First Insights on Value-Based Healthcare of Elders Using ICHOM Older Person Standard Set Reporting
Bibliographic record
Abstract
Abstract Background: Clinical guidelines for specific conditions fragment care provision for elders. The International Consortium for Health Outcomes Measurement (ICHOM) has developed a global standard set of outcome measures for comprehensive assessment of older persons. The goal of this study was to report value-based health metrics in Taiwan using this ICHOM toolset. Methods: The cross-sectional study of baseline data excerpted from a prospective longitudinal cohort, which recruited people ≥65 years old with ≥3 chronic medical conditions who received value-based healthcare services between July and December 2018. All participants received measurements of physical performance, anthropometric characteristics, health-related behaviors, Charlson Comorbidity Index, and Montreal Cognitive Assessment. The ICHOM toolset comprises three tiers: 1 includes frailty and having chosen a preferred place of death; 2 includes polypharmacy, falls, and participation in decision-making; and 3 includes loneliness, activities of daily living, pain, depression, and walking speed. These items were converted into a 0–10 point value-based healthcare score, with high value-based health status defined as ≥8/10 points. Results: Frequencies of individual ICHOM indicators were: frail 11.7%, chose preferred place of death 14.4%, polypharmacy 31.5%, fell 17.1%, participated in decision-making 81.6%, loneliness 26.8%, limited activities of daily living 22.4, pain 10.4%, depressed mood 13.0%, and slowness 38.5%. People with high disease severity (OR 0.40, 95% CI 0.21–0.76, p=0.005) or cognitive impairment (OR 0.49, 95%CI 0.27–0.87, p=0.014) were less likely to have high value-based healthcare status. Conclusions: The ICHOM Standard Set Older Person health outcome measures provide an opportunity to shift from a disease-centric medical paradigm to patient-focused goals. This study identified advanced age, chronic disease and cognitive impairment as important barriers to achieving high value-based healthcare status.
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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 | 0.039 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".