Improving the Efficiency and Capacity of Virtual Genetics Clinics using Human Factors Design Methods
Bibliographic record
Abstract
The COVID-19 pandemic brought with it significant challenges for healthcare as well as some opportunities.The Prenatal Genetics Clinic at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa moved to virtual care at the outset of the pandemic.Herein, we conducted a quality improvement study focused on developing a better understanding of factors influencing the efficiency of prenatal Genetics Counsellors (GCs) to support virtual care and related patient activities, with the intent of developing strategies to improve workflow.Within the context of the pandemic, we used remote and field methods of quantitative and qualitative inquiry to develop a better understanding of clinical workflows after the implementation of virtual care.This included diary studies to track time on task and naturalistic observation sessions at the Clinic.The Lean Improvement Approach was used to assess barriers to efficient workflows and develop recommendations for improvement.The analysis of GC's virtual and on-site workflows has increased our understanding of the work GCs perform, allowed us to define the challenges with virtual care experienced by GCs, and evaluate the existing processes to help identify systemic design problems and opportunities for improvement.The strategies suggested from this analysis fall within real-world project constraints, which included the inability to increase the number of GCs and inability to renovate the current clinic space.Our results show that GCs spend at least half of their time on patient-related tasks before and/or after the patient's appointment.Therefore, looking at potential inefficiencies or bottlenecks during these phases of work may help improve their overall workflow and flow through of patients.The results and recommendations that emerged were captured in a framework that identifies 3 key areas for strategic improvement measures -digital accommodation, administration support, and clinical assistance.iii Recommendations from the administrative and clinical assistance groups have already been implemented in the form of a new scheduling strategy and a new support staff role for clinical assistance.The remaining recommendations will support the on-going development of a strategic plan for improving work efficiency across the entire patient service unit of the Department of Genetics.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".