Curriculum for Early Exposure to Clinical Informatics and Data Science for Noninformatics Trainees to Promote Interest and Inclusion in Informatics
Background Curricula aimed at increasing exposure to informatics and practical data analytics among medical trainees could increase their effectiveness in clinical research, quality improvement, and clinical operations. Objectives The Clinical Informatics Data Science (CI-DS) pathway is a cross-disc...
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| Published in: | ACI open Vol. 7; no. 2; pp. e79 - e86 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01.07.2023
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| ISSN: | 2566-9346, 2566-9346 |
| Online Access: | Get full text |
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| Summary: | Background Curricula aimed at increasing exposure to informatics and practical data analytics among medical trainees could increase their effectiveness in clinical research, quality improvement, and clinical operations.
Objectives The Clinical Informatics Data Science (CI-DS) pathway is a cross-disciplinary curriculum aimed at improving informatics exposure among medical trainees. We describe the development of this novel curriculum, the inaugural cohort, and lessons learned.
Methods The CI-DS pathway is framed around upfront informatics didactics followed by a longitudinal, experiential training focused on mentorship, clinical data extraction/machine learning, and health technology governance. The curriculum was evaluated based on pre- and postpathway surveys completed by learners and logs of the elective activities selected by learners.
Results The CI-DS pathway attracted 19 learners across 12 medical subspecialties, from medical students to fellows. Baseline surveys showed limited exposure to informatics across learners. The top three longitudinal activities completed were participating in electronic health record (EHR) governance meetings, data science supplemental courses, and designated mentorship meetings. Comparison of baseline with postpathway surveys demonstrated significant improvements in learner self-reported confidence in appraising an EHR modification ticket, accessing UCSF's deidentified data, exploring a database with basic structured query language (SQL), extracting data using SQL, and interpreting machine learning models.
Conclusion An early exposure curriculum in clinical informatics with training in data extraction and governance can successfully recruit a diverse array of learners and improve confidence in practical informatics skills. We reflect on the strengths and weaknesses of this curriculum, and summarize the lessons learned to guide others in creating similar curricula for noninformatics clinicians. |
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| ISSN: | 2566-9346 2566-9346 |
| DOI: | 10.1055/s-0043-1775971 |