Improving Genetics Clinic Efficiency and Capacity Using Design and Human Factors Methods
Bibliographic record
Abstract
This quality improvement study focused on developing an understanding of factors influencing the ability of prenatal genetics counsellors (GCs) to efficiently see patients during the COVID-19 pandemic and strategies to improve their workflow using techniques from human factors and design. The demand for Prenatal Genetics Clinics is rising which has increased pressure on GCs to become more efficient. Genetics counsellors can improve access to their services by reducing the time spent on the tasks performed before and after a genetic counselling encounter, thereby increasing the number of patients they see. We were limited to certain methods to understand the differences in workflow before and during the COVID-19 pandemic. This study involved a literature review, archival analysis of workflow studies conducted before the pandemic, stakeholder meetings and mapping, a brainstorming session, as well as documenting time-on-task in a diary and naturalistic observation sessions. A task analysis was developed to identify factors influencing efficiency related to the design of the space, processes and the use of artefacts. Virtual and on-site workflows show that GCs spend at least half of their time on tasks before and/or after the patient’s appointment. Looking at potential inefficiencies or bottlenecks in workflow formed the development of a strategic plan for improving GC workflows at the prenatal Genetics Clinic. Improvements suggested through this analysis were constrained to support the current number of healthcare providers working within the existing space configuration.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".