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Record W4210374180 · doi:10.2196/preprints.25505

A Clinical Communication Tool (Loop) for Team-Based Care in Pediatric and Adult Care Settings: Hybrid Mixed Methods Implementation Study (Preprint)

2020· preprint· en· W4210374180 on OpenAlexaff
Amna Husain, Eyal Cohen, Raluca Dubrowski, Trevor Jamieson, Allison Kurahashi, Bhadra Lokuge, Adam Rapoport, Stéphanie Saunders, Elaine Stasiulis, Jennifer Stinson, Saranjah Subramaniam, Pete Wegier, Melanie Barwick

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHumber River Regional HospitalMcMaster UniversityInstitute for Clinical Evaluative SciencesUniversity of TorontoSickKids FoundationPublic Health OntarioHospital for Sick ChildrenSinai Health SystemLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsUploadHealth carePreprintWorkflowNursingMedicineLoop (graph theory)Medical educationPsychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND Communication within the circle of care is central to coordinated, safe, and effective care; yet patients, caregivers, and health care providers often experience poor communication and fragmented care. Through a sequential program of research, the Loop Research Collaborative developed a web-based, asynchronous clinical communication system for team-based care. Loop assembles the circle of care centered on a patient, in private networking spaces called Patient Loops. The patient, their caregiver, or both are part of the Patient Loop. The communication is threaded, it can be filtered and sorted in multiple ways, it is securely stored, and can be exported for upload to a medical record. OBJECTIVE The objective of this study was to implement and evaluate Loop. The study reporting adheres to the Standards for Reporting Implementation Research. METHODS The study was a hybrid type II mixed methods design to simultaneously evaluate Loop’s clinical and implementation effectiveness, and implementation barriers and facilitators in 6 health care sites. Data included monthly user check-in interviews and bimonthly surveys to capture patient or caregiver experience of continuity of care, in-depth interviews to explore barriers and facilitators based on the Consolidated Framework for Implementation Research (CFIR), and Loop usage extracted directly from the Loop system. RESULTS We recruited 25 initiating health care providers across 6 sites who then identified patients or caregivers for recruitment. Of 147 patient or caregiver participants who were assessed and met screening criteria, 57 consented and 52 were enrolled on Loop, creating 52 Patient Loops. Across all Patient Loops, 96 additional health care providers consented to join the Loop teams. Loop usage was followed for up to 8 months. The median number of messages exchanged per team was 1 (range 0-28). The monthly check-in and CFIR interviews showed that although participants acknowledged that Loop could potentially fill a gap, existing modes of communication, workflows, incentives, and the lack of integration with the hospital electronic medical records and patient portals were barriers to its adoption. While participants acknowledged Loop’s potential value for engaging the patient and caregiver, and for improving communication within the patient’s circle of care, Loop’s relative advantage was not realized during the study and there was insufficient tension for change. Missing data limited the analysis of continuity of care. CONCLUSIONS Fundamental structural and implementation challenges persist toward realizing Loop’s potential as a shared system of asynchronous communication. Barriers include health information system integration; system, organizational, and individual tension for change; and a fee structure for health care provider compensation for asynchronous communication.

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)0.068
metaresearch head score (Gemma)0.069
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.563
Teacher spread0.497 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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