Accuracy of Adverse Drug Reaction Documentation upon Implementation of an Ambulatory Electronic Health Record System
Bibliographic record
Abstract
BACKGROUND: Detection, monitoring and treatment of adverse drug reactions (ADRs) are paramount to patient safety. The use of a comprehensive electronic health record (EHR) system has the potential to address inadequacies in ADR documentation and to facilitate ADR reporting to health agencies. However, effective methods to maintain the quality of documented ADRs within an EHR have not been well studied. OBJECTIVE: To evaluate the accuracy and effectiveness of ADR documentation transfer throughout the implementation of a comprehensive EHR system. METHODS: Retrospective analysis of ADR documentation at a tertiary care pediatric hospital between January 2013 and June 2014. ADRs documented in the newly implemented ambulatory EHR, pharmacy system and hybrid health record system were extracted. Documentation inconsistencies and processes for managing ADR documentation within the EHR were reviewed. RESULTS: A total of 115 patients with 260 unique ADRs were identified. Only 155 (60 %) of the identified ADRs were found in the ambulatory EHR system. The remaining 105 ADRs (40 %) were missing from the EHR when it was compared with the other systems. Seventy-two patients (63 %) returned for a follow-up visit, and each had their ADR documentation reviewed in the ambulatory EHR. Following the visit, 44 % of these ambulatory EHR records still included incorrect information. CONCLUSIONS: We identified discrepancies in ADR documentation within hospital systems, which need to be addressed as healthcare institutions transition to EHRs. Processes related to the transfer of ADR information into the EHR should be clearly defined. To improve the quality of ADR documentation, steps to force complete and continual ADR verification should be introduced at early stages of implementation of a new EHR, and all responsible providers should play a role.
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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 | 0.034 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".