Remote symptom monitoring alerts for nurses: Removing the noise.
Bibliographic record
Abstract
379 Background: Within remote symptom monitoring (RSM) programs, nurses may respond to many symptom alerts in a given day. The shift to standard-of-care delivery necessitates adding this responsibility to an already strained nursing workforce. Thus, selecting the right symptoms to alert is critical and requires care to minimize non-actionable alerts. Little is known about strategies to reduce alert burden on nursing. Methods: In this quality improvement initiative, we aimed to improve the nurse’s perception of alert utility and minimize “noise” or alerts that were not actionable. A continuous quality improvement approach, with multiple Plan Do Study Act (PDSA) cycles, was conducted based on nursing feedback. Modifications were captured, described, and categorized. Alert details prior to and after changes are described. Descriptive statistics were calculated using frequencies and percentages for categorical variables. Results: In PDSA cycle 1, we allowed nurses to set an expected level for specific symptoms to “snooze” alerts for up to 1 month in June 2021. Snoozed alerts did not trigger to nurses. Overall, 5.8% (405/7029) of symptom alerts were snoozed from late June 2021-May 2023 (Alert Redundancy Change). In PDSA cycle 2, an option “I don’t want a call back” was added for patients in late January 2022; 42.8% (2394/5595) of subsequent symptom alert surveys requested no call back from the nurse (Survey Response Threshold Change). In PDSA cycle 3, nurses reported that “insomnia” was not actionable weekly. This was encountered in 7.2% (170/2368) of surveys prior to removal at the end of June 2022. “Insomnia” was replaced with “rash”, which generated alerts in 6.5% (295/4549) of surveys (Survey Content Change). In PDSA cycle 4, nurses identified that hospitalized patients generated alerts that were not appropriate for outpatient action. The system was modified to add a banner bar highlighting to the nurse that the patient was hospitalized and alert could be closed with a response of “patient hospitalized” late February 2023. Following implementation, a total of 9.1% (35/384) enrolled patients either self-selected or a navigator marked them as hospitalized and therefore their surveys were paused and/or the nurse was able to close the alert selecting “patient hospitalized”. (Survey Location Change). With these changes, there were 9.9% (97/975) surveys in May 2023 with at least one actionable alert. Conclusions: Modifications to alert systems can reduce the number of non-actionable alerts that nursing must address in real-world settings, thus minimizing burden on staff.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".