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Record W2782046928 · doi:10.2196/mededu.8976

Integrating Patient-Centered Electronic Health Record Communication Training into Resident Onboarding: Curriculum Development and Post-Implementation Survey Among Housestaff

2018· article· en· W2782046928 on OpenAlexvenueno aff
Maria Alcocer Alkureishi, Wei Wei Lee, Sandra Webb, Vineet M. Arora

Bibliographic record

VenueJMIR Medical Education · 2018
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersUniversity of ChicagoArnold P. Gold Foundation
KeywordsOnboardingCurriculumSpecialtyMedicineLikert scaleMedical educationElectronic health recordScale (ratio)Best practiceFamily medicineNursingHealth carePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Electronic health record (EHR) use can enhance or undermine the ability of providers to deliver effective, humanistic patient-centered care. Given patient-centered care has been found to positively impact patient health outcomes, it is critical to provide formal education on patient-centered EHR communication skills. Unfortunately, despite increasing worldwide EHR adoption, few institutions educate trainees on EHR communication best practices. OBJECTIVE: The goal of this research was to develop and deliver mandatory patient-centered EHR training to all incoming housestaff at the University of Chicago. METHODS: We developed a brief patient-centered EHR use curriculum highlighting best practices based on a literature search. Training was embedded into required EHR onboarding for all incoming housestaff (interns, residents, and fellows) at the University of Chicago in 2015 and was delivered by institutional Clinical Applications Trainers. An 11-item posttraining survey consisting of ten 5-point Likert scale questions and 1 open-ended question was administered. Responses at the high end of the scale were grouped to dichotomize data. RESULTS: All 158 of the incoming 2015 postgraduate trainees participated in training and completed surveys (158/158, 100.0%). Just over half (86/158, 54.4%) were interns and the remaining were residents and fellows (72/158, 45.6%). One-fifth of respondents (32/158, 20.2%) were primary care trainees (defined as internal medicine, pediatric, and medicine-pediatric trainees), and the remaining 79.7% (126/158) were surgical or specialty trainees. Self-perceived pre- versus posttraining knowledge of barriers, best practices, and ability to implement patient-centered EHR skills significantly increased (3.1 vs 3.9, P<.001 for all). Most felt training was effective (90.5%), should be required (86.7%), and would change future practice as a result (70.9%). The only significant difference between intern and resident/fellow responses was prior knowledge of patient-centered EHR use barriers; interns endorsed higher prior knowledge than resident peers (3.27 vs 2.94 respectively, P=.03). Response comparison of specialty or surgical trainees (n=126) to primary care trainees (n=32) showed no significant differences in prior knowledge of barriers (3.09 vs 3.22, P=.50), of best practices (3.08 vs 2.94, P=.37), or prior ability to implement best practices (3.11 vs 2.84, P=.15). Primary care trainees had larger increases posttraining than surgical/specialty peers in knowledge of barriers (0.8 vs 0.7, P=.62), best practices (1.1 vs 0.8, P=.08), and ability to implement best practices (1.1 vs 0.7, P=.07), although none reached statistical significance. Primary care trainees also rated training as more effective (4.34 vs 4.09, P=.03) and felt training should be required (4.34 vs 4.09, P=.10) and would change their future practice as a result (4.13 vs 3.73, P=.02). CONCLUSIONS: Embedding EHR communication skills training into required institutional EHR training is a novel and effective way to teach key EHR skills to trainees. Such training may help ground trainees in best practices and contribute to cultivating an institutional culture of humanistic, patient-centered EHR use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.464
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations16
Published2018
Admission routes1
Has abstractyes

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