Patient-Centered Care and Patient-Reported Measures: Let’s Look Before We Leap
Bibliographic record
Abstract
This commentary focuses on patient-reported measures as tools to support patient-centered care for patients with multiple chronic conditions (MCCs). We argue that those using patient-reported measures in care management or evaluation of services for MCC patients should do so in recognition of the challenges involved in treating them. MCC patient care is challenging because (1) it is difficult to specify the causes of particular symptoms; (2) assessment of many important symptoms relies on subjective report; and (3) patients require care from a variety of providers. Due to the multiple domains of health affected in single individuals, and the large variation in needs, care that is holistic and individualized (i.e. patient-centered) is appropriate for MCC patients. However, due to the afore-mentioned challenges, it is important to carefully consider what this care entails and how practical contexts shape it. Patient-centered care for MCC patients implies continuous, dialogic patient-provider relationships, and the formulation of coherent and adaptive multi-disciplinary care protocols. We identify two broadly defined contextual influences on the nature and quality of these processes and their outputs: (1) busy practice settings and (2) fragmented information technology. We then identify several consequences that may result from inattention to these contextual influences upon introduction of patient-reported measure applications. To maximize the benefits, and minimize the harms of patient-reported measure use, we encourage policy makers and providers to attend carefully to these and other important contextual factors before, during and after the introduction of patient-reported measure initiatives.
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 | 0.071 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.044 | 0.075 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".