41 Innovative solutions to support “Virtual First” pediatric endocrine care
Bibliographic record
Abstract
Abstract Primary Subject area Endocrinology and Metabolism Background With the onset of the SARS-CoV-2 pandemic, health care providers everywhere were forced to rapidly shift the way they deliver care. Within our community-based academic organization, there was variability in response to required changes among different clinical areas, with many clinics ramping down their services while they restructured. In our pediatric endocrinology clinic, we had built the infrastructure to support virtual care using a provincial platform as part of a pilot program for our diabetes population in the year preceding the pandemic. This experience set the stage for a swift pivot to virtual care. To ensure ongoing high quality consultation and follow-up services during the pandemic, our clinic required rapid restructuring to successfully and immediately shift completely to a sustainable “virtual first” approach in March 2020. Objectives In the months following the onset of the SARS-CoV-2 pandemic, we sought to quickly develop and implement innovative strategies, using a quality improvement framework, to supplement virtual care and maintain high quality care delivery. Design/Methods As soon as physical distancing measures were implemented in March 2020, our multidisciplinary team held daily 30-minute meetings to troubleshoot, brainstorm, and strategize potential adaptations in care delivery to ensure we continued to meet patient and family needs with primarily virtual care. Barriers and problems were presented and prioritized, solutions proposed, then implemented with support of operation and e-health teams. Attention to educational needs for medical students, residents and fellows helped shape solutions. Results The following innovative solutions were successfully implemented within three months: • a drive thru hemoglobin A1C clinic for patients with diabetes • a streamlined “low touch” Auxology Clinic to supplement virtual visits when body measurement, vital signs or physical exam assessment were required • pre-visit preparation instructions for patients and families • active promotion of patient portal enrolment • re-design of follow-up orders content to allow providers to accurately indicate suitability of virtual care alone or with support measures • a workflow to allow quick conversion from in-person to virtual visits to prevent cancellations related to isolation requirements • an educational framework to ensure level-appropriate exposure to and involvement in patient care for trainees • auto-faxing of medication and supplies • printer mapping and workflow for external lab requisitions • provider/staff scheduling and role re-assignment to facilitate minimal number of on-site staff • support of the team to adopt best practices for virtual visits Conclusion While virtual care delivery existed before the pandemic, it was rarely used outside of pilot projects, or only from necessity, when travel to a health care facility was not possible. Herein we provide an overview of an innovative, primarily virtual, care delivery model to satisfy patient and family needs in a pediatric endocrinology clinic in an academic centre. Many components of our model have (and can be) applied or adapted to support care delivery in other clinical areas. The people, processes, and digital health adaptations required to support a primarily virtual mode of care were critical to its success.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".