Performance Measurement for People with Multimorbidity and Complex Health Needs
Bibliographic record
Abstract
This paper reviews approaches to performance measurement in health systems with particular attention to people with multimorbidity and complex health needs. Performance measurement should be informative and used by multiple stakeholders in order to align performance improvement efforts. System performance measures must allow for macro-system and meso-organization and provider-level reporting, and they should be relevant and important to stakeholders at each level, as well as to patients and all potential care recipients. Measures that assess health outcomes and individuals' experiences with providers, including care planning and coordination of care across providers, are essential to assess value for people with multimorbidity and complex health needs. I suggest that performance measurement for this population should be motivated by the Complexity Framework and organized by the Triple Aim. Based on the care needs and appropriate goals for the health system for this population, applicable measures and suggestions for implementing and using performance measurement systems are identified. Particularly in the case of people with multimorbidity and complex health needs, performance measures must move beyond measures specific to individual encounters to track care for people over time and space. Measures must be rooted in individuals' own needs and goals for care. New systems are required to enable collection and reporting of these measures.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".