The Impact of a New Triage and Booking System on Renal Clinic Wait Times
Bibliographic record
Abstract
Background:Prolonged wait times are known barriers to accessing nephrology care for patients needing more urgent specialist services. Improved process and standardized triage systems are known to minimize wait times of urgent or semi-urgent care in health care disciplines. In Central Zone (CZ) renal clinic, mean wait times for urgent (P1) and semi-urgent (P2) referrals were prolonged before 2014. We also observed prolonged wait times for elective (P3-P5) categories. Improving wait times was identified as an access to care quality improvement focus in CZ renal clinic of the Nova Scotia Health Authority (NSHA).Objectives:To describe our new referral process and new triage system, and to examine their effect on number of referrals wait-listed and mean wait times.Design:A quasi-experimental design was used.Setting:Halifax, Nova Scotia, Canada.Participants:Patients referred to Central Zone Renal Clinic between 2012 and 2018.Measurements:A time series of referral counts and wait times for each triage category were measured before our interventions and after implementing our interventions.Methods:We reviewed our referral processes to identify gaps leading to prolonged wait times. On January 1, 2014, we implemented new administrative procedures: pretriage (standardized referral information form and staff training), triage (standardized clinic intake criteria and new triage guidelines), posttriage (protecting clinic spots for urgent and semi-urgent referrals, wait-list maintenance, and increasing new referral clinic capacity). Data were collected prospectively. Descriptive analysis on mean wait times was done using run charts.Results:A 33% reduction in total number of referrals wait-listed was observed over 4.5 years after intervention. Descriptive analysis of the urgent and semi-urgent categories (P1 and P2) revealed a significant shift of mean wait times on run charts after the interventions. Target wait time was achieved in 94% of P1 category and 78% of P2 category.Limitations:This type of study design does not exclude confounding variables influencing results. We did not explore stakeholder satisfaction or whether the new referral process presented barriers to resending referrals that had insufficient triage data. The long-term sustainability of adding demand-responsive surge clinics and opportunity cost were not assessed. Our referral process and triage system have not been externally validated and may not be applicable in settings without wait-lists or settings that use electronic, telephone or telemedicine consults.Conclusion:Our selective intake of referrals with adequate triage information and referrals needing nephrology consult as defined by our clinic intake criteria reduced number of referrals wait-listed. We saw improved wait times for urgent and semi-urgent referrals with these categories now falling within target wait times for the vast majority of patients. The work of this improvement initiative continues especially for the lower-risk triage categories.Trial registration:Not applicable as this was a Quality improvement initiative.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".