New Technologies and Digital Health Tools in Patients With Solid Tumors and Hematological Malignancies: Cross-Sectional Multicenter Survey Study
Bibliographic record
Abstract
Background: Barriers to eHealth use include lack of technological infrastructure, resistance to change, and inequities in access. However, patterns of access to and use of eHealth tools in people being treated for cancer have not been fully described in the literature. Objective: Our aim was to describe the patterns of access to and use of eHealth tools among outpatients receiving treatment for hematological malignancies and solid tumors. Methods: We conducted a cross-sectional multicenter study using a survey offered to patients aged over 18 years receiving outpatient treatment for hematological malignancies or solid tumors at 4 teaching hospitals in Madrid, Spain, between February 1, 2021, and November 30, 2021. The survey instrument featured questions about patients' demographic and social characteristics, cancer diagnosis, use of information and communication technology (ICT), use and opinions of the Patient Portal, and changes in ICT use during the COVID-19 pandemic. To study the relationship between the different variables, 2-tailed Student t tests or ANOVA were used for variables with normal distribution, and the Mann-Whitney or Kruskal-Wallis tests were used for variables with nonnormal distribution. Statistical analyses were performed using SPSS (version 25; IBM Corp) for Windows. Results: In total, 200 patients were included in our study. Median age was 60 (range 21-87) years. A total of 130 (65%) patients presented with hematological malignancies. Most (n=181, 90.5%) patients considered that eHealth tools might help to improve communication with the medical team during their treatment. Retired participants (28.6% vs 71.4%; P<.001), those older than 60 years (26% vs 74%; P<.001), and those without higher education (2.6% vs 97.4%; P<.001) showed significantly lower rates of internet use, with no observed sex-related differences. A total of 177 (88.5%) patients found the Patient Portal useful, and 140 (70%) reported increased use of ICT due to the COVID-19 pandemic. Conclusions: Most (177/200, 88.5%) patients viewed eHealth tools as useful and believed that it was helpful to improve communication with their care team. However, notable gaps in the use of eHealth were observed in certain groups of patients, with significant differences in use due to age, education, and employment status. Strategies to identify subgroups at risk for unequal access to digital health, as well as to facilitate access and use, are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".