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首页> 外文期刊>BMC Medical Informatics and Decision Making >Development of a validation algorithm for 'present on admission' flagging
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Development of a validation algorithm for 'present on admission' flagging

机译：开发一种“在场入场”标记的验证算法

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Background 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. Methods 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. Results 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 Conclusion 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.
机译：背景技术过去，由于之前存在的和入院后的状况难以区分，因此使用常规医院数据来了解不良结局的模式受到限制。在美国，多年来一直提倡使用“入院时现况”（POA）指标来区分既往病情或共病病情与护理期间发生的病情，作为支持质量保证活动的工具。提高风险调整方法的准确性。现在，美国，澳大利亚和加拿大都分配了一个标志，以指示开始诊断的时间。为了提高质量，关注的是“非POA”诊断（即在医院获得的诊断）。方法我们的目标是开发一种算法来评估“非POA”标志分配的有效性。我们对《国际疾病分类》（澳大利亚修订版，第10版）进行了专家审查，以鉴定出医院似乎无法获得的疾病。测试了生成的计算机算法，对2005/06维多利亚州（澳大利亚）入场事件数据集中标记为并发症的所有诊断进行了测试。报告的措施包括ICD章节对新的澳大利亚“条件发作”标志的适当分配率以及无效标志的模式。结果在审查的18,418个诊断代码中，有93.4％（n = 17,195）反映了至少三分之二的评论者对举报状态的同意（包括64.4％的一致同意; Fleiss'Kappa：0.61）。在新算法的测试中，在分析的维多利亚记录中发现，标记出的所有医院获得的诊断代码中有96.14％是有效的。单个代码的比例较低，被认为是可以接受的，占76.2％，但这反映出所使用的代码比例高结论结论有关诊断发生时间的指标变量可以大大扩展常规编码数据在改善医院质量中的应用程式。考虑到医院编码的这种变化，此处开发和测试的数据清理仪器可以帮助指导那些卫生系统中的编码实践。该算法体现了开发编码标准和编码者培训的原理，这将提高常规使用非POA信息的数据有效性。

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