Western Sydney Local Health District, Australia.
Health Inf Manag. 2018 Jan;47(1):38-45. doi: 10.1177/1833358317721305. Epub 2017 Jul 26.
To examine the validity of routinely collected data in identifying hip fractures (HFs) and to identify factors associated with incorrect coding.
In a prospective cohort study between January 2014 and June 2016, HFs were identified using physician diagnosis and diagnostic imaging and were recorded in a Registry. Records of HFs in the health information exchange (HIE) were identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification/Australian Classification of Health Interventions/Australian Coding Standards codes. New HFs were estimated by episode of care, hospital admission and with an algorithm. Data from the HIE and the Registry were compared.
The number of HFs as the principal diagnosis (PD) recorded by episode (864) was higher than by admission (743), by algorithm (711) and in the Registry (638). The sensitivity was high for all methods (90-93%) but the positive predictive value was lower for episode (68%) than for admission (80%) or algorithm (81%). The number of HFs with surgery recorded in the PD by episode (639), algorithm (630) and in the Registry (623) was similar but higher than by admission (589). The episode and algorithm methods also had higher sensitivity (91-92%) than the admission method (84%) for HFs with surgery. Factors associated with coding errors included subsequent HF, long hospital stay, intracapsular fracture, younger age, male, HF without surgery and death in hospital.
When it is not practical to use the algorithm for regular monitoring of HFs, using PD by admission to estimate total HFs and PD by episode in combination with a procedure code to estimate HFs with surgery can produce robust estimations.
检验常规收集数据在识别髋部骨折（HFs）中的有效性，并确定与错误编码相关的因素。
在 2014 年 1 月至 2016 年 6 月期间进行的前瞻性队列研究中，使用医生诊断和诊断成像来识别 HFs，并将其记录在注册中心。使用国际疾病分类第十版、澳大利亚修改版/澳大利亚健康干预分类/澳大利亚编码标准代码在健康信息交换（HIE）中识别 HFs 的记录。使用医疗事件、住院和算法来估计新的 HFs。将 HIE 和注册中心的数据进行比较。
作为主要诊断（PD）记录的 HFs 数量，通过医疗事件（864 例）高于入院（743 例）、通过算法（711 例）和注册中心（638 例）。所有方法的敏感性均较高（90-93%），但通过医疗事件（68%）的阳性预测值低于入院（80%）或算法（81%）。通过医疗事件（630 例）和注册中心（623 例）记录的 PD 中有手术记录的 HFs 数量与算法（630 例）相似，但高于入院（589 例）。对于有手术记录的 HFs，医疗事件和算法方法的敏感性也高于入院方法（91-92%）。与编码错误相关的因素包括后续 HFs、住院时间长、囊内骨折、年龄较小、男性、无手术的 HFs 和院内死亡。
当使用算法对 HFs 进行常规监测不切实际时，可以使用入院时的 PD 来估计总 HFs，结合程序代码来估计有手术的 HFs，可以得出可靠的估计。
