Patient-Centered Care and Patient-Reported Measures: Let’s Look Before We Leap
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it