OBJECTIVES The quality of hospital discharge care and patient factors (health and sociodemographic) impact the rates of unplanned readmissions. This study aims to measure the effects of controlling for the patient factors when using readmission rates to quantify the weighted edges between health care providers in a collaboration network. This improved understanding may inform strategies to reduce hospital readmissions, and facilitate quality-improvement initiatives. METHODS We extracted 4 years of patient, provider, and activity data related to cardiology discharge workflow. A Weibull model was developed to predict the risk of unplanned 30-day readmission. A provider-patient bipartite network was used to connect providers by shared patient encounters. We built collaboration networks and calculated the Shared Positive Outcome Ratio (SPOR) to quantify the relationship between providers by the relative rate of patient outcomes, using both risk-adjusted readmission rates and unadjusted readmission rates. The effect of risk adjustment on the calculation of the SPOR metric was quantified using a permutation test and descriptive statistics. RESULTS Comparing the collaboration networks consisting of 2,359 provider pairs, we found that SPOR values with risk-adjusted outcomes are significantly different than unadjusted readmission as an outcome measure (p-value = 0.025). The two networks classified the same provider pairs as high-scoring 51.5% of the time, and the same low scoring provider pairs 85.6% of the time. The observed differences in patient demographics and disease characteristics between high-scoring and low-scoring provider pairs were reduced by applying the risk-adjusted model. The risk-adjusted model reduced the average variation across each individual's SPOR scored provider connections. CONCLUSIONS Risk adjusting unplanned readmission in a collaboration network has an effect on SPOR-weighted edges, especially on classifying high-scoring SPOR provider pairs. The risk-adjusted model reduces the variance of providers' connections and balances shared patient characteristics between low- and high-scoring provider pairs. This indicates that the risk-adjusted SPOR edges better measure the impact of collaboration on readmissions by accounting for patients' risk of readmission.

中文翻译：

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风险调整医疗服务提供者合作网络。

目的医院出院护理的质量和患者因素（健康和社会人口统计学）影响计划外的再入院率。这项研究旨在衡量使用再入院率量化协作网络中医疗保健提供者之间的权重优势时控制患者因素的效果。这种更好的理解可以为减少医院再住院的策略提供参考，并促进质量改进计划。方法我们提取了4年与心脏病出院工作流程相关的患者，提供者和活动数据。建立了Weibull模型来预测计划外30天再次入院的风险。提供者-患者两方网络用于通过共享患者遭遇来连接提供者。我们建立了协作网络并计算了共享的积极结果比率（SPOR），以风险调整后的再入院率和未调整后的再入院率，通过患者相对结局的相对比率来量化提供者之间的关系。使用排列检验和描述性统计数据量化了风险调整对SPOR度量计算的影响。结果通过比较由2359个提供商对组成的协作网络，我们发现具有风险调整结果的SPOR值与未经调整的再入院率作为结果度量（p值= 0.025）显着不同。这两个网络将相同的提供者对分类为高分的51.5％的时间，相同的低评分提供者对分类为86.5％的时间。通过应用风险调整模型，减少了高评分和低评分提供者对之间的患者人口统计学特征和疾病特征差异。风险调整后的模型减少了每个人的SPOR评分提供商连接之间的平均差异。结论风险调整协作网络中的计划外重新接纳会影响SPOR加权边缘，尤其是对高分SPOR提供者对进行分类。风险调整后的模型减少了医疗服务提供者之间的联系差异，并平衡了低和高评分医疗服务提供者对之间共享的患者特征。这表明风险调整后的SPOR边缘可通过考虑患者的再入院风险更好地衡量协作对再入院的影响。