| Literature DB >> 35650460 |
Abstract
The sharing of patients reflects collaborative relationships between various healthcare providers. Patient-sharing in the outpatient sector is influenced by both physicians' activities and patients' preferences. Consequently, a patient-sharing network arises from two distinct mechanisms: the initiative of the physicians on the one hand, and that of the patients on the other. We draw upon medical claims data to study the structure of one patient-sharing network by differentiating between these two mechanisms. Owing to the institutional requirements of certain healthcare systems rather following the Bismarck model, we explore different triadic patterns between general practitioners and medical specialists by applying exponential random graph models. Our findings imply deviation from institutional expectations and reveal structural realities visible in both networks.Entities:
Keywords: Closed and open triads; Exponential random graph models; Outpatient sector; Patient- and physician-induced networks; Service complementarity
Mesh:
Year: 2022 PMID: 35650460 PMCID: PMC9474566 DOI: 10.1007/s10729-022-09595-3
Source DB: PubMed Journal: Health Care Manag Sci ISSN: 1386-9620
Fig. 1Schematic representation of patient-sharing network creation and differentiation. Notes: Step 1: From the patient-physician bipartite network, we first generated a network linking physicians based on common patients (unipartite network). Step 2/3 (table and illustration): The result is a weighted network among physicians. Step 4: To form a patient- and physician-induced network, we utilized the referral information available in the accounting data. This means that patient-sharing links that came about due to referrals are classified rather as physician-induced ties. Patient-sharing ties without any referral details can be assigned to patients’ choices. GP = general practitioner; Step 4: MS = medical specialist; = with referral; = without referral; Own development, based on Moen et al. [3]; Casalino et al. [55]
Overview of triadic structures for physician- and patient-induced network and expected results
● = general practitioner with pediatricians (G/GP); ○ = medical specialist (S/MS)
Further endogenous and exogenous effects
GP = general practitioner (with pediatricians)
Fig. 2Visualization of the physician-induced network. Notes: Kamada-Kawai-Algorithm; blue nodes = general practitioner; orange nodes = medical specialist; 16 isolates
Fig. 3Visualization of the patient-induced network. Notes: Kamada-Kawai-Algorithm; blue nodes = general practitioner; orange nodes = medical specialist; no isolates
Results of the ERGMs
*Significant at p ≤ 0.05
G = general practitioner (with pediatricians); S = Medical specialist. λ = 2 if not indicated otherwise. Larger lambda values for structural parameters were selected to control for dense regions within the networks and to achieve convergence or to get a satisfactory model fit (for further details see [68]). Density were fixed to assist convergence [71]
Results of the ERGMs—Further endogenous and exogenous effects
*Significant at p ≤ 0.05
GP = general practitioner (with pediatricians). λ = 2 if not indicated otherwise. Larger lambda values for structural parameters were selected to control for dense regions within the networks and to achieve convergence or to get a satisfactory model fit (for further details see [68]). Density were fixed to assist convergence [71]