Prioritizing Access to the National Diabetes Center NDC Services Based on Clinical Need
Bibliographic record
Abstract
NDC is one of the busiest services at Hamad General Hospital (HGH) with 450-1000 new referrals monthly and an average waiting time of 84 days for an initial appointment. Triage guidelines from several government institutions in Canada, UK, and Australia stated that the patients with urgent conditions should be assessed within 2 weeks. Delaying urgent cases management poses a great patient safety risk that may endanger patient health and can lead to increased healthcare cost. In December 2016, a multidisciplinary team (physician, nurses, administration, and quality staff) was formed to pilot an urgent clinic service. The team standardized triaging criteria, calculated waste, and improved the process from triaging until the first appointment at NDC. Goal: To provide timely diagnosis and management for newly-referred patients to NDC who are considered urgent after physician's triage. Team Aim: To improve the percentage of referred patients with urgent cases who attended their initial consultation visit at the Diabetes-Endocrine Urgent Clinic within 2 weeks from physician's triage to 60% by May 2017. Methods: An urgent clinic exclusively for indicated urgent new referrals was piloted last December 2016. Fishbone diagram and process map were created. For testing changes, the Model for Improvement or Plan-Do-Study-Act (PDSA) was utilized. Several trials were done to identify the cases to be considered urgent during triage, manpower, clinic scheduling, appointment booking process, feedback gathering, and data collection. Results:For the past 9 months, there were 769 patients triaged to the urgent clinic. Around 65% of them were seen within 2 weeks, 12% no-show, and 23% were unable to attend due to various causes. We were able to surpass our goal for several months starting from January 2017 with the exemption in June 2017 when the clinic was closed for 10 days due to the Eid holidays.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".