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Record W2789335579 · doi:10.1097/acm.0000000000002178

Reconciling Technology and People: Quality of Care During End-of-Rotation Transfers

2018· letter· en· W2789335579 on OpenAlexaffabout
Diana Ramos Torres

Bibliographic record

VenueAcademic Medicine · 2018
Typeletter
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnthusiasmFeelingQuality (philosophy)PsychologyHealth careNonverbal communicationMedicineNursingSocial psychologyDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

To the Editor: Beginning my inpatient internal medicine rotation, I struggled with my first case: A 75-year-old male, nonverbal and hospitalized for five weeks. What could I do for him? What was the plan moving forward? The daily brief notes concluded, “Stable, continue the same.” What was I to make of this? Electronic tools provided a summary of his hospital stay, but I wished the resident who had been in charge before I began my rotation was with me that morning. So, at the start of my internal medicine rotation, technology abounded. But it was not enough. Later during the rotation, our team split into two to take turns at taking time off for the holidays. Again, written handoffs and technology seemed insufficient to facilitate effective transfers of care. Further, end-of-rotation transfers disrupt continuity of care, prolong hospital stays, and increase mortality.1 Yet, their effect on care quality appears underappreciated. Despite doctors’ enthusiasm for technology, such tools might limit communication of thoughts and feelings, reducing insight into patients’ health status. Instead, person-to-person transfer is reflective, including unwritten thoughts shared verbally. I have noticed that barriers to transferring care at rotation’s end include not only insufficient person-to-person communication but also lack of protected time, inability to clarify notes, insufficient training among residents and students, a mismatch between admissions and staff schedules, and inconsistent transfer methods. The time required to understand complex cases often delays care and contributes to management errors—despite the availability of electronic tools. New teams’ expectations might not match patients’ expectations, so care negotiations start from “scratch,” delaying medical and other arrangements and prolonging hospital stay. The lack of information and experience at transfers can negatively affect discharge decisions.2 Students, residents, and fellows play important roles during transfers of care; regrettably, actions are too often hasty, and finding time to prepare for these transfers can be challenging. Transfer of care requires more than handoff training and electronic platforms for notes. Starting rotations one day early, allowing trainees to take transfers directly from departing teams, and/or facilitating check-ins by phone or video conferencing at the end of the day could all improve transfers. Voice memo applications in hospital computers could overcome barriers at the end of rotations, supporting reflective practice. Despite the wonders of technology, both reflection and personal interaction during transfer of care in a complex, vulnerable environment are essential. Medical trainees must be able to prioritize care coordination with one another. Person-to-person communication will help close the gap between technology and its physician users, thus offering patients the best of both. Acknowledgments: The author would like to thank Dr. Peter Nugus for his support and encouragement. Diana Ramos Torres, MD, MAHPEPostgraduate year 2 family medicine resident and PhD student, Family Medicine–Medical Education, Department of Family Medicine, McGill University, Montreal, Quebec, Canada; [email protected] First published online February 13, 2018

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)0.005
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.012
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.005
Open science0.0030.002
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0050.002

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 head0.032
GPT teacher head0.345
Teacher spread0.313 · 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 designNot applicable
Domainnot available
GenreCommentary

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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Citations0
Published2018
Admission routes2
Has abstractyes

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