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Validation of a Novel Statistical Method to Identify Aberrant Patient Logging: A Multi-Institutional Study.

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Summary

A new statistical method using Mahalanobis Distance (MD) effectively identifies aberrant student logging in supervised clinical practice experiences (SCPEs), offering a less labor-intensive alternative to manual faculty review.

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Area of Science:

  • Medical Education
  • Health Professions Education
  • Data Analytics in Healthcare

Background:

  • Student patient encounter logging is crucial for assessing supervised clinical practice experiences (SCPEs).
  • Current manual review of logs by faculty is resource-intensive and its accuracy in reflecting actual patient encounters is unknown.
  • A need exists for efficient and accurate methods to identify aberrant logging behavior.

Purpose of the Study:

  • To identify and validate a statistical method for detecting aberrant student logging in SCPEs.
  • To compare the efficacy of this statistical method against traditional faculty review processes.

Main Methods:

  • A multi-institutional study involving 6 physician assistant (PA) programs.
  • Utilized Mahalanobis Distance (MD) to statistically identify probable multivariate outliers in student logs.
  • Validated the MD method against a gold standard of manual faculty consensus review.

Main Results:

  • The MD-based categorization demonstrated high sensitivity (0.846) and specificity (0.766) compared to faculty consensus.
  • No significant differences were found in key performance metrics between MD-based and individual program faculty categorization.
  • The MD method proved to be a reliable tool for identifying aberrant logging behavior.

Conclusions:

  • The Mahalanobis Distance (MD) method provides a statistically sound and less labor-intensive approach to identifying aberrant student logging.
  • This automated screening method can help optimize faculty resources and facilitate timely interventions for students.
  • Implementing MD-based screening can improve the quality and accuracy of clinical exposure logging during SCPEs.