Ellenbogen Michael I, Feldman Leonard S, Prichett Laura, Zhou Junyi, Brotman Daniel J
Department of Medicine, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Departments of Medicine and Pediatrics, 1500 Johns Hopkins School of Medicine , Baltimore, MD, USA.
Diagnosis (Berl). 2024 Apr 22;11(3):303-311. doi: 10.1515/dx-2023-0184. eCollection 2024 Aug 1.
Low-value care is associated with increased healthcare costs and direct harm to patients. We sought to develop and validate a simple diagnostic intensity index (DII) to quantify hospital-level diagnostic intensity, defined by the prevalence of advanced imaging among patients with selected clinical diagnoses that may not require imaging, and to describe hospital characteristics associated with high diagnostic intensity.
We utilized State Inpatient Database data for inpatient hospitalizations with one or more pre-defined discharge diagnoses at acute care hospitals. We measured receipt of advanced imaging for an associated diagnosis. Candidate metrics were defined by the proportion of inpatients at a hospital with a given diagnosis who underwent associated imaging. Candidate metrics exhibiting temporal stability and internal consistency were included in the final DII. Hospitals were stratified according to the DII, and the relationship between hospital characteristics and DII score was described. Multilevel regression was used to externally validate the index using pre-specified Medicare county-level cost measures, a Dartmouth Atlas measure, and a previously developed hospital-level utilization index.
This novel DII, comprised of eight metrics, correlated in a dose-dependent fashion with four of these five measures. The strongest relationship was with imaging costs (odds ratio of 3.41 of being in a higher DII tertile when comparing tertiles three and one of imaging costs (95 % CI 2.02-5.75)).
A small set of medical conditions and related imaging can be used to draw meaningful inferences more broadly on hospital diagnostic intensity. This could be used to better understand hospital characteristics associated with low-value care.
低价值医疗与医疗成本增加及对患者的直接伤害相关。我们试图开发并验证一种简单的诊断强度指数(DII),以量化医院层面的诊断强度,该指数由可能不需要影像学检查的特定临床诊断患者中高级影像学检查的患病率来定义,并描述与高诊断强度相关的医院特征。
我们利用州住院数据库中急性护理医院有一项或多项预定义出院诊断的住院患者数据。我们测量了相关诊断的高级影像学检查的接受情况。候选指标由医院中患有特定诊断并接受相关影像学检查的住院患者比例来定义。表现出时间稳定性和内部一致性的候选指标被纳入最终的DII。根据DII对医院进行分层,并描述医院特征与DII评分之间的关系。使用多水平回归,通过预先指定的医疗保险县级成本指标、达特茅斯地图集指标和先前开发的医院层面利用指数对该指数进行外部验证。
这个由八个指标组成的新型DII与这五项指标中的四项呈剂量依赖性相关。最强的关系是与影像学成本相关(比较影像学成本的三分位数3和1时,处于较高DII三分位数的比值比为3.41(95%CI 2.02 - 5.75))。
一小部分医疗状况及相关影像学检查可用于更广泛地对医院诊断强度得出有意义的推论。这可用于更好地理解与低价值医疗相关的医院特征。