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Identification of best practices for training in remote symptom monitoring using electronic patient-reported outcomes.

2023· article· en· W4388201913 on OpenAlexaff
D’Ambra Dent, Stacey A. Ingram, Megan Patterson, Keyonsis Hildreth, Jennifer Young Pierce, Chelsea McGowen, Chao‐Hui Huang, J. Nicholas Dionne‐Odom, Ethan Basch, Angela M. Stover, Doris Howell, Justin D. Smith, Bryan J. Weiner, Gabrielle B. Rocque

Bibliographic record

VenueJCO Oncology Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Institutes of Health
KeywordsPDCAChampionBest practiceMedicineScale (ratio)Session (web analytics)Medical educationQuality managementNursingMultidisciplinary approachTraining (meteorology)Computer scienceOperations managementEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.188
GPT teacher head0.516
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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