Challenges of Conducting Point-Of-Care Testing Operations In The Municipal Public Health System Based Ambulatory Care Clinics In New York City
Bibliographic record
Abstract
Introduction: Our New York City Municipal Public Health System-based multisite ambulatory clinics that offer various waived POCT (point of care tests) and provider-performed microscopy (PPM) to our communities, ensure standardization and quality of POC testing across our health system.Our laboratory service conducts system-wide centralized implementation, monitoring and oversight of the POCT operations regarding regulatory compliance, test performance, quality control and training.With our day-to-day POCT operations, like all other clinical laboratories, our ambulatory care clinics encounter various hurdles and challenges.Here we elaborated on the issues that we encounter and how we manage to overcome them.Method: We categorized the challenges that affect our managerial as well as field level laboratory operations and have devised ways to deal with those POCT operational issues.Result: Among the staffing issues, rapid staff turnover causes significant delays or cessation of POCT operations in our ambulatory care clinics, due to orientation, reeducation and ensuring competency to the staff, that conducts POCT in our clinics.Besides, supply shortages, staff shortages and absences due to illness and overwork are the issues, noted at the laboratory field operational level.Delays in the processing of paperwork and new staff recruitments and in the laboratory supply chains are notable management issues.Conclusion: Even though the vastness and complexity of our multisite ambulatory care network sometimes affect our ambulatory care clinic POCT operation in a very challenging way, our timely, planned and coordinated intervention, close communication and initiatives handle those issues very effectively to ensure the quality of POC testing for the patient safety and care across our health system.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".