Optimizing use of an electronic medical record system for quality improvement initiatives in hemodialysis: Review of a single center experience
Bibliographic record
Abstract
INTRODUCTION: The complexity of managing patients with end-stage kidney disease on hemodialysis underscores the importance of implementing quality improvement (QI) initiatives to enhance patient safety and prioritize patient-centered care. To address this, we established a QI committee at our tertiary academic center focusing on evidence-based practices, patient-centered approaches, and cost efficiency. To facilitate the seamless implementation of QI initiatives, we leveraged the capabilities of our electronic medical record (EMR) system. METHODS: This review details effective strategies for optimizing use of an EMR system to successfully implement QI efforts. Drawing from our experience, we provide detailed descriptions and practical insights that can be applied to other EMRs. FINDINGS: The creation of a secure and accessible dashboard, offering real-time data on quality metrics, stands out as the most notable feature. This dashboard operates through an algorithm that merges data from both our dialysis and hospital EMR systems. Its primary objectives are to streamline the identification of high-priority patients, enhance team communication, and facilitate tracking of quality indicators. Additionally, we integrated clinical pathways, checklists, and standardized protocols into the renal EMR to ensure smooth implementation of QI interventions. Notable examples of these interventions include an incremental hemodialysis protocol, a new hemodialysis start checklist, vaccination care plans, and personalized kidney transplant workups. Programmed electronic automatic reminders have proven invaluable in ensuring timely follow-ups of assigned tasks. The EMR has also contributed to medication optimization and deprescribing by generating patient lists based on specific medication classes. Finally, the EMR's capability to swiftly generate lists of patients with specific features has significantly facilitated targeted QI interventions. CONCLUSIONS: Leveraging the capabilities of an EMR system can be crucial for enhancing care of hemodialysis patients and implementing effective QI initiatives.
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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 | 0.065 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".