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Estimated Indirect Cost Savings of Using Telehealth Among Nonelderly Patients With Cancer

2023· article· en· W4315435407 on OpenAlexaff
Krupal Patel, Kea Turner, Amir Alishahi Tabriz, Brian D. Gonzalez, Laura B. Oswald, Oliver T. Nguyen, Young‐Rock Hong, Heather Jim, Anthony C. Nichols, Xuefeng Wang, Edmondo Robinson, Cristina Naso, Philippe E. Spiess

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsWestern University
FundersKite PharmaJanssen Scientific AffairsNational Comprehensive Cancer NetworkMoffitt Cancer Center
KeywordsTelehealthMedicineTelemedicineCancerCohortFamily medicineMedical emergencyHealth careEmergency medicineDemographyInternal medicine

Abstract

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Importance: Patients with cancer typically have greater financial hardships and time costs than individuals without cancer. The COVID-19 pandemic has exacerbated this, while posing substantial challenges to delivering cancer care and resulting in important changes in care-delivery models, including the rapid adoption of telehealth. Objective: To estimate patient travel, time, and cost savings associated with telehealth for cancer care delivery. Design, Setting, and Participants: An economic evaluation of cost savings from completed telehealth visits from April 1, 2020, to June 30, 2021, in a single-institution National Cancer Institute-Designated Comprehensive Cancer Center. All patients aged 18 to 65 years who completed telehealth visits within the designated time frame and had a Florida mailing address documented in their electronic medical record were included in the study cohort. Data were analyzed from April 2020 to June 2021. Main Outcomes and Measures: The main outcome was estimated patient cost savings from telehealth, which included 2 components: costs of travel (defined as roundtrip distance saved from car travel) and potential loss of productivity due to the medical visit (defined as loss of income from roundtrip travel plus loss of income from in-person clinic visits). Two different models with a combination of 2 different mileage rates ($0.56 and $0.82 per mile) and census tract-level median hourly wages were used. Results: The study included 25 496 telehealth visits with 11 688 patients. There were 4525 (3795 patients) new or established visits and 20 971 (10 049 patients) follow-up visits. Median (IQR) age was 55.0 (46.0-61.0) years among the telehealth visits, with 15 663 visits (61.4%) by women and 18 360 visits (72.0%) by Hispanic non-White patients. According to cost models, the estimated mean (SD) total cost savings ranged from $147.4 ($120.1) at $0.56/mile to $186.1 ($156.9) at $0.82/mile. For new or established visits, the mean (SD) total cost savings per visit ranged from $176.6 ($136.3) at $0.56/mile to $222.8 ($177.4) at $0.82/mile, and for follow-up visits, the mean (SD) total cost savings per visit was $141.1 ($115.3) at $0.56/mile to $178.1 ($150.9) at $0.82/mile. Conclusions and Relevance: In this economic evaluation, telehealth was associated with savings in patients time and travel costs, which may reduce the financial toxicity of cancer care. Expansion of telehealth oncology services may be an effective strategy to reduce the financial burden among patients with cancer.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.390
Teacher spread0.317 · 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 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

Citations166
Published2023
Admission routes1
Has abstractyes

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