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Enregistrement W2304839812 · doi:10.1108/ijopm-02-2015-0086

The effect of social relationships on the rates of referral to specialists

2016· article· en· W2304839812 sur OpenAlexaff
Rosa Hendijani, Diane P. Bischak

Notice bibliographique

RevueInternational Journal of Operations & Production Management · 2016
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueHealthcare Systems and Technology
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésReferralAffect (linguistics)Family medicineMedicinePerspective (graphical)NursingPsychologyComputer science

Résumé

récupéré en direct d'OpenAlex

Purpose – In order to decrease patient waiting time and improve efficiency, healthcare systems in some countries have recently begun to shift away from decentralized systems of patient referral from general practitioners (GPs) to specialists toward centralized ones. From a queueing theory perspective, centralized referral systems can decrease waiting time by reducing the variation in the referral process. However, from a social psychological perspective, a close relationship between referring physician and specialist, which is characteristic of decentralized referral systems, may safeguard against high referral rates; since GPs refer patients directly to the specialists whom they know, they may be reluctant to damage that relationship with an inappropriate referral. The purpose of this paper is to examine the effect upon referral behavior of a relationship between physicians, as is found in a decentralized referral system, vs a centralized referral system, which is characterized by an anonymous GP-specialist relationship. In a controlled experiment where family practice residents made decisions concerning referral to specialists, physicians displaying high confidence referred significantly fewer patients in a close relationship condition than in a centralized referral system, suggesting that for some physicians, referral behavior can be affected by the design of the service system and will, in turn, affect system performance. Design/methodology/approach – The authors used a controlled experiment to test the research hypotheses. Findings – Physicians displaying high confidence referred significantly fewer patients in a close relationship condition than in a centralized referral system, suggesting that for some physicians, referral behavior can be affected by system attributes and will, in turn, affect system performance. Research limitations/implications – The current study has some limitations, however. First, the sample consisted only of family practice residents and did not have the knowledge and experience of GPs regarding the referral process. Second, the authors used hypothetical patient case descriptions instead of real-world patients. Repeating this experiment with primary care physicians in real setting would be beneficial. Practical implications – The study indicates that decentralized referral systems may act (rightly or wrongly) as a restraint on the rate of referrals to specialists. Thus, an implementation of a centralized referral system should be expected to produce an increase in referrals simply due to the change in the operational system setup. Even if centralized referral systems are more efficient and can facilitate the referral process by creating a central queue rather than multiple single queues for patients, the removal of social ties such as long-term social relationships that are developed between GPs and specialists in decentralized referral systems may act to counterbalance these theoretical gains. Social implications – This study provide support for the idea that non-clinical factors play an important role in referrals to specialists and hence in the quality of provided care, as was suggested by previous studies in this area (Hajjaj et al. , 2010; Reid et al. , 1999). The design of the service system may inadvertently influence some doctors to refer too many patients to specialists when there is no need for a specialist visit. In high-utilization health systems, this may cause some patients to be delayed (or even denied) in obtaining specialist access. Healthcare systems may be able to implement behavioral-based techniques in order to mitigate the negative consequences of a shift to centralized referral systems. One approach would be to try to create a feeling of close relationship among doctors in centralized referral systems. High communication and frequent interaction among GPs and specialists can boost the feelings of teamwork and personal efficacy through social comparison (Schunk, 1989, 1991) and vicarious learning (Zimmerman, 2000), which can in turn motivate GPs to take control of the patient care process when appropriate, instead of referring patients to specialists. Originality/value – The authors’ study is the first examining the effect of social relationships between GPs and specialists on the referral patterns. Considering the significant implications of referral decisions on patients, doctors, and the healthcare systems, the study can shed light into a better understanding of the social and behavioral aspects of the referral process.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,884
Score d'incertitude au seuil0,213

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,045
Tête enseignante GPT0,324
Écart entre enseignants0,278 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2016
Routes d'admission1
Résumé présentoui

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