Enhancing medical education with data-driven software: The TrainCoMorb app

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Medical education can take advantage of big data to enhance the learning experience of students. This paper describes the development of TrainCoMorb, an online, data-driven application for medical students who can practice recognizing comorbidities and their attributable risk for negative outcomes. Trainees access TrainCoMorb to create scenarios of comorbidities, step-by-step, and see snapshots of the risk for inpatient death, hospital septicemia and the projected length of stay. The study utilized an enormous claims dataset (N=11m.). A dynamic Bayesian algorithm was developed, which calculates and updates conditional probabilities for the outcomes under study in each phase of an ongoing scenario. The trainee initiates a scenario by selecting demographics and a principal diagnosis, then adds chronic and hospital-acquired conditions to see a summary of the attributable risk in each phase. TrainCoMorb is anticipated to assist medical students gain a better understanding of comorbidities and their impact on clinical outcomes.

Original languageEnglish
Title of host publicationTHE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC
EditorsJohn Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos, Emmanouil Zoulias
PublisherIOS Press
Pages83-86
Number of pages4
ISBN (Electronic)9781643680927
DOIs
StatePublished - 2020

Publication series

NameStudies in Health Technology and Informatics
Volume272
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Keywords

  • Bayesian methods
  • Comorbidities
  • Medical education

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