Nursing strategies to improve alert closure for remote symptom monitoring.
Bibliographic record
Abstract
421 Background: For successful remote symptom monitoring using patient-reported outcomes, nurses should respond to alerts in a timely fashion. Where clinical trials utilized research staff for alert management, the shift to standard-of-care delivery necessitates that this responsibility be added as a task to an already strained nursing workforce. Little is known about strategies to engage nurses to improve timeliness of alert management. Methods: In this quality improvement initiative, we aimed to improve timeliness of alert closures generated by moderate or severe symptoms within a remote symptom monitoring program. Optimal closure was defined as < 48 hours, which was consistent with institutional requirements for response to patient phone calls. A continuous quality improvement approach, with multiple Plan Do Study Act (PDSA) cycles was conducted. Data was captured from the electronic medical record and PRO platform (Carevive). Descriptive statistics included frequencies and percentages. The proportion of alerts closed each month < 48 hours, 48-72 hours, 3-7 days, and > 7 days were reported overall and by disease team (i.e., major cancer types). Surveys not closed were considered > 7 days. The timing of strategies to improve nursing engagement were documented and evaluated for impact on alert closure. Results: From June 1, 2021-May 31, 2022, 1121 moderate or severe alerts were generated from 234 patients. Disease teams had variable remote symptom monitoring start dates: breast, leukemia, and limited gynecologic (prior to 6/2021); myeloma and gastrointestinal (7/2021); genitourinary (10/2021); head and neck (12/2021); melanoma (2/2022); and Lymphoma (4/2022). In 6/2021, the overall alert closure at < 48 hours, 48-72 hours, 3-7 days, and > 7 days was 57%, 4%, 14%, and 25% respectively (n = 28). To improve alert closures, several key strategies were deployed to improve alert closure times including disease-specific reporting and meetings with nursing leadership (10/2021); identification of a nurse champion, creation of “cheat sheets” to remind nurses how to close alerts, and individualized calls with nurses with open alerts (1/2022), and inclusions of requirement to close alerts in nursing newsletters (2/2022). Overall, alert closure less than 48 hours improved to 61% by 12/2021 (n = 97) and to 69% by 5/2022 (n = 167). Disease group alert closure varied, with higher closure more commonly in teams with greater duration of use, such as breast cancer team with an alert closure of 85% < 48 hours in May 2022. Conclusions: Key nursing engagement strategies improve alert closure for remote symptom monitoring programs implemented in real-world settings.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".