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Record W4403730732 · doi:10.2196/60585

Oncology Clinicians' Perspectives of a Remote Patient Monitoring Program: Multi-Modal Case Study Approach

2024· article· en· W4403730732 on OpenAlexvenueno aff
Ann M. Mazzella Ebstein, Robert Michael Daly, Jennie Huang, Camila Bernal, Clare Wilhelm, Katherine S. Panageas, Jessie C. Holland, Rori Salvaggio, Jill Ackerman, Jennifer R. Cracchiolo, Gilad J. Kuperman, Jun J. Mao, Aaron Begue, Margaret Barton‐Burke

Bibliographic record

VenueJMIR Human Factors · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsPreprintModalMedicineMedical physicsOncologyInternal medicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Remote patient monitoring (RPM) aims to improve patient access to care and communication with clinical providers. Overall, understanding the usability of RPM applications and their influence on clinical care workflows is limited from the perspectives of clinician end users at a cancer center in the Northeast, United States. OBJECTIVE: Explore the usability and functionality of RPM and elicit the perceptions and experiences of oncology clinicians using RPM for oncology patients after hospital discharge. METHODS: The sample included 30 of 98 clinicians (31% response rate) managing at least five patients in the RPM program and responding to the m-Health Usability between March 2021- October 2021. Overall, clinicians responded positively to the survey. Item responses with the highest proportion of disagreement were explored further. A nested sample of five clinicians who responded to the study survey (30% response rate) participated in interview sessions conducted from November 2021 to February 2022, and averaged 60 minutes each. RESULTS: Survey responses highlighted that RPM was easy to use and learn and verified symptom alerts during follow-up phone calls. Areas to improve identified practice changes from reporting RPM alerts through digital portals and its influence on clinicians' workload burden. Interview sessions revealed three main themes: clinician understanding and usability constraints, patient constraints, and suggestions for improving the program. Subthemes for each theme were explored, characterizing technical and functional limitations that could be addressed to enhance efficiency, workflow, and user experience. CONCLUSIONS: Clinicians support the value of RPM for improving symptom management and engaging with providers. Functional changes to enhance the program's utility, such as input from patients about temporal changes in their symptoms and technical resources for home monitoring devices.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0100.003
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.187
GPT teacher head0.561
Teacher spread0.374 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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