Quality improvement for parenteral nutrition in hospital: Applying a gap analysis to an electronic health record to review parenteral nutrition processing
Bibliographic record
Abstract
BACKGROUND: In light of the complex and high-risk nature of parenteral nutrition (PN), reviewing PN processing steps is essential to minimize patient harm. The main steps include ordering, verification, compounding, and administration. Electronic health records (EHRs) have become increasingly utilized and can play a critical role in enhancing the safety of PN processin. Epic EHR is used throughout all PN processing steps within our health system. There is limited literature on health system quality improvement initiatives in PN processing. METHODS: We reviewed the steps of PN processing in our health region and applied a gap analysis to assess Epic's functionality in PN processing. This gap analysis aimed to identify opportunities to enhance PN safety. RESULTS: Epic applies 32 of 40 functions that enhance PN safety. We selected three functions to prioritize adding into future EHR iterations; these include (1) bidirectional automatic interfacing between the automated compounding device and EHR reflecting real-time updates on product availability/shortages, (2) automatically transmitting a pharmacist-modified PN order back to the prescriber for approval, and (3) adding additional clinical decision support tools, one of which is incorporating a 3-in-1 qualification calculator and the second is requiring prescriber justification for using compounded formulations over multichamber bags. Additional opportunities for improving safety in PN processing were identified and added to the gap analysis. CONCLUSION: Using a gap analysis is a simple process to review a health system's EHR to identify opportunities to enhance patient care.
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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 | 0.099 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".