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

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

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

Bibliographic record

VenueInternational Journal of Operations & Production Management · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReferralAffect (linguistics)Family medicineMedicinePerspective (graphical)NursingPsychologyComputer science

Abstract

fetched live from 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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.324
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations5
Published2016
Admission routes1
Has abstractyes

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