Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, USA; Department of Pediatrics, Vanderbilt University Medical Center, USA; School of Nursing, Vanderbilt University, Nashville, TN, USA.
J Biomed Inform. 2019 May;93:103142. doi: 10.1016/j.jbi.2019.103142. Epub 2019 Mar 7.
It remains unclear how to incorporate terminology changes, such as the International Classification of Disease (ICD) transition from ICD-9 to ICD-10, into established automated healthcare quality metrics.
To evaluate whether general equivalence mapping (GEM) can apply ICD-9 based metrics to ICD-10 patient data. To develop and validate novel ICD-10 reference codesets.
Retrospective analysis for eleven Choosing Wisely (CW) metrics was performed using three scripted algorithms on an institutional clinical data warehouse. ICD-10 data were compared against published ICD-9 based metric definitions using two equivalence mapping algorithms. A third algorithm implemented novel reference ICD-10 codes matching the original ICD-9 codes' intent for comparison with patient ICD-10 data.
All adult patients seen at Vanderbilt University Medical Center, April - September 2016.
The prevalence of eleven CW services during the six-month period.
The three algorithms found similar prevalence of avoidable CW services, with an unweighted-mean of 8.4% (range: 0.16-65%), or approximately 20,000 CW services out of 240,000 potential cases in 515,406 unique patients. The algorithms' median sensitivity was 0.80 (interquartile range: 0.75-0.95), median specificity was 0.88 (IQR: 0.77-0.94), and median Rand accuracy was 0.84 (IQR: 0.79-0.89). The attributed waste of these eleven services for the period ranged from $871,049 to $951,829 between methods. Accuracy assessment demonstrated that the GEM-based methods suffered recall losses for metrics requiring multistep mapping due to incompleteness, while novel ICD-10 metric definitions avoided these challenges.
Comprehensive mapping enables use of legacy metrics across ICD generations, but requires computational complexity that can be avoided with novel ICD-10 based metric definitions. Variation in the dollars attributed to waste due to ICD mapping introduces ambiguity that may affect quality-based reimbursement.
目前尚不清楚如何将术语更改（例如国际疾病分类（ICD）从 ICD-9 到 ICD-10 的过渡）纳入既定的自动化医疗保健质量指标。
评估一般等效映射（GEM）是否可以将基于 ICD-9 的指标应用于 ICD-10 患者数据。开发和验证新的 ICD-10 参考代码集。
使用三个脚本算法对机构临床数据仓库中的十一个明智选择（CW）指标进行回顾性分析。使用两种等效映射算法将 ICD-10 数据与已发布的基于 ICD-9 的指标定义进行比较。第三个算法实现了新颖的参考 ICD-10 代码，这些代码与原始 ICD-9 代码的意图相匹配，用于与患者的 ICD-10 数据进行比较。
2016 年 4 月至 9 月期间在范德比尔特大学医学中心就诊的所有成年患者。
六个月期间十一项 CW 服务的流行程度。
三种算法发现可避免的 CW 服务的流行程度相似，未加权平均值为 8.4%（范围：0.16-65%），或者在 515406 个唯一患者中的 240000 个潜在病例中约有 20000 个 CW 服务。算法的中位灵敏度为 0.80（四分位距：0.75-0.95），中位特异性为 0.88（四分位距：0.77-0.94），中位 Rand 准确度为 0.84（四分位距：0.79-0.89）。在此期间，这十一项服务的归因于浪费的费用在方法之间从 871049 美元到 951829 美元不等。准确性评估表明，基于 GEM 的方法由于不完整而在需要多步骤映射的指标中遭受召回损失，而新的 ICD-10 指标定义则避免了这些挑战。
全面映射可使 ICD 代际之间能够使用传统指标，但需要计算复杂性，而基于新的 ICD-10 指标定义则可以避免这种复杂性。由于 ICD 映射导致归因于浪费的美元变化带来了歧义，这可能会影响基于质量的报销。
