Effect of a Mobile App for the Pharmacotherapeutic Follow-Up of Patients With Cancer on Their Health Outcomes: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Oral antineoplastic agents (OAAs) have revolutionized cancer management. However, they have been reported with adverse side effects and drug-drug interactions. Moreover, patient adherence to OAA treatment is critical. Mobile apps can enable remote and real-time pharmacotherapeutic monitoring of patients, while also promoting patient autonomy in their health care. OBJECTIVE: The primary objective was to analyze the effect of using a mobile app for the follow-up of patients with oncohematological malignancies undergoing treatment with OAAs on their health outcomes. The secondary objectives were to analyze the role of the app in communication with health care professionals and patient satisfaction with the app. METHODS: We performed a comparative, quasi-experimental study based on a prepost intervention with 101 patients (control group, n=51, traditional pharmacotherapeutic follow-up vs intervention group, n=50, follow-up through e-OncoSalud, a custom-designed app that promotes follow-up at home and the safety of patients receiving OAAs). The effect of this app on drug safety, adherence to treatment, and quality of life was evaluated. RESULTS: With regard to drug safety, 73% (37/51) of the patients in the control group and 70% (35/50) of the patients in the intervention group (P=.01) presented with drug-related problems. The probability of detecting an insufficiently treated health problem in the intervention group was significantly higher than that in the control group (P=.04). The proportion of patients who presented with side effects in the intervention group was significantly lower than that in the control group (P>.99). In the control group, 49% (25/51) of the patients consumed some health resources during the first 6 months of treatment compared with 36% (18/50) of the patients in the intervention group (P=.76). Adherence to treatment was 97.6% (SD 7.9) in the intervention group, which was significantly higher than that in the control group (92.9% [SD 10.0]; P=.02). The EuroQol-5D in the intervention group yielded a mean (SD) index of 0.875 (0.156), which was significantly higher than that in the control group (0.741 [0.177]; P<.001). Approximately 60% (29/50) of the patients used the messaging module to communicate with pharmacists. The most frequent types of messages were acknowledgments (77/283, 27.2%), doubts about contraindications and interactions with OAAs (70/283, 24.7%), and consultations for adverse reactions to treatment (39/283, 13.8%). The satisfaction with the app survey conducted in the intervention group yielded an overall mean (SD) score of 9.1 (0.4) out of 10. CONCLUSIONS: Use of e-OncoSalud for the real-time follow-up of patients receiving OAAs facilitated the optimization of some health outcomes. The intervention group had significantly higher health-related quality of life and adherence to treatment than the control group. Further, the probability of the intervention group presenting with side effects was significantly lower than that of the control group.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".