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Readmission Risk Assessment Technologies and the Anchoring and Adjustment Heuristic.

James H Schreiner1, Deborah L Thurston2, Ann Willemsen-Dunlap3

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Healthcare workers

Keywords:
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Area of Science:

  • Healthcare Management
  • Clinical Decision Support
  • Health Informatics

Background:

  • High patient readmission rates (23%) incur significant costs ($42 billion annually).
  • Integrating predictive risk tools into human-led decision-making processes remains unclear.
  • Current healthcare relies heavily on expert judgment for readmission risk assessment.

Purpose of the Study:

  • To investigate how healthcare workers' risk estimates are influenced by a predictive readmission risk tool.
  • To test the hypothesis of anchoring and adjustment behavior in response to the tool and expert input.
  • To understand the interplay between artificial intelligence tools and human expertise in clinical settings.

Main Methods:

  • A study involving 56 case managers at Order of St. Francis Healthcare.
  • Development of fictional patient scenarios for risk assessment tasks.
  • Survey design with control and experimental groups, analyzed using ANOVA/GLM and t-tests.

Main Results:

  • Healthcare workers' initial risk estimates were anchored by the predictive tool's output.
  • Workers adjusted estimates based on their own expertise after initial anchoring.
  • Further adjustments were made when presented with additional human expert opinions.

Conclusions:

  • Healthcare workers demonstrated a reliance on both predictive models and their own expertise.
  • The study validates a combined approach using AI tools and human judgment for readmission risk.
  • Findings suggest a hybrid model for optimizing clinical decision support systems.