Evaluating the Connections Between Primary Care Practice and Clinical Laboratory Testing: A Review of the Literature and Call for Laboratory Involvement in the Solutions
Bibliographic record
Abstract
CONTEXT: Growing evidence has demonstrated a high frequency of quality gaps in laboratory medicine, with recent studies estimating that 15% to 54% of primary care medical errors reported by primary care physicians and staff are related to the testing process. However, there is lack of evidence-based performance metrics in the preanalytic and postanalytic phases of the testing pathway for primary care practices. OBJECTIVE: To use results of the literature review to assist in the development of quality indicators that could improve preanalytic and postanalytic processes in primary care-based laboratory medicine. DATA SOURCES: Literature in Ovid/MEDLINE from 2001 through 2011 was searched as a primary source of information. Ninety-five peer-reviewed and non-peer-reviewed publications were retrieved following title and abstract review and 10 articles were reviewed in their entirety by the authors. A systematic review of the literature was conducted regarding the connections between clinical laboratories and primary care offices and the resulting errors. Root causes of errors were categorized into 7 major themes: process failures, delays, communication gaps, errors in judgment and cognition, influence of minorities/language, practice culture, and lack of patient centeredness. Selected articles were evaluated for evidence quality using the Systematic Evidence Review and Evaluation Methods for Quality Improvement grading scale developed by the Centers for Disease Control and Prevention. CONCLUSIONS: The focused literature review documented 7 key error themes in the laboratory medicine/primary care testing process. Performance metrics related to these themes are proposed that deserve future study for evidence-based improvement.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".