A Quality Improvement Project to Reduce Inappropriate Telemetry Utilization in Nephrology Inpatients at an Academic Hospital
Bibliographic record
Abstract
Background: Cardiac telemetry plays a key role in diagnosing and monitoring arrhythmias in hospitalized patients. The American Heart Association (AHA) provides recommendations on the use of telemetry outside the intensive care unit (ICU). These can be stratified into three categories; telemetry is indicated (Class I), telemetry may provide benefit (Class II) or telemetry is unlikely to be of benefit or may cause harm (Class III). The AHA and Choosing Wisely Canada suggest that telemetry use should be guideline-based and should not be used outside the ICU without a plan for discontinuation. In the United States, interventions that modify the Electronic Medical Record (EMR) have been shown to improve telemetry utilization. Patients admitted to our nephrology ward are often prescribed telemetry inappropriately, which impacts patients and providers, and increases healthcare costs. Methods: We used the Model for Improvement framework to conduct a quality improvement project with the aim of reducing inappropriate telemetry utilization (ordered for a Class III indication). We employed an interrupted time series design to evaluate telemetry utilization from January 2018 to September 2019 (pre- intervention period, which was retrospective) and September 2019 to September 2020 (postintervention period, which was prospective). We implemented a modification to our electronic health record (EMR) that forced prescribers to choose an appropriate telemetry indication. Results: There was a reduction of Class III telemetry utilization from 56 to 22%. This reduction was sustained for 12 months following implementation. We piloted a nursing-led discontinuation protocol which resulted in 35% of telemetry orders being discontinued prior to the 48-hour prescribed period. Interpretation: Our study shows that interventions to enhance the EMR in a way that supports better utilization of telemetry can be successful at Canadian institutions. Our next steps will be to implement a permanent nursing-led discontinuation protocol to reduce the duration of telemetry utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".