Perceived Barriers and Facilitators in Using Patient-Reported Outcome Systems for Cancer Care: Systematic Mapping Study
Bibliographic record
Abstract
BACKGROUND: Cancer is a major global health problem. Patient-reported outcome (PRO) systems have been developed to support the treatment of patients with cancer. Although clear evidence of the benefits of the routine use of electronic patient-reported outcomes (ePROs) exists, engaging physicians in using these systems has been challenging. OBJECTIVE: This study aims to identify and analyze what is currently known about health care professionals' (HCPs) perceived barriers and facilitators that exist and influence the use of ePRO systems for cancer care. METHODS: We carried out a systematic mapping study by conducting searches of 3 databases (Association for Computing Machinery, PubMed, and Scopus). Eligible papers were published between 2010 and 2021, and they described HCPs' perspectives on using ePROs. The data on the included papers were extracted, a thematic meta-synthesis was performed, and 7 themes were summarized into 3 categories. RESULTS: A total of 17 papers were included in the study. The HCPs' perceived barriers and facilitators of using ePROs can be summarized into 7 themes: clinical workflow, organization and infrastructure, value to patients, value to HCPs, digital health literacy, usability, and data visualization and perceived features. These themes can be further summarized into 3 categories: work environment, value to users, and suggested features. According to the study, ePROs should be interoperable with hospital electronic health records and adapted to the hospital workflow. HCPs should get appropriate support for their use. Additional features are needed for ePROs, and special attention should be paid to data visualization. Patients should have the option to use web-based ePROs at home and complete it at the time most valuable to the treatment. Patients' ePRO notes need attention during clinical visits, but ePRO use should not limit patient-clinician face-to-face communication. CONCLUSIONS: The study revealed that several aspects need improvement in ePROs and their operating environments. By improving these aspects, HCPs' experience with ePROs will enhance, and thus, there will be more facilitating factors for HCPs to use ePROs than those available today. More national and international knowledge about using ePROs is still needed to cover the need for information to develop them and their operating environments to meet the needs of HCPs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".