Using Visual Methods to Capture Patient Perspectives in Interprofessional Team-Based Care for Chronic Disease Management
Bibliographic record
Abstract
Background: Understanding how patients perceive their role in the healthcare team can improve overall satisfaction of care and health outcomes. However, it has been challenging to capture the diversity of patient experiences using traditional research approaches. The goal of this study was to explore the perspectives of patients involved in an interprofessional team-based chronic disease management program for chronic obstructive pulmonary disease using visual research techniques. Methods: Our visual approach began with patients autonomously drawing (or mapping) experiences with their healthcare team. The maps were explored with the patients through focus group discussions. Maps were inductively coded to identify similarities and differences between participants’ perceptions. Focus group transcripts were first analyzed independently, then compared to and integrated into the map analysis. Findings: Overall, participants (n = 13) were satisfied as patients of team-based care. Participants drew multiple healthcare providers, sources of information, and themselves to represent their teams. Relationships and significance were represented using arrows, the size of each team member, facial expressions, and symbols. Four key elements of effective team-based models of care emerged: 1) effective information sharing, 2) diversity of providers’ roles, 3) empowerment through self-management, and 4) enhanced access to care. Conclusion: This study used visualization methodology to obtain patient feedback on the program’s performance, elicit patients’ experiences, and attempt to mitigate some of the limitations of isolated survey and focus group methodology, subsequently obtaining rich data on team-based care. Our research also informs ongoing quality improvement of the team nbased model for chronic disease management.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".