identify those without the
disease (true negatives).
Example: A specific strep throat test ensures false positives
are minimal.
Mnemonic: SPecific = rule out (SpPin) when positive.
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Chapter 7: Evidence-Based Decision Making and Diagnostic Testing This chapter explores how clinicians use elements of the history, physical examination, and diagnostic tests to inform clinical decisions. It emphasizes evaluating and applying clinical evidence, understanding key statistical concepts, and communicating findings effectively to patients. Using Elements of the History and Physical Examination as Diagnostic Tools

  1. Diagnostic Value : o History and physical examination findings often act as "tests." o Example: The presence of chest pain radiating to the arm in a 55-year-old smoker increases the likelihood of myocardial infarction (heart attack).
  2. Pre-Test Probability : o The likelihood a patient has a condition before additional tests. o Clinicians use population data, risk factors, and symptoms to estimate this. Evaluating Diagnostic Tests
  3. Sensitivity and Specificity : o Sensitivity : The ability of a test to correctly identify those with the disease (true positives).  Example: A highly sensitive COVID-19 test identifies most infected individuals but may have some false positives.  Mnemonic: SeNsitive = rule iN (SnNout) for disease screening. o Specificity : The ability to correctly identify those without the disease (true negatives).  Example: A specific strep throat test ensures false positives are minimal.  Mnemonic: SPecific = rule out (SpPin) when positive.
  1. Likelihood Ratios (LRs) : o Combine sensitivity and specificity to quantify the impact of a test result on disease probability.  Positive LR (LR+) : How much a positive test increases the odds of disease.  Formula: Sensitivity / (1 - Specificity).  Negative LR (LR−) : How much a negative test decreases the odds of disease.  Formula: (1 - Sensitivity) / Specificity. o Example : A test with an LR+ of 10 greatly increases the probability of disease after a positive result. Fagan Nomogram  A graphical tool that combines pre-test probability, likelihood ratio, and post-test probability. o How it works :
  2. Draw a line from the pre-test probability (based on clinical judgment).
  3. Pass it through the LR of the test.
  4. Read the post-test probability on the right. o Example : A pre-test probability of 50%, with an LR+ of 5, increases the post-test probability to about 85%. Natural FrequenciesConcept : Presenting probabilities as actual numbers rather than percentages to improve understanding. o Example: "Out of 1,000 patients like you, 80 will have the disease, and this test will correctly identify 70 of them." Reproducibility and Kappa Score

Decision-Making and Reducing Framing Effects

  1. Framing Effects : o The way information is presented affects decision-making.  Example : "10% chance of dying" vs. "90% chance of surviving" conveys the same data but impacts perception differently.
  2. Informed Shared Decision-Making : o Clinicians should reduce framing effects and encourage collaborative decisions. o Use Decision Aids :  Provide risks, benefits, and options visually (e.g., diagrams, charts). Approaches to Communication
  3. The Five A’s : o Ask : About patient concerns and symptoms. o Advise : Provide clear recommendations. o Assess : Understand patient readiness for change. o Assist : Offer resources or tools. o Arrange : Follow-up care.
  4. FRAMES Approach : o Feedback : Share personal risk factors. o Responsibility : Emphasize patient autonomy. o Advice : Provide tailored suggestions. o Empathy : Show understanding. o Self-efficacy : Build confidence in making changes. Key Strategies to Deepen Understanding
  1. Practice Calculations : o Example for Sensitivity : If a test identifies 80 out of 100 patients with disease (true positives) and misses 20 (false negatives), sensitivity is 80%. o Example for LR+ : A test with sensitivity 0.9 and specificity 0. has an LR+ of 0.9 / (1 – 0.8) = 4.5. Use this to predict post-test probability.
  2. Use Visual Aids : o Draw a Fagan Nomogram and practice mapping pre-test probabilities (e.g., 30%) and LRs to determine post-test outcomes.
  3. Teach Back : o Explain sensitivity, specificity, and LRs to a peer. Example: "Sensitivity tells us how good the test is at finding people who actually have the disease."
  4. Apply Concepts Clinically : o Estimate pre-test probabilities in patient cases. Example: In a smoker with chronic cough, pre-test probability for lung cancer is moderate; order a chest CT for confirmation.
  5. Work with Numbers : o Convert percentages into frequencies. Example: "If 1,000 people are tested and 90% specificity, 100 people without the disease will test positive (false positives)."