Application of Segmentation Methods in Health Care Services Planning and Delivery
Bibliographic record
Abstract
Abstract Segmentation is a well-known marketing technique used to identify homogenous groups of consumers to better respond to their needs and preferences. The method aligns with the concept of patient-centricity in health care and has already been applied in some contexts. This chapter provides an overview of existing applications of segmentation to improve the quality of health care services provided to individuals along the health continuum. The literature reviewed highlights applications of segmentation for population health management, risk stratification and personalized care purposes. Segmentation was either expert- or data-driven or a combination of the two. Data-driven approaches were often based on administrative data available from electronic health records and claims data, but the need to consider a more comprehensive set of health-related factors, such as social and behavioral determinants of health and patients’ experience measures was highlighted. A lack of evidence demonstrating how applications of segmentation approaches can lead to better tailoring of health services and improve the quality of care provided was noted and should be addressed in future research and applications.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".