UCLA General Internal Medicine & Health Services Research, Los Angeles, California, USA.
Department of Population Medicine, Harvard Medical School, Boston, Massachusetts, USA.
BMC Health Serv Res. 2021 Oct 23;21(1):1143. doi: 10.1186/s12913-021-07116-6.
High-cost high-need patients are typically defined by risk or cost thresholds which aggregate clinically diverse subgroups into a single 'high-need high-cost' designation. Programs have had limited success in reducing utilization or improving quality of care for high-cost high-need Medicaid patients, which may be due to the underlying clinical heterogeneity of patients meeting high-cost high-need designations.
Our objective was to segment a population of high-cost high-need Medicaid patients (N = 676,161) eligible for a national complex case management program between January 2012 and May 2015 to disaggregate clinically diverse subgroups. Patients were eligible if they were in the top 5 % of annual spending among UnitedHealthcare Medicaid beneficiaries. We used k-means cluster analysis, identified clusters using an information-theoretic approach, and named clusters using the patients' pattern of acute and chronic conditions. We assessed one-year overall and preventable hospitalizations, overall and preventable emergency department (ED) visits, and cluster stability.
Six clusters were identified which varied by utilization and stability. The characteristic condition patterns were: 1) pregnancy complications, 2) behavioral health, 3) relatively few conditions, 4) cardio-metabolic disease, and complex illness with relatively 5) low or 6) high resource use. The patients varied by cluster by average ED visits (2.3-11.3), hospitalizations (0.3-2.0), and cluster stability (32-91%).
We concluded that disaggregating subgroups of high-cost high-need patients in a large multi-state Medicaid sample identified clinically distinct clusters of patients who may have unique clinical needs. Segmenting previously identified high-cost high-need populations thus may be a necessary strategy to improve the effectiveness of complex case management programs in Medicaid.
高成本高需求患者通常通过风险或成本阈值来定义，这些阈值将临床不同的亚组汇总为一个单一的“高需求高成本”分类。针对高成本高需求医疗补助患者的利用或改善护理质量的项目收效有限，这可能是由于符合高成本高需求分类的患者存在潜在的临床异质性。
我们的目的是对 2012 年 1 月至 2015 年 5 月期间有资格参加全国复杂病例管理计划的高成本高需求医疗补助患者（N=676161）进行细分，以分解临床不同的亚组。患者必须是联合健康医疗补助受益人中年度支出最高的前 5%。我们使用 K-均值聚类分析，采用信息论方法识别聚类，并使用患者的急性和慢性疾病模式对聚类进行命名。我们评估了一年的总住院和可预防住院、总急诊就诊和可预防急诊就诊以及聚类的稳定性。
确定了六个利用情况和稳定性各不相同的聚类。其特征疾病模式包括：1）妊娠并发症、2）行为健康、3）相对较少的疾病、4）心脏代谢疾病以及复杂疾病伴相对 5）低或 6）高资源利用。根据聚类的平均急诊就诊次数（2.3-11.3）、住院次数（0.3-2.0）和聚类稳定性（32-91%），患者在聚类中存在差异。
我们得出的结论是，在大型多州医疗补助样本中细分高成本高需求患者的亚组，可以确定具有独特临床需求的临床特征明显不同的患者聚类。因此，细分先前确定的高成本高需求人群可能是提高医疗补助复杂病例管理计划有效性的必要策略。
