Analysis of Factors Influencing Physicians' Referral Decision-Making for Rehabilitation Outpatient Services in the Chinese Healthcare Landscape (Preprint)
Bibliographic record
Abstract
BACKGROUND Stratified healthcare systems are utilized globally to optimize medical resource allocation and enhance patient care experiences. Although successfully implemented in countries like the United Kingdom, Australia, and Canada, China's introduction of stratified healthcare in 2015 has achieved progress in disease management but still faces challenges due to the lack of a comprehensive referral evaluation system and patients' preference for higher-tier medical institutions. OBJECTIVE This study aims to investigate the factors influencing Chinese rehabilitation physicians' referral decisions for outpatient rehabilitation patients. The findings may provide empirical evidence for developing stratified rehabilitation triage tools and constructing a referral evaluation system in China. METHODS This cross-sectional study, conducted from September 2023 to January 2024, examined factors impacting physicians' referral decisions for outpatient rehabilitation services in China. Data were gathered via convenient stratified sampling from physicians and outpatient rehabilitation patients across 12 medical institutions in 5 cities in China . RESULTS A total of 131 rehabilitation physicians conducted diversion assessments for 1984 outpatient rehabilitation patients in this study. 45.5% of outpatient rehabilitation patients were considered by physicians to be referred to rehabilitation outpatient clinics, 19% to primary healthcare institutions, 20.4% to secondary institutions, and 15.2% to tertiary institutions. Single-factor analysis indicated that age, disease, functional impairment, disease control, disease stability, and Longshi Scale results were significantly associated with physicians' decisions regarding the referral institutions for outpatient rehabilitation patients. Logistic regression analysis showed that neurological disorders (OR=1.875, P=0.042), cardiopulmonary diseases (OR=2.193, P=0.036), geriatric conditions (OR=0.396, P<0.001), disease control (OR=0.231, P<0.001), and Longshi scale bedridden (OR=0.101, P<0.001) and domestic groups (OR=0.239, P<0.001) as independent factors for referrals to tertiary versus primary institutions. Orthopedic diseases (OR=3.270, P<0.001), geriatric conditions (OR=0.582, P=0.009), cognitive impairments (OR=1.979, P=0.012), multiple impairments (OR=0.354, P=0.002), and disease control (OR=0.235, P<0.001) were key factors for tertiary versus secondary referrals. CONCLUSIONS In advancing China's rehabilitation triage in the future, gaining a deep understanding of the key factors influencing physicians' decisions and quickly establishing a referral evaluation system will facilitate the accurate diversion of outpatient rehabilitation patients, enabling them to receive convenient, high-quality, and low-cost medical services. Additionally, it will assist the government in reasonably and effectively allocating medical resources, thus achieving the optimization and coordination of the healthcare system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".