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Record W4289530769 · doi:10.2196/preprints.41393

An After-Hours Virtual Care Service for Children With Medical Complexity and New Medical Technology: Mixed Methods Feasibility Study (Preprint)

2022· preprint· en· W4289530769 on OpenAlexaboutno aff
Katherine Babayan, Krista Keilty, Jessica Esufali, Francisco J Grajales

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicLiterature Analysis and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineService (business)Medical emergencyMedical recordNursingFamily medicineBusiness

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> Family caregivers (FCs) of children with medical complexity require specialized support to promote the safe management of new medical technologies (eg, gastrostomy tubes) during hospital-to-home transitions. With limited after-hours services available to families in home and community care, medical device complications that arise often lead to increased FC stress and unplanned emergency department (ED) visits. To improve FC experiences, enable safer patient discharge, and reduce after-hours ED visits, this study explores the feasibility of piloting a 24/7 virtual care service (Connected Care Live) with families to provide real-time support by clinicians expert in the use of pediatric home care technologies. </sec> <sec> <title>OBJECTIVE</title> This study aims to establish the economic, operational, and technical feasibility of piloting the expansion of an existing nurse-led after-hours virtual care service offered to home and community care providers to FCs of children with newly inserted medical devices after hospital discharge at Toronto’s Hospital for Sick Children (SickKids). </sec> <sec> <title>METHODS</title> This exploratory study, conducted from October 2020 to August 2021, used mixed data sources to inform service expansion feasibility. Semistructured interviews were conducted with FCs, nurses, and hospital leadership to assess the risks, benefits, and technical and operational requirements for sustainable and cost-effective future service operations. Time and travel savings were estimated using ED visit data in SickKids’ electronic medical records (Epic) with a chief complaint of “medical device problems,” after-hours medical device inquiries from clinician emails and voicemails, and existing service operational data. </sec> <sec> <title>RESULTS</title> A total of 30 stakeholders were interviewed and voiced the need for the proposed service. Safer and more timely management of medical device complications, improved caregiver and provider experiences, and strengthened partnerships were identified as expected benefits, while service demand, nursing practice, and privacy and security were identified as potential risks. A total of 47 inquiries were recorded over 2 weeks from March 26, 2021, to April 8, 2021, with 51% (24/47) assessed as manageable via service expansion. This study forecasted annual time and travel savings of 558 hours for SickKids and 904 hours and 22,740 km for families. Minimal technical and operational requirements were needed to support service expansion by leveraging an existing platform and clinical staff. Of the 212 ED visits related to “medical device problems” over 6 months from September 1, 2020, to February 28, 2021, enteral feeding tubes accounted for nearly two-thirds (n=137, 64.6%), with 41.6% (57/137) assessed as virtually manageable. </sec> <sec> <title>CONCLUSIONS</title> Our findings indicate that it is feasible to pilot the expansion of Connected Care Live to FCs of children with newly inserted enteral feeding tubes. This nurse-led virtual caregiver service is a promising tool to promote safe hospital-to-home transitions, improve FC experiences, and reduce after-hours ED visits. </sec>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.003
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 head0.039
GPT teacher head0.401
Teacher spread0.362 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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