Australian Centre for Economic Research on Health, School of Medicine, University of Queensland, Brisbane, Australia.
BMC Med Inform Decis Mak. 2009 Dec 1;9:48. doi: 10.1186/1472-6947-9-48.
The use of routine hospital data for understanding patterns of adverse outcomes has been limited in the past by the fact that pre-existing and post-admission conditions have been indistinguishable. The use of a 'Present on Admission' (or POA) indicator to distinguish pre-existing or co-morbid conditions from those arising during the episode of care has been advocated in the US for many years as a tool to support quality assurance activities and improve the accuracy of risk adjustment methodologies. The USA, Australia and Canada now all assign a flag to indicate the timing of onset of diagnoses. For quality improvement purposes, it is the 'not-POA' diagnoses (that is, those acquired in hospital) that are of interest.
Our objective was to develop an algorithm for assessing the validity of assignment of 'not-POA' flags. We undertook expert review of the International Classification of Diseases, 10th Revision, Australian Modification (ICD-10-AM) to identify conditions that could not be plausibly hospital-acquired. The resulting computer algorithm was tested against all diagnoses flagged as complications in the Victorian (Australia) Admitted Episodes Dataset, 2005/06. Measures reported include rates of appropriate assignment of the new Australian 'Condition Onset' flag by ICD chapter, and patterns of invalid flagging.
Of 18,418 diagnosis codes reviewed, 93.4% (n = 17,195) reflected agreement on status for flagging by at least 2 of 3 reviewers (including 64.4% unanimous agreement; Fleiss' Kappa: 0.61). In tests of the new algorithm, 96.14% of all hospital-acquired diagnosis codes flagged were found to be valid in the Victorian records analysed. A lower proportion of individual codes was judged to be acceptably flagged (76.2%), but this reflected a high proportion of codes used <5 times in the data set (789/1035 invalid codes).
An indicator variable about the timing of occurrence of diagnoses can greatly expand the use of routinely coded data for hospital quality improvement programmes. The data-cleaning instrument developed and tested here can help guide coding practice in those health systems considering this change in hospital coding. The algorithm embodies principles for development of coding standards and coder education that would result in improved data validity for routine use of non-POA information.
过去，由于无法区分预先存在的和入院后的条件，常规医院数据在了解不良结果模式方面的应用受到限制。多年来，美国一直提倡使用“入院时存在”（或 POA）指标来区分预先存在的或合并症与在护理期间发生的疾病，作为支持质量保证活动和提高风险调整方法准确性的工具。美国、澳大利亚和加拿大现在都分配了一个标志来表示诊断的发病时间。出于质量改进的目的，感兴趣的是“非 POA”诊断（即那些在医院获得的诊断）。
我们的目标是开发一种评估“非 POA”标志分配有效性的算法。我们对国际疾病分类，第 10 次修订版，澳大利亚修改版（ICD-10-AM）进行了专家审查，以确定那些不可能在医院获得的条件。由此产生的计算机算法在 2005/06 年维多利亚（澳大利亚）入院事件数据集的所有被标记为并发症的诊断中进行了测试。报告的指标包括按 ICD 章节对新的澳大利亚“疾病发病”标志的适当分配率，以及无效标志的模式。
在审查的 18418 个诊断代码中，有 93.4%（n=17195）至少有 3 位审查员中的 2 位同意标志状态（包括 64.4%的一致意见；Fleiss' Kappa：0.61）。在对新算法的测试中，在所分析的维多利亚记录中，发现所有标记为医院获得的诊断代码中有 96.14%是有效的。个别代码的可接受标志比例较低（76.2%），但这反映了数据集中使用次数较少的代码比例较高（789/1035 无效代码）。
关于诊断发生时间的指示变量可以极大地扩展常规编码数据在医院质量改进计划中的使用。这里开发和测试的数据清理工具可以帮助指导那些考虑改变医院编码的卫生系统的编码实践。该算法体现了制定编码标准和编码员教育的原则，这将提高常规使用非 POA 信息的数据有效性。
