Hamburg Center for Health Economics, Esplanade 36, 20354, Hamburg, Germany.
OptiMedis AG, Buchardstraße 17, 20095, Hamburg, Germany.
BMC Med Res Methodol. 2023 Oct 28;23(1):252. doi: 10.1186/s12874-023-02048-7.
Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices' connectedness to peers and their prescribing performance in two German regions.
We first mapped physician practice networks using links established between two practices that share 8 or more patients; we calculated network-level (density, average path length) and node-level measures (degree, betweenness, eigenvector). We defined prescribing performance as the total number of inappropriate medications prescribed or appropriate medications not prescribed (PIMs) to senior patients (over the age of 65) during the calendar year 2016. We used FORTA (Fit fOR The Aged) algorithm to classify medication appropriateness. Negative binomial regression models estimate the association between node-level measures and prescribing performance of physician practices controlling for patient comorbidity, provider specialization, percentage of seniors in practice, and region. We conducted two sensitivity analyses to test the robustness of our findings - i) limiting the network mapping to patients younger than 65; ii) limiting the network ties to practices that share more than 25 patients.
We mapped two patient-sharing networks including 436 and 270 physician practices involving 28,508 and 20,935 patients and consisting of 217,126 and 154,274 claims in the two regions respectively. Regression analyses showed a practice's network connectedness as represented by degree, betweenness, and eigenvector centrality, is significantly negatively associated with prescribing performance (degree-bottom vs. top quartile aRR = 0.04, 95%CI: 0.035,0.045; betweenness-bottom vs. top quartile aRR = 0.063 95%CI: 0.052,0.077; eigenvector-bottom vs. top quartile aRR = 0.039, 95%CI: 0.034,0.044).
Our study provides evidence that physician practice prescribing performance is associated with their peer connections and position within their network. We conclude that practices occupying strategic positions at the edge of networks with advantageous access to novel information are associated with better prescribing outcomes, whereas highly connected practices embedded in insulated information environments are associated with poor prescribing performance.
由于多种药物治疗的巨大临床和经济成本以及人口老龄化的加剧，优化处方实践非常重要。先前的研究表明，社会关系在推动处方行为方面的重要性。我们使用社交网络分析，研究了德国两个地区医生实践与同行的联系与其在处方行为表现之间的关系。
我们首先使用在共享 8 个或更多患者的两个实践之间建立的链接来映射医生实践网络；我们计算了网络级别（密度，平均路径长度）和节点级别（度数，介数，特征向量）的度量。我们将处方行为表现定义为 2016 年为老年患者（65 岁以上）开出的不合适药物总数或未开出的适当药物（PIM）。我们使用 FORTA（适用于老年人）算法来分类药物的适当性。负二项回归模型估计节点级别的措施与医生实践的处方行为表现之间的关联，同时控制患者合并症，提供者专业化，实践中老年人的比例和地区。我们进行了两项敏感性分析以检验研究结果的稳健性- i）将网络映射限制在 65 岁以下的患者；ii）将网络联系限制在共享 25 个以上患者的实践。
我们绘制了两个患者共享网络，包括 436 个和 270 个医生实践，涉及 28508 个和 20935 个患者，分别由两个地区的 217126 个和 154274 个索赔组成。回归分析表明，实践的网络连通性（由度数，介数和特征向量中心性表示）与处方行为表现显著负相关（底部与顶部四分位数的 aRR=0.04，95%CI：0.035，0.045；底部与顶部四分位数的 aRR=0.063，95%CI：0.052，0.077；底部与顶部四分位数的 aRR=0.039，95%CI：0.034，0.044）。
我们的研究提供了证据，表明医生实践的处方行为表现与他们的同行联系及其在网络中的位置有关。我们的结论是，处于网络边缘具有有利获取新信息的战略地位的实践与更好的处方结果相关，而处于隔离信息环境中的高度联系的实践与较差的处方行为表现相关。
