Association of Social Determinants With Children's Hospitals' Preventable Readmissions Performance
Association of Social Determinants With Children's Hospitals' Preventable Readmissions Performance
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DOI：
10.1001/jamapediatrics.2015.4440
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发表时间：
2016-04-01
期刊：
影响因子：
26.1
通讯作者：
Shah, Samir S.
中科院分区：
文献类型：
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作者：
Sills, Marion R.;Hall, Matt;Shah, Samir S.
重要的是，绩效衡量风险调整对面临重新入院按绩效付费(P4P)措施的巨额经济处罚的医院利益相关者非常感兴趣。尽管有证据表明社会健康决定因素(SDH)与患者再入院风险之间存在关联，但SDH风险调整对医院再入院P4P惩罚的影响还没有被很好地了解。目的确定对常见SDH措施的风险调整是否会影响全国儿童医院队列中基于再入院P4P惩罚状态。设计、设置和参与者在2013年对儿科健康信息系统数据库中43家独立儿童医院进行的回顾性队列研究。我们评估了2013年的医院出院情况，这些医院符合3M Health Information Systems 2013年可能可预防的重新入院措施的标准。这项分析是从2015年7月到2015年8月进行的。EXPOSURES两个风险调整模型：根据疾病严重程度进行调整的基线模型和根据疾病严重程度和以下4个SDH变量进行调整的SDH增强模型：种族、民族、支付者和患者所在家庭邮政编码的家庭收入中位数。主要结果和衡量医院潜在可预防再入院惩罚状态的变化(即，医院是否超过惩罚阈值的变化)使用观察到的与预期的潜在可预防可预防的可读性比率1.0作为惩罚阈值。符合纳入标准的43家医院179,400家医院出院的结果SDH变量的医院水平百分比的中位数(四分位数范围[IQR])为39.2%非白人(n=71300；IQR，28.6%-54.6%)，17.9%西班牙裔(n=32060；IQR，6.7%-37.0%)，58.7%公共保险(n=106116；IQR，50.4%-67.8%)。患者家庭邮政编码所在医院的家庭收入中位数为40 674美元(IQR为35 912-46 190美元)。与基线模型相比，SDH的调整导致3家医院在15天窗口内的惩罚状态发生变化(2家不再超过惩罚阈值，1家新被处罚)，5家医院在30天窗口内(3家不再超过惩罚阈值，2家新被处罚)。结论SDH的相关风险调整改变了基于再入院的P4P措施的医院的惩罚状态。如果不调整SDH的P4P措施，医院可能会受到部分与医院护理质量以外的患者SDH因素有关的处罚。
IMPORTANCE Performance-measure risk adjustment is of great interest to hospital stakeholders who face substantial financial penalties from readmissions pay-for-performance (P4P) measures. Despite evidence of the association between social determinants of health (SDH) and individual patient readmission risk, the effect of risk adjusting for SDH on readmissions P4P penalties to hospitals is not well understood.OBJECTIVE To determine whether risk adjustment for commonly available SDH measures affects the readmissions-based P4P penalty status of a national cohort of children's hospitals.DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of 43 free-standing children's hospitals within the Pediatric Health Information System database in the calendar year 2013. We evaluated hospital discharges from 2013 that met criteria for 3M Health Information Systems' potentially preventable readmissions measure for calendar year 2013. The analysis was conducted from July 2015 to August 2015.EXPOSURES Two risk-adjustment models: a baseline model adjusted for severity of illness and an SDH-enhanced model that adjusted for severity of illness and the following 4 SDH variables: race, ethnicity, payer, and median household income for the patient's home zip code.MAIN OUTCOMES AND MEASURES Change in a hospital's potentially preventable readmissions penalty status (ie, change in whether a hospital exceeded the penalty threshold) using an observed-to-expected potentially preventable readmissions ratio of 1.0 as a penalty threshold.RESULTS For the 179 400 hospital discharges from the 43 hospitals meeting inclusion criteria, median (interquartile range [IQR]) hospital-level percentages for the SDH variables were 39.2% nonwhite (n = 71 300; IQR, 28.6%-54.6%), 17.9% Hispanic (n = 32 060; IQR, 6.7%-37.0%), and 58.7% publicly insured (n = 106 116; IQR, 50.4%-67.8%). The hospital median household income for the patient's home zip code was $ 40 674 (IQR, $ 35 912-$ 46 190). When compared with the baseline model, adjustment for SDH resulted in a change in penalty status for 3 hospitals within the 15-day window (2 were no longer above the penalty threshold and 1 was newly penalized) and 5 hospitals within the 30-day window (3 were no longer above the penalty threshold and 2 were newly penalized).CONCLUSIONS AND RELEVANCE Risk adjustment for SDH changed hospitals' penalty status on a readmissions-based P4P measure. Without adjusting P4P measures for SDH, hospitals may receive penalties partially related to patient SDH factors beyond the quality of hospital care.