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Record W7117530860 · doi:10.2196/73058

Patient and Clinician Perspectives on Expanding Telehealth Use for Older Adults Across the Cancer Control Continuum: Mixed Methods Study

2025· article· en· W7117530860 on OpenAlexvenueno aff
Robin T. Higashi, Bella Etingen, Jessica Lee, Suzanne Cole, John C. Mansour, Alice Zhao, Timothy P. Hogan

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthCancerTelemedicineControl (management)Service (business)MEDLINECancer prevention

Abstract

fetched live from OpenAlex

Background: Reliance on telehealth increased dramatically during the COVID-19 pandemic, introducing new opportunities to consider the use of telehealth across the cancer control continuum. However, patient, clinician, and staff perspectives about the types of cancer care appointments that are considered appropriate and the clinical care needs to support expanded remote care services are limited. Understanding older adults' diverse technology needs and perspectives is especially important given that they comprise a large and growing proportion of patients with cancer. Objective: This study aimed to describe the perceptions and experiences of older patients with cancer and their clinical care team members regarding the expansion of telehealth use across the cancer control continuum and to solicit suggestions about how to support telehealth use for cancer care delivery. Methods: Using a convergent mixed methods design, we surveyed and interviewed patients aged ≥60 years, clinicians, and staff at a comprehensive cancer center in the southern United States between December 2020 and November 2021. Interview questions were rooted in the sociotechnical model, which proposes 8 interrelated dimensions representing factors influencing the design, use, and outcomes associated with health information technologies. Patient survey domains included telehealth experience and satisfaction and factors affecting telehealth perceptions and use; clinician survey domains included contexts of telehealth appropriateness, training, and barriers and facilitators to telehealth service provision. Survey data were analyzed using descriptive statistics. Qualitative data were thematically analyzed using a combined deductive and inductive approach. Results: We received completed surveys from 128 patients (567 invited) and 106 clinicians and staff (146 invited). We completed 14 patient (29 invited) and 20 clinician and staff (22 invited) interviews. Across all participants, most agreed or strongly agreed that multiple cancer care appointment types should be offered via telehealth, including discussing treatment side effects (75/102, 73.5% of patients and 66/94, 70.2% of clinicians and staff), results communication (71/102, 69.6% of patients and 65/94, 69.1% of clinicians and staff), and treatment follow-up (67/102, 65.7% of patients and 52/93, 55.9% of clinicians and staff). In interviews, participants elaborated on factors influencing the appropriateness of telehealth versus in-person appointments, including symptom severity, type of cancer, and purpose of the appointment. Many patient and staff suggestions focused on ways to address digital literacy gaps, while clinicians recommended improving clinic workflows, infrastructure, and training. Conclusions: Overall, clinicians, staff, and older patients with cancer all responded positively toward expanding telehealth use across multiple cancer and appointment types across the cancer control continuum. Older adults with cancer are generally interested in telehealth for cancer care, especially if strategies to address digital literacy gaps are incorporated. Clinicians and staff members expressed specialized training and infrastructure needs to optimize telehealth uptake and service delivery.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.510
Teacher spread0.470 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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