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Record W2082260805 · doi:10.2196/jmir.1655

An Evaluation of the Use of Smartphones to Communicate Between Clinicians: A Mixed-Methods Study

2011· article· en· W2082260805 on OpenAlexaff
Robert Wu, Peter G. Rossos, Sherman Quan, Scott Reeves, Vivian Lo, Brian M. Wong, Mark Chun Ming Cheung, Dante Morra

Bibliographic record

VenueJournal of Medical Internet Research · 2011
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreThe Wilson CentreUniversity Health NetworkUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsPagerHuman multitaskingMedicineHealth careNursingPsychologyMedical emergencyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Communication between clinicians is critical to providing quality patient care but is often hampered by limitations of current systems. Smartphones such as BlackBerrys may improve communication, but studies of these technologies have been limited to date. OBJECTIVE: Our objectives were to describe how smartphones were adopted for clinical communication within general internal medical wards and determine their impact on team effectiveness and communication. METHODS: This was a mixed-methods study that gathered data from the frequency of smartphone calls and email messages, clinicians' interviews, and ethnographic observations of clinical communication interactions. Triangulation of qualitative and quantitative data was undertaken to develop common themes that encompass comprehensive and representative insights across different methods. RESULTS: Findings from our study indicated that over a 24-hour period, nurses sent on average 22.3 emails to the physicians mostly through the "team smartphone," the designated primary point of contact for a specific medical team. Physicians carrying the team smartphone received on average 21.9 emails and 6.4 telephone calls while sending out 6.9 emails and initiating 8.3 telephone calls over the 24-hour period. Our analyses identified both positive and negative outcomes associated with the use of smartphones for clinical communication. There was a perceived improvement in efficiency over the use of pagers for clinical communication for physicians, nurses, and allied health professionals. In particular, residents found that the use of smartphones helped to increase their mobility and multitasking abilities. Negative outcomes included frequent interruptions and discordance between what doctors and nurses considered urgent. Nurses perceived a worsening of the interprofessional relationships due to overreliance on messaging by text with a resulting decrease in verbal communication. Unprofessional behaviors were observed in the use of smartphones by residents. CONCLUSIONS: Routine adoption of smartphones by residents appeared to improve efficiency over the use of pagers for physicians, nurses, and allied health professionals. This was balanced by negative communication issues of increased interruptions, a gap in perceived urgency, weakened interprofessional relationships, and unprofessional behavior. Further communication interventions are required that balance efficiency and interruptions while maintaining or even improving interprofessional relationships and professionalism.

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)0.041
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.733
GPT teacher head0.704
Teacher spread0.029 · 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 designQualitative
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".

Quick stats

Citations211
Published2011
Admission routes1
Has abstractyes

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