How to integrate vital interdisciplinary information in a non-closed-loop system?
Bibliographic record
Abstract
314 Background: History of hypersensitivity reactions (HSR) must be readily accessible to ensure patient safety while receiving chemotherapy. While documentation of HSR is a routine process, gaps are found in maintaining vital information amongst various stand-alone systems (SAS). Centralized documentation (CD) by frontline nursing staff in the Electronic Patient Record is key to reduce risk. Pharmacists refer to CD for HSR information when processing chemotherapy. We developed and evaluated a technology-based workaround approach as a possible solution to a non-closed-loop system. Methods: An e-clinical point of care documentation tool was designed and built for nursing data collection of HSR details within the Electronic Patient Record. Vital parameters necessary for “complete” HSR documentation were outlined in a cue card, including time of reaction, reaction drug, volume and rate of infusion, management, vital signs and objective symptoms, re-challenge or discontinuation, and patient outcome. This cue card standardizes e-documentation. Pre- and post-system rollout survey was used to gauge effectiveness of CD by nursing and pharmacists. Results: Over a 6-month period, there were 173 HSRs in 11,754 patient visits (1.5%), with variability in collection of vital documentation in relation to roll out of the e-tool. In the pre-CD rollout phase (May 25 to September 12, 2011) 108 HSRs were identified that were documented in multiple locations including an electronic prescribing SAS (86%) and the chemotherapy nursing paper record (77%). In the post-CD phase (April 23 to June 23, 2012) 65 HSRs were identified, 46% of which were documented in CD, 72% in the electronic prescribing SAS, and 85% in paper record. Six of 7 pharmacists (85%) and 7 of 10 nurses (70%) who were surveyed indicated that the new CD documentation process was “effective” or “significantly effective”. Conclusions: As non-fully integrated systems exist in current environments, technology workarounds should be used to “close the loop”. E-Clinical documentation is an upcoming centralized technology solution, which we have used with promising initial adoption results. Formal e-documentation tools can lead to more detailed documentation than current processes.
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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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".