Advancing digital patient-centered measurement methods for team-based care
Bibliographic record
Abstract
Objectives: To conceptualize new methods for integrating patient-centered measurement into team-based care. Methods: A standalone portal was introduced into a rural clinic to support conceptualization of new methods for integration of patient-centered measurement in team-based care. The portal housed mental health-related online resources, three patient-reported measures and a self-action plan. Six providers and four patients used the portal for four months. Our data collection techniques included clinic discussions, one-on-one interviews, workflow diagrams and data generated through the portal. Analysis was supported through coding interview transcripts, looking across multiple sources of research data and research team discussions. Results: Our research team conceptualized five team-based patient-centered measurement methods through this study. Patient-centered measurement Team Mapping offfers a technique to provide greater clarity of care-team roles and responsibilities in data collected through patient-centered measurement. Longitudinal Care Alignment can guide the care-team on incorporating patient-centered measurement into ongoing provider-patient interactions. Digital Tool Exploration can be used to evaluate a team's readiness toward digital tool adoption, and the impact of these tools. Team-based quality improvement serves as a framework for engaging teams in patient-centered quality improvement. Shared learning is a method that promotes patientprovider interactions that validate patient's perspectives of their care. Conclusion: The portal illuminated new methods for the integration of patient-centered measurement in team-based care. The first three proposed patient-centered measurement methods provides ways to assess how a clinic can incorporate patient-centered measurement methods into team-based care. The latter two methods focus on the aim of patient-generated data in which patient's values and perspectives are represented and quality of patient-centered care can be evaluated. Further testing is needed to assess the utility of these patient-centered measurement methods across different clinical settings and domains.
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 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 | 0.105 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".