Goodwin James S, Li Shuang, Zhou Jie, Graham James E, Karmarkar Amol, Ottenbacher Kenneth
Department of Medicine, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.
Sealy Center on Aging, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.
BMC Health Serv Res. 2017 May 30;17(1):376. doi: 10.1186/s12913-017-2318-9.
To compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data Set (MDS), and Texas Medicaid data from 2011-2013.
We used Medicare Part A and B and MDS data either alone or in combination to identify LTC nursing home stays by three methods. One method used Medicare Part A and B data; one method used Medicare Part A and MDS data; and the current CMS method used MDS data alone. We validated each method against Texas 2011 Medicare-Medicaid linked data for those with dual eligibility.
Using Medicaid data as a gold standard, all three methods had sensitivities > 92% to identify LTC nursing home stays of more than 100 days in duration. The positive predictive value (PPV) of the method that used both MDS and Medicare Part A data was 84.65% compared to 78.71% for the CMS method and 66.45% for the method using Part A and B Medicare. When the patient population was limited to those who also had a SNF stay, the PPV for identifying LTC nursing home was highest for the method using Medicare plus MDS data (88.1%).
Using both Medicare and MDS data to identify LTC stays will lead to more accurate attribution of CMS nursing home quality indicators.
为了将用于识别长期护理(LTC)养老院入住情况(与熟练护理机构(SNFs)的入住情况相区分)的不同方法与医疗保险和医疗补助服务中心(CMS)目前使用的方法进行比较。我们使用了2011 - 2013年的全国和德克萨斯州医疗保险理赔数据、最低数据集(MDS)以及德克萨斯州医疗补助数据。
我们单独或组合使用医疗保险A部分和B部分以及MDS数据,通过三种方法识别长期护理养老院入住情况。一种方法使用医疗保险A部分和B部分数据;一种方法使用医疗保险A部分和MDS数据;当前CMS方法仅使用MDS数据。我们针对具有双重资格的人群,根据德克萨斯州2011年医疗保险 - 医疗补助关联数据对每种方法进行了验证。
以医疗补助数据作为金标准,所有三种方法识别持续时间超过100天的长期护理养老院入住情况的敏感性均> 92%。使用MDS和医疗保险A部分数据的方法的阳性预测值(PPV)为84.65%,相比之下,CMS方法为78.71%,使用医疗保险A部分和B部分的方法为66.45%。当患者群体限于那些也有熟练护理机构入住经历的人时,使用医疗保险加MDS数据的方法识别长期护理养老院的PPV最高(88.1%)。
使用医疗保险和MDS数据来识别长期护理入住情况将导致医疗保险养老院质量指标的归因更准确。