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Record W4321611701 · doi:10.2196/42881

Evaluation of a Secure Messaging System in the Care of Children With Medical Complexity: Mixed Methods Study

2023· article· en· W4321611701 on OpenAlexafffundvenue
Camilla Parpia, Clara Moore, Madison Beatty, Susan Miranda, Sherri Adams, Jennifer Stinson, Arti D. Desai, Leah Bartlett, Erin Culbert, Eyal Cohen, Julia Orkin

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsRoyal Victoria Regional Health CentreCredit Valley HospitalHospital for Sick ChildrenSickKids FoundationUniversity of Toronto
FundersGovernment of OntarioOntario Centres of Excellence
KeywordsPhoneThematic analysisComputer scienceMobile phoneDescriptive statisticsWorld Wide WebQualitative research

Abstract

fetched live from OpenAlex

BACKGROUND: The Connecting2gether (C2) platform is a web and mobile-based information-sharing tool that aims to improve care for children with medical complexity and their families. A key feature of C2 is secure messaging, which enables parental caregivers (PCs) to communicate with their child's care team members (CTMs) in a timely manner. OBJECTIVE: The objectives of this study were to (1) evaluate the use of a secure messaging system, (2) examine and compare the content of messages to email and phone calls, and (3) explore PCs' and CTMs' perceptions and experiences using secure messaging as a method of communication. METHODS: This is a substudy of a larger feasibility evaluation of the C2 platform. PCs of children with medical complexity were recruited from a tertiary-level complex care program to use the C2 platform for 6 months. PCs could invite CTMs involved in their child's care to register on the platform. Messages were extracted from C2, and phone and email data were extracted from electronic medical records. Quantitative data from the use of C2 were analyzed using descriptive statistics. Messaging content codes were iteratively developed through a review of the C2 messages and phone and email communication. Semistructured interviews were completed with PCs and CTMs. Communication and interview data were analyzed using thematic analysis. RESULTS: A total of 36 PCs and 66 CTMs registered on the C2 platform. A total of 1861 messages were sent on C2, with PCs and nurse practitioners sending a median of 30 and 74 messages, respectively. Of all the C2 messages, 85.45% (1257/1471) were responded to within 24 hours. Email and phone calls focused primarily on clinical concerns and medications, whereas C2 messaging focused more on parent education, proactive check-ins, and nonmedical aspects of the child's life. Four themes emerged from the platform user interviews related to C2 messaging: (1) connection to the care team, (2) efficient communication, (3) clinical uses of secure messaging, and (4) barriers to use. CONCLUSIONS: Overall, our study provides valuable insight into the benefits of secure messaging in the care of children with medical complexity. Secure messaging provided the opportunity for continued family teaching, proactive check-ins from health care providers, and casual conversations about family and child life, which contributed to PCs feeling an improved sense of connection with their child's health care team. Secure messaging can be a beneficial additional communication method to improve communication between PCs and their care team, reducing the associated burden of care coordination and ultimately enhancing the experience of care delivery. Future directions include the evaluation of secure messaging when integrated into electronic medical records, as this has the potential to work well with CTM workflow, reduce redundancy, and allow for new features of secure messaging.

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)0.058
metaresearch head score (Gemma)0.061
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 score0.058
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.145
GPT teacher head0.511
Teacher spread0.366 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

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