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Related Concept Videos

Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

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Related Experiment Video

Updated: Jul 12, 2026

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

Using decision tree models to depict primary care physicians CRC screening decision heuristics.

Sarah B Wackerbarth1, Yelena N Tarasenko, Laurel A Curtis

  • 1Martin School of Public Policy and Administration, University of Kentucky, 435 Patterson Office Tower, Lexington, KY, USA. sbwack0@uky.edu

Journal of General Internal Medicine
|August 22, 2007
PubMed
Summary

Primary care physicians use specific decision rules, or heuristics, when recommending colorectal cancer screening. These heuristics for timing and type of screening were identified, but no link between them was found.

Related Experiment Videos

Last Updated: Jul 12, 2026

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 Decision Making
  • Health Services Research
  • Oncology

Background:

  • Colorectal cancer screening is crucial for early detection and prevention.
  • Primary care physicians play a key role in guiding patients on screening recommendations.
  • Understanding physician decision-making processes can inform quality improvement initiatives.

Purpose of the Study:

  • To identify the specific decision heuristics used by primary care physicians for colorectal cancer screening recommendations.
  • To analyze the criteria physicians employ when determining screening timing and type.

Main Methods:

  • Qualitative study employing in-depth, semi-structured interviews with 66 primary care physicians.
  • Analysis of interview transcripts using constant comparative methodology to develop decision trees.
  • Independent review by three researchers to ensure reliability of identified heuristics.

Main Results:

  • Physicians utilized four distinct heuristics for screening timing (e.g., "age 50," "age 50 with family history adjustments").
  • Five heuristics were identified for screening type selection (e.g., fecal occult blood test, colonoscopy, combination).
  • No association was found between the heuristics used for timing and those used for screening type.

Conclusions:

  • Evidence confirms the use of heuristics in colorectal cancer screening recommendations by primary care physicians.
  • Further research is necessary to evaluate the impact of these heuristics on the quality of cancer care.
  • Identifying these decision patterns is a step towards optimizing screening strategies.