Human Factors Perspectives on a Systemic Approach to Ensuring a Safer Medication Delivery Process
Bibliographic record
Abstract
The current, prevailing approach to addressing medication delivery safety issues has been to apply solutions at the point of failure with direct, local remediation. These include computerized physician order entry to address transcription and prescribing problems, tall man lettering for label clarity and smart pump systems to address programming use errors. We discuss the lack of a systemic, holistic approach to addressing medication delivery issues that has led to fragmented solutions that do not address the problem as intended and introduce new, unintended patient safety issues. We use recent case studies in addition to our own experimental data from human factors investigations to show how a comprehensive human factors approach can be applied to address systemic error in medication delivery. Only by identifying how (1) subsystems interconnect, (2) information flows, (3) care providers communicate and (4) users are impacted will healthcare organizations and system vendors be able to fully address error in medication delivery. Much of what is required from organizations is to transcend the organizational boundaries of medicine, pharmacy and nursing to produce a delivery system that ensures an integrated approach that addresses all stakeholders' needs.
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 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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".