Risk Adjusting Health Care Provider Collaboration Networks.
Risk Adjusting Health Care Provider Collaboration Networks.
复制标题
风险调整医疗保健提供者协作网络。
DOI：
10.1055/s-0039-1694990
复制
发表时间：
2019
影响因子：
1.7
通讯作者：
Soulakis,NicholasD
中科院分区：
文献类型：
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作者：
Chandler,ArielE;Mutharasan,RKannan;Amelia,Lia;Carson,MatthewB;Scholtens,DeniseM;Soulakis,NicholasD
目的出院护理质量和患者因素（健康和社会人口学）影响计划外再入院率。本研究的目的是测量控制的病人因素时，使用再入院率量化的加权优势之间的合作网络中的医疗保健提供者的影响。这种改进的理解可能会通知策略，以减少再入院，并促进质量改进initiatives.MethodsWe提取4年的患者，供应商，和活动数据相关的心脏病出院工作流程。开发了一个威布尔模型来预测计划外30天再入院的风险。一个提供者-患者的双向网络被用来连接供应商共享的病人遇到。我们建立了合作网络，并计算了共享的积极成果比率（SPOR），以量化提供者之间的关系，通过患者结果的相对比率，使用风险调整后的再入院率和未调整的再入院率。使用置换检验和描述statistics.ResultsComparing的合作网络组成的2,359供应商对的SPOR度量的计算风险调整的效果进行了量化，我们发现，SPOR值与风险调整后的结果是显着不同，比未经调整的再入院作为一个结果的措施（p值= 0.025）。这两个网络将相同的供应商对分类为高分51.5%的时间，相同的低分供应商对85.6%的时间。通过应用风险调整模型，降低了高分和低分提供者对之间观察到的患者人口统计学和疾病特征差异。风险调整后的模型减少了平均变化在每个人的SPOR评分provider connections.ConclusionsRisk调整计划外再入院的协作网络有影响SPOR加权的边缘，特别是在分类高分SPOR供应商对。风险调整后的模型减少了供应商的连接和平衡之间的低和高分供应商对共享的患者特征的方差。这表明风险调整后的SPOR边缘通过考虑患者的再入院风险更好地测量了协作对再入院的影响。
ObjectivesThe 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.MethodsWe 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 theShared 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.ResultsComparing 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.ConclusionsRisk 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.