Examining How Technology Supports Shared Decision-Making in Oncology Consultations: Qualitative Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Commonly used digital health technologies, such as electronic health record systems and patient portals as well as custom-built digital decision aids, have the potential to enhance person-centered shared decision-making (SDM) in cancer care. SDM is a 2-way exchange of information between at least a clinician and the patient and a shared commitment to make informed decisions. However, there is little evidence in the literature on how technologies are used for SDM or how best they can be designed and integrated into workflows and practice. This may be due to the nature of SDM, which is fundamentally human interactions and conversations that produce desired human outcomes. Therefore, technology must be nonintrusive while supporting the human decision-making process. OBJECTIVE: This study examined how digital technologies can help cancer care professionals improve SDM in oncology consultations. METHODS: Health care professionals who treat patients with cancer were invited to participate in online co-design focus group meetings. During these sessions, they shared their experiences using digital technologies for SDM and provided suggestions to improve their use of digital technologies. The session recordings were transcribed and then analyzed using qualitative thematic analysis. The 3-talk SDM model, which consists of 3 steps-team talk, option talk, and decision talk-was used as the guiding framework. This approach was chosen because the 3-talk SDM model has been adopted in Australia. The researchers walked the participants through the SDM model and discussed their routine clinical workflows. RESULTS: In total, 9 health care professionals with experience treating patients with cancer and using technologies participated in the study. Two focus groups and 2 interviews were conducted in 2024. Three themes and 7 subthemes were generated from the thematic analysis. The findings indicated that various digital technologies, such as electronic health record systems, mobile devices, and patient portals, are used by cancer care professionals to help improve patients' understanding of their disease and available care options. Digital technologies can both improve and undermine SDM. Current systems are generally not designed to support SDM. Key issues such as data integration and interoperability between systems negatively impact the ability of digital technologies to support SDM. Emerging technologies such as generative artificial intelligence were discussed as potential facilitators of SDM by automating information gathering and sharing with patients and between health professionals. CONCLUSIONS: This research indicates that digital technologies have the potential to impact SDM in oncology consultations. However, this potential has not yet been fully realized, and significant modifications are required to optimize their usefulness in person-centered SDM. Although technology can facilitate information sharing and improve the efficiency of consultation workflows, it is only part of a complex human communication process that needs support from multiple sources, including the broader multidisciplinary cancer team.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".