Medication Safety Following Electronic Health Record Implementation in Pediatric Intensive Care
Bibliographic record
Abstract
Background: Globally, medication-related safety incidents contribute significantly to patient harm. Patients are at risk of preventable harm from medication use in healthcare, with pediatric care being particularly vulnerable. Electronic Health Records (EHRs) have been implemented to influence medication safety, offering both benefits and challenges for direct care providers and patients. This dissertation investigates the influence of EHR implementation on medication safety in a Pediatric Intensive Care Unit (PICU) at an urban children's hospital in Alberta, Canada, using Safety-II and resilience engineering frameworks. Methods: This manuscript-style dissertation consists of three studies. The first manuscript is a scoping review identifying approaches to studying medication safety following EHR implementation, highlighting gaps in the literature and conceptual design elements for future studies. The second manuscript is a mixed-methods study analyzing voluntary incident reports submitted pre- and post-EHR implementation in the PICU. Quantitative analysis cataloged and compared primary incidents and contributory factors, while thematic analysis identified correlations between socio-technical influences. The third manuscript is a case study realist evaluation, incorporating qualitative socio-technical, Safety-II, and resilience engineering frameworks to explore the experiences and perceptions of PICU nurses, physicians, and pharmacists related to medication use and the EHR. Results: The scoping review revealed conceptual design elements for studying medication safety in EHR contexts. The mixed-methods study found that prescribing incidents were reported significantly more often post-implementation, while administration incidents were reported significantly less. Four qualitative themes emerged from the incident reports: (1) order and instruction (in)accuracy at the user interface; (2) misinformation and decision-making; (3) continuity of care between clinical units; and (4) equipment and supply-related challenges. The case study identified three themes demonstrating how direct care providers adjusted their work to ensure safer outcomes: (1) the evolving roles and responsibilities of physicians, nurses, and pharmacists; (2) staying “on top” of orders; and (3) balancing risk, efficiency, and relationships in patient care. Conclusion: This research highlights the persistent challenges of medication safety post-EHR implementation and the resilient strategies direct care providers employ to mitigate risks. It challenges current structures of power and hierarchy in medication safety and questions the role of standardization as it contributes to safer systems. The findings provide insights to guide future research, system transformation, and quality improvement efforts. Leaders and administrators can use these findings to anticipate potential issues and inform EHR implementation strategies or design their own investigations into medication safety.
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".