Point-of-Care Testing: A Process to Engage Multidiscipline Health Care Providers in Role Definition
Bibliographic record
Abstract
Point-of-care testing (POCT), laboratory tests performed near the patient, is pushing the boundaries of our traditional notions of scope of practice. The demand for more rapid results; the availability of technologically advanced, easy-to-use POCT devices; and laboratory staff shortages are shifting test performance away from the laboratory to POCT performed by direct health care providers. Whereas testing conducted in the laboratory is performed by qualified medical laboratory technologists and is highly regulated, POCT is often performed by nonlaboratory operators with limited training and minimal knowledge of good laboratory practice. Our objective: collaboratively validate and implement a process and tools to clarify POCT roles and responsibilities. We identified several prerequisites to success, including clear definition of POCT roles, management of the associated responsibilities, and a process to engage the direct health care providers performing POCT. Strategies used included obtaining support from the organization’s quality council and professional practice groups; recruiting stakeholders from acute, rural, and community settings; forming a POCT working group to collaborate and make recommendations; and piloting the recommendations before full implementation. Working from a generic template, members of the group developed a POCT process map outlining several streams of activities, defining the roles and responsibilities required to deliver quality POCT results. The streams included the POCT testing process, quality assurance, training and competency, service change activity, and information connectivity. The process has been successfully piloted and is currently being implemented across the region. If all supporting prerequisites for success are met, then transitioning the processes should assure sustainable quality practice.
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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 | 0.225 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".