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Record W4307055435 · doi:10.1093/pch/pxac100.076

77 Evaluating virtual family-centered rounds effectiveness, efficiency and usability during the COVID-19 pandemic

2022· article· en· W4307055435 on OpenAlexaff
Melanie Buba, Catherine Dulude, Stephanie Sutherland, Dennis Newhook, Maryam Attef

Bibliographic record

VenuePaediatrics & Child Health · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsCarleton UniversityChildren's Hospital of Eastern OntarioAgricultural Research Institute of OntarioUniversity of Ottawa
Fundersnot available
KeywordsUsabilityDescriptive statisticsPandemicData collectionCoronavirus disease 2019 (COVID-19)PsychologyVirtual patientMedicineComputer scienceNursingStatisticsHuman–computer interactionMathematicsInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Virtual care has seen exponential growth since the onset of the COVID-19 pandemic, however, the evaluation of virtual care tools and services is lacking, particularly in the inpatient setting. In April 2020, we virtualized our in-person family-centered rounds (FCR) process and demonstrated the ability to perform virtual family-centered rounds (vFCR). In this study, we evaluate vFCR against the accepted standard of in-person FCR to ensure high quality care is maintained and encourage adoption by health care providers, administrators, patients and caregivers. Objectives The objective of this study is to compare vFCR to established core components and timing for in-person FCR. Perceptions of overall satisfaction, safety and technology usability were also explored. Design/Methods This is a mixed methods process evaluation of vFCR. Data collection was through virtual and in-person observation and post-vFCR survey of participants. Virtual observations focused on timing and adherence to core components of FCR, while in-person naturalistic observations focused on technology interaction and usability. Observation data underwent quantitative and content analysis. Data from post-vFCR questionnaires were subject to descriptive statistical analysis and content analysis of free-text responses. Results Sixty-two vFCR were observed virtually and 35 vFCR were observed in-person. Adherence to the core components of FCR during vFCR was variable (Table 1). Mean duration of a single patient round was 8.44 ± 4.93 minutes, with a mean transition time between patients of 3.96 ± 2.96 minutes. One hundred and four surveys were completed (76% response rate), 42 by patients and caregivers and 62 by members of the interdisciplinary medical team. The majority (93%) of respondents surveyed were satisfied or very satisfied with vFCR, and 67% felt it was important or very important to do FCR virtually during the pandemic to keep people safer. Importantly, vFCR was perceived by 96% of medical team members as supporting shared decision making with patients and caregivers, and 78% of patients/caregivers felt like a valued partner in their (child’s) care. Virtual family-centered rounds technology was perceived as easy or very easy to use by 95% of respondents. Additional positive and negative comments were submitted by 38% of respondents about their experience with vFCR (Figure 1). Conclusion Virtual family-centered rounds afford adherence to the core components of family-centered rounds. Satisfaction with vFCR and perceived usability of vFCR technology were both highly rated. Respondents also felt vFCR were important for safety during the pandemic. Rounds duration and transition times between patients were seen as opportunities for improvement. Observation and questionnaire data suggest the efficiency and quality of vFCR may be optimized through routine training on the rounding process and technology, as well as recognition of family-centered rounds as a component of inpatient pediatric care that should be prioritized.

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)0.018
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.063
GPT teacher head0.393
Teacher spread0.330 · 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 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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