Identification of best practices for training in remote symptom monitoring using electronic patient-reported outcomes.
Bibliographic record
Abstract
441 Background: As remote symptom monitoring (RSM) using electronic patient-reported outcomes (ePROs) is increasingly implemented as part of standard-of-care, practices must be prepared to train diverse clinical teams. Little is known about best practices for training multidisciplinary teams to engage effectively with ePROs. Methods: This quality improvement initiative evaluated a training approach for RSM using Plan Do Study Act (PDSA) cycles conducted in oncology practices at the University of Alabama at Birmingham (UAB). Multiple team members participated in training sessions over the duration of implementation scale-up. Training logs, field notes on barriers, and iterations to the training approach were updated using Excel spreadsheets. A recorded training session was utilized as an update to the training approach conducted via Zoom. Results: Overall, 145 providers (nurses, social workers, navigators, clinicians) were trained. Initial training (PDSA Cycle I) included a Zoom lecture for lay navigators, nurses, and physicians conducted by the physician lead, which included rationale for RSM, provider roles, and technical instruction. Barriers identified included limited knowledge retention and difficulty using ePRO technology in practice. In PDSA Cycle II, training included advanced practice providers. In addition, a nurse champion was added to the training team. Content was split into a lecture for rationale and roles (Zoom or in person, based on provider preference) and one on one hands on in-clinic training on technical aspects of ePRO delivery led by the training manager and nurse champion. While this approach increased engagement, provider turnover necessitating multiple trainings and low knowledge sustainment were barriers. In PDSA Cycle III, additional staff were trained including the intake team, nurse navigators, and social workers. The lecture was recorded for delivery by the training manager or independent viewing. In addition to the initial training, written standard operating procedures for providers, and check-ins or repeat sessions with participants were added for longitudinal engagement. Conclusions: Four key provider training elements were: (1) training more provider types to support the use of ePROs in clinical care delivery; (2) emphasizing in-person technical training by an individual with relevant experience; (3) using asynchronous materials to support both scalability and ongoing support; and (4) including additional sessions with longitudinal one-on-one provider training.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".