Integrating the Patient Perspective into Healthcare and Real-World Evidence: The Multi-site, Cross-Disease, Patient-Centered Outcomes Research Project in the Medical Informatics Initiative (PCOR-MII)
Bibliographic record
Abstract
Abstract This paper presents the Patient-Centered Outcomes Research within the Medical Informatics Initiative (PCOR-MII) project, focusing on the integration of patient-reported outcomes (PROs) into a large-scale national data sharing infrastructure, established in Germany by the Medical Informatics Initiative (MII). PCOR-MII aims to systematically address the interests of various stakeholders in patient-reported health data and three dimensions of clinical utility: (1) prediction, (2) monitoring, and (3) outcome assessment. The project builds upon harmonized technical, data, and compliance environments established at the participating institutions as part of the MII to deploy and roll out software solutions for capturing PROs and making them accessible within local electronic health record (EHR) systems. To overcome interoperability challenges, PCOR-MII is developing a construct-oriented PROM module for the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR)–based German National Core Dataset. The project applies its approach to three patient populations with distinct characteristics: anorexia nervosa targeting risk prediction (dimension 1), kidney transplantation prioritizing health status and adherence monitoring (dimension 2), and persistent somatic symptoms primarily aimed at assessing and understanding outcomes (dimension 3). With their emphasis on different aspects of PROs, those application areas can serve as blueprints for a broader roll-out. PCOR-MII represents a structured and comprehensive effort to incorporate PROs into a national data infrastructure, promising more precise diagnostics, improved treatment decisions, and the generation of new biomedical insights. We believe that our structured approach may serve as a guiding framework for others aiming to implement PROs in diverse healthcare settings.
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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 | 0.355 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".