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 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 burden (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 whole person-focused goals. This study identified advanced age, chronic disease burden 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 | 0.042 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| 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".