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Record W4321013857 · doi:10.2196/preprints.45451

Real-Time Virtual Support as an Emergency Department Strategy for Rural, Remote, and Indigenous Communities in British Columbia: Descriptive Case Study (Preprint)

2023· preprint· en· W4321013857 on OpenAlexaboutno aff
Helen Novak Lauscher, Brydon Blacklaws, Erika Pritchard, Elsie Jiaxi Wang, Kurtis Stewart, Jeff Beselt, Kendall Ho, John Pawlovich

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEmergency departmentPeer supportMedicineNursingHealth careBurnoutPreprintPublic relationsPolitical science

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> British Columbia has over 200 rural, remote, and Indigenous communities that have limited health care resources due to physician isolation, sparsity in clinical resources, the lack of collegial support, and provider burnout. Real-time virtual support (RTVS) peer-to-peer pathways provide support to patients and providers. Amid the COVID-19 pandemic exacerbating existing health care disparities and equitable access to timely care, RTVS presents a portable and additional opportunity to be deployed in a hospital or patient home setting in rural communities. We highlight the story of the Rural Urgent Doctor in-aid (RUDi) pathway within RTVS that successfully supported the Dawson Creek District Hospital (DCDH) emergency department (ED) in 2021. </sec> <sec> <title>OBJECTIVE</title> This study aims to describe the rapid implementation process and identify facilitators and barriers to successful implementation. </sec> <sec> <title>METHODS</title> This case study is grounded in the Quadruple Aim and Social Accountability frameworks for health systems learning. The entire study period was approximately 6 months. After 1 week of implementation, we interviewed RUDi physicians, DCDH staff, health authority leadership, and RTVS staff to gather their experiences. Content analysis was used to identify themes that emerged from the interviews. </sec> <sec> <title>RESULTS</title> RUDi physicians covered 39 overnight shifts and were the most responsible providers (MRPs) for 245 patients who presented to the DCDH ED. A total of 17 interviews with key informants revealed important themes related to leadership and relationships as facilitators of the coverage’s success, the experience of remote physician support, providing a “safety net,” finding new ways of interprofessional collaboration, and the need for extensive IT support throughout. Quality improvement findings identified barriers and demonstrated tangible recommendations for how this model of support can be improved in future cases. </sec> <sec> <title>CONCLUSIONS</title> By acting as the MRP during overnight ED shifts, RUDi prevented the closure of the DCDH ED and the diversion of patients to another rural hospital. Rapid codevelopment and implementation of digital health solutions can be leveraged with existing partnerships and mutual trust between RTVS and rural EDs to ease the pressures of a physician shortage, particularly during COVID-19. By establishing new and modified clinical workflows, RTVS provides a safety net for rural patients and providers challenged by burnout. This case study provides learnings to be implemented to serve future rural, remote, and Indigenous communities in crisis. </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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.308
Teacher spread0.250 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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