The cream-skimming behaviors of tertiary hospitals under medical alliances: evidence from China
Bibliographic record
Abstract
BACKGROUND: While previous studies have delved into the formation and development of medical alliances in China, there has been limited focus on investigating inequity in the referral rates and the quality of care received provided to patients with experience of referral and those without under the introduction of medical alliances. This study explored: (1) inequity in the odds of being referred to healthcare institutions within medical alliances; and (2) inequity in the quality of care received between the referred and non-referred patients. METHODS: This study employed a dataset comprising 440,950 individuals who had at least one outpatient visit at healthcare facilities in Hangzhou city, Zhejiang province, China from January 1, 2020 to September 24, 2021. Quality of outpatient care was measured by the odds of having seven-day all-cause follow-up encounters to any healthcare institution. Binary regression models combined with random effects were constructed to examine inequity in the referral rates and the quality of care received. A set of sensitivity analyses were conducted to check the robustness of study findings. RESULTS: This study has three key findings. First, outpatients' insurance status, rather than their specific diseases and health conditions, was identified the most significant determinant driving healthcare institutions' referral decisions. Compared with outpatients covered by public health insurance programs, those without such coverage were more likely to be referred by tertiary hospitals to primary care facilities (coefficient = 1.33; 95% CI: 0.56-2.11) while being less likely to be referred by primary care facilities to tertiary hospitals (coefficient = -2.00; 95% CI: -3.08 - -0.92). Second, the referred outpatients received poorer quality of care, as indicated by higher odds of having all-cause follow-up encounters within seven days at any healthcare institution, compared to those non-referred outpatients. Third, outpatients with chronic diseases and public health insurance coverage not only experienced higher referral rates but poorer quality of outpatient care after being referred from tertiary hospitals to primary care facilities, compared to their counterparts. CONCLUSION: This study demonstrated that tertiary hospitals "siphoned-off" outpatients with public health insurance coverage from primary care facilities. Outpatients who were older, were male, with chronic diseases, and with public health insurance coverage were more likely to experience not only higher referral rates but poorer quality of outpatient care after being referred from tertiary hospitals to primary care facilities. Tailored policies are required to protect and compensate the most vulnerable population groups.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".