eHealth in Modern Patient-Caregiver Communication: High Rate of Acceptance Among Physicians for Additional Support of Breast Cancer Patients During Long-Term Therapy
Bibliographic record
Abstract
BACKGROUND: Lack of adherence and compliance with drug regimens among breast cancer patients represent substantial problems in oral therapies, leading to significant impacts on mortality. Where other systems have failed, electronic health (eHealth) could be a possible solution to improve medication intake, along with the doctor-patient relationship. Initial results from studies concerning new interventions for therapy support are promising, but reports suggest that general acceptance of new treatment support tools is needed among patients and physicians alike. OBJECTIVE: The aim of this study was to investigate the actual use of the Internet and other modern media among physicians involved in breast cancer treatment. METHODS: Using a standardized questionnaire, actual utilization of new media among physicians was analyzed. Internet-related behaviors in private, as well as in business life, were investigated. Attention was focused on physicians' opinions regarding modern eHealth tools and how patients could be best supported to enhance adherence. RESULTS: A total of 120 physicians, all participating in breast cancer care, completed the questionnaire (median age 41 years). Almost all participants (99.2%, 119/120) used the Internet for general purposes and 98.3% (118/120) used it for medical issues as well. Virtually all medical professionals (99.2%, 119/120) reported that they owned a computer, while more recently invented technologies such as tablets and smartphones were owned by 31.9% (38/119) and 73.1% (87/119), respectively. The Internet was favored by 66.4% (79/119) of the physicians in our survey as a source for patient support; 71.2% (84/118) would also favor modern media for side effect registration. Based on our analysis, the most frequent Internet-utilizing physicians were characterized by age <60, worked in a hospital, and were employed as a junior physician. CONCLUSIONS: This study demonstrated a high usage of Internet-related technologies among physicians, indicating that the use of eHealth for advanced and individualized support in breast cancer care is a promising addition to treatment management. Such technologies have the potential to enhance adherence and compliance in therapy among cancer patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".