VALUE-BASED HEALTHCARE AS A FRAMEWORK OF CARE FOR OLDER ADULTS AND THEIR FAMILY CAREGIVERS
Bibliographic record
Abstract
Abstract Value-based healthcare (VBHC) is a term synonymous with the pursuit of greater value in healthcare, however, the term “value” lacks conceptual clarity. Using Porter’s 2010 seminal VBHC shows promise for placing the older adult and their family caregiver at the center of care models, which is a central tenet in healthcare value. The purpose of our study is to conceptualize how Porter’s VBHC is defined, operationalized, and implemented to address the need for person-and-family centered care models that improve outcomes while being cost effective. A literature search in six academic databases was conducted to identify articles examining VBHC; specifically studies with a Porter-based patient-centric focus. 1,001 articles were retrieved for initial review. Using a consensus-based logic model for inclusion/exclusion, 802 met the inclusion criteria for full text review. Articles were examined using the following objectives: 1) conceptually map the VBHC literature, 2) identify application of Porter’s equation, and 3) identify the methodologies used to measure outcomes, costs, and value. Findings were cross-compared and emergent themes organized to Porter facets of value (outcomes, costs, and value). Our review found the three facets of Porter’s VBHC are established across the literature, however, most studies examine only one or two facets and fail to specify or define all three. Although applied in research, there is a lack of consistency in the actual use of Porter’s definition. Five recommendations for future research using Porter’s VBHC include: (1) pre-selection of outcomes/costs, (2) operationalizing outcomes/costs, (3) value framework creation, (4) data collection, and (5) value calculation.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 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".