Evolving Toward Shared HIV Care Using the Champlain BASE eConsult Service
Bibliographic record
Abstract
<b>Background.</b> Electronic consultation (eConsultation) is a potential strategy to improve access to specialist expertise and facilitate collaborative care models. The Champlain BASE eConsult service allows for asynchronous communication between primary care providers (PCP) and specialists on a secure, web-based system. HIV experts accessible include HIV physician specialists, HIV pharmacists, and social workers with expertise in HIV. <b>Objective.</b> This study aims to describe the use, value, and utility of this eConsultation service in the care of people living with HIV and to characterize the common question types and clinical topics asked by PCPs. <b>Methods.</b> We analyzed the data from eConsults sent to the HIV specialty group in Ontario’s Champlain Local Health Integration Network between February 2015 and December 2017. Usage data and close-out survey responses were analyzed using descriptive statistics, eConsults were classified using a predefined list of validated taxonomy, and a thematic analysis was performed on the consultation logs to identify common clinical themes. <b>Results.</b> Among the 46 eConsults, the most common question type related to drug treatment (58.7%, <i>n</i> = 27) and management (19.6%, <i>n</i> = 9). The main clinical themes involved the care of significant complexities in people living with HIV, such as comorbidities and drug interactions, and suggestions of coordinated patient care. As well, eConsult was used for advice regarding pre-exposure prophylaxis for HIV-negative patients at risk of HIV infection. PCPs highly valued the eConsult service (average rating 4.8/5). <b>Conclusion.</b> Overall, this study demonstrates that eConsult provides an efficient and valuable service to PCPs caring for patients living with or at risk for HIV by improving access to HIV specialists and facilitating the delivery of team-based comprehensive care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".