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Record W4317542625 · doi:10.2196/39289

Provider and Older Patient Responses to Rapid Expansion of Telehealth in an Urban Cancer Center: Mixed Methods Critical Incident Evaluation

2023· article· en· W4317542625 on OpenAlexvenueno aff
Robin T. Higashi, Bella Etingen, Suzanne Cole, John C. Mansour, Jessica L. Lee, Timothy P. Hogan

Bibliographic record

VenueIproceedings · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthNursingWorkflowMedicineThematic analysisTelemedicineDescriptive statisticsPandemicData collectionStakeholderFamily medicineMedical emergencyMedical educationQualitative researchHealth careCoronavirus disease 2019 (COVID-19)Public relationsPolitical scienceSociology

Abstract

fetched live from OpenAlex

Background Synchronous video visits (“telehealth”) were rapidly adopted by many cancer centers across the nation to facilitate provision of care during the COVID-19 pandemic; however, in many cases, there was little time to comprehensively assess patient and provider needs related to this rollout. In addition, attitudes toward telehealth use among older patients with cancer, who may face increased vulnerability to inequities in access to care due to limited digital literacy, were largely unknown at that time. Objective The objectives of this concurrent mixed methods study were to (1) assess stakeholder experiences with telehealth since its rollout during the COVID-19 pandemic at an urban comprehensive cancer center and (2) solicit suggestions to optimize workflow and enhance telehealth implementation beyond the pandemic. Methods We conducted surveys and critical incident interviews with providers, staff, and older patients (aged ≥60 years) from a comprehensive cancer center in a large urban area. Data collection occurred from December 2020 to November 2021. We analyzed survey data using descriptive statistics and qualitative data using deductive and inductive thematic content analysis facilitated by NVivo 12.0 (QSR Australia). Results We completed a total of 106 provider or staff surveys, 128 patient surveys, 20 provider or staff interviews, and 14 patient interviews. While the majority (70.7%) of surveyed providers and staff agreed or strongly agreed that the technology used to support telehealth visits at Simmons fit well within their clinical workflow, several suggestions were offered to enhance telehealth implementation, including conducting proactive, systematic training and technical assistance; making appointment scheduling and rooms flexible for in-person or telehealth conversion in real time to streamline workflow; expanding availability of telehealth to supportive care services and physically frail patients; and increasing provider engagement via telehealth meetings and conferences. Less than a third (30.8%) of providers or staff agreed or strongly agreed that the institution did a good job of preparing patients for their first telehealth encounter, and patients reported experiencing challenges with joining video visits (29%) and understanding the telehealth process (28%). Participants suggested several strategies to assist patients with limited digital literacy, including offering video tutorials of the connection process, creating “fake appointments” to practice web-based connections, and hiring a digital navigator to assist with technical difficulties and setup of the web-based portal. Despite challenges, a majority of surveyed patients (65.7%) and providers or staff (76.9%) intend to continue using telehealth after the COVID-19 pandemic passes. Conclusions Use of telehealth for cancer care was received positively by older patients and providers or staff. Taking targeted steps to support enhanced implementation post pandemic could reduce barriers to care, including among older adults and other populations with limited digital literacy, thereby promoting greater equity of access to telehealth and the potential benefits it offers.

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)0.004
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 score0.560
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.120
GPT teacher head0.513
Teacher spread0.393 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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