Factors Influencing Physicians' Referral Decision-Making for Rehabilitation Outpatient Services in the Health Care Landscape of China: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Stratified health care systems are used 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 health care 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 the patient factors (diagnosis, functional impairments, disease status, condition stability, duration of illness, and functional status measured via the Longshi Scale) impacting physicians' referral decisions for outpatient rehabilitation services in China. Data were collected through 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. In total, 45.5% (902/1984) of outpatient rehabilitation patients were considered by physicians to be referred to rehabilitation outpatient clinics, 19% (376/1984) to primary health care institutions, 20.4% (405/1984) to secondary institutions, and 15.2% (301/1984) 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 (odds ratio [OR] 1.88, 95% CI 1.02-3.43; P=.04), cardiopulmonary diseases (OR 2.91, 95% CI 1.07-7.93; P=.04), geriatric conditions (OR 0.40, 95% CI 0.23-0.68; P<.001), disease control (OR 0.23, 95% CI 0.13-0.34; P<.001), and Longshi Scale results for the bedridden (OR 0.10, 95% CI 0.14-0.34; P<.001), and domestic groups (OR 0.24, 95% CI 0.14-0.34; P<.001) as independent factors for referrals to tertiary versus primary institutions. Orthopedic diseases (OR 3.27, 95% CI 1.89-5.67; P<.001), geriatric conditions (OR 0.58, 95% CI 0.33-1.87; P=.009), cognitive impairments (OR 1.98, 95% CI 1.17-3.36; P=.01), multiple impairments (OR 0.35, 95% CI 0.18-0.70; P=.002), and disease control (OR 0.26, 95% CI 0.15-0.37; P<.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. In addition, it will assist the government in reasonably and effectively allocating medical resources, thus achieving the optimization and coordination of the health care system.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».