Risk Adjustment of Capitation Payment System: What Can Indonesia Adopt from other Countries?
Bibliographic record
Abstract
AbstractCapitation calculation in Indonesia is not adjusted by individual and aggregate risk. Without risk adjustment, capitation ratesare likely to overpay or underpay primary care. This study aimed to review risk-adjusted capitation payment in other countriesfor evaluation of capitation payment system in Indonesia. The conduct and reporting of this systematic review followedthe recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This studyused comprehensive electronic search in five databases: Pubmed, Proquest, Scopus, PMC, and EBSCOHost. Search resultsfrom five databases in April 2018, yielded a total 19 titles that will continue to review the full article and at the end, 4 articlesincluded for systematic review. Based on risk adjustment of capitation payment system in UK, USA, Canada and Sweden,Indonesia may initiate the use of risk adjustment based on the distribution of age and sex. Then Indonesia can develop riskadjustment based on diagnosis and socioeconomic factors to create more fair and accurate capitation rates for primary care.AbstrakPerhitungan kapitasi di Indonesia belum disesuaikan berdasarkan risiko individu dan agregat. Tanpa penyesuaian risiko, rate kapitasicenderung untuk membayar lebih atau kurang fasilitas kesehatan tingkat pertama. Studi ini bertujuan untuk meninjau sistem pembayarankapitasi di negara lain sebagai dasar evaluasi untuk sistem pembayaran kapitasi di Indonesia Penyusunan systematic review inimenggunakan rekomendasi dari Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Studi ini mengumpulkanartikel dari lima database yaitu: Pubmed, Proquest, Scopus, PMC, and EBSCOHost. Hasil pencarian dari lima database padabulan April 2018, didapatkan 19 judul artikel yang akan dilanjutkan untuk ditinjau secara menyuluruh, dan akhirnya didapatkan 4artikel yang akan diikutsertakan dalam systematic review. Berdasarkan penyesuaian risiko sistem pembayaran kapitasi di UK, USA,Canada dan Swedia, Indonesia dapat memulai sistem pembayaran kapitasi berdasarkan penyesuiaan distribusi umur dan jenis kelamin.Selanjutnya Indonesia dapat mengembangkan sistem pembayaran kapitasi berdasarkan diagnosis dan sosioekonomi untukmenciptakan rate kapitasi yang lebih adil dan akurat untuk fasilitas kesehatan tingkat pertama.
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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 | 0.062 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".