Abstract：
BACKGROUND: The Veterans Affairs Surgical Quality Improvement Program (VASQIP) trains surgical quality nurses (SQNs) at each Veterans Affairs (VA) hospital to extract or verify 187 variables from the medical record for all cardiac surgical cases. For ten preoperative laboratory values, VASQIP has a semiautomated (SA) system in which local lab values are automatically extracted, verified by SQNs, and lab values recorded at other VA facilities are manually extracted. The objective of this study was to develop and validate a method to automate the extraction of these ten preoperative laboratory values and compare results with the current SA method.
METHODS: We developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness.
RESULTS: For surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries.
CONCLUSIONS: Although SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.
METHODS: We developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness.
RESULTS: For surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries.
CONCLUSIONS: Although SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.
摘要：
背景：退伍军人事务外科质量改进计划（VASQIP）对每个退伍军人事务（VA）医院的外科质量护士（SQN）进行培训，以从所有心脏外科病例的医疗记录中提取或验证187个变量。对于十个术前实验室值，VASQIP具有半自动（SA）系统，在该系统中自动提取本地实验室值，由SQN验证，并手动提取在其他VA设施记录的实验室值。这项研究的目的是开发和验证一种方法来自动提取这十个术前实验室值，并将结果与当前的SA方法进行比较。
方法：我们开发了使用逻辑观察标识符名称和代码从VA公司数据仓库中提取十个术前实验室值和测量日期的方法。自动(A)和SA信息提取在协议方面进行了比较，符合数据定义，接近手术，和不幸。
结果：对于具有A和SA实验室值的手术，十个变量的组内相关系数在0.90到0.98之间。对于几个变量，A方法导致数据丢失率低得多(例如，高密度脂蛋白的缺失数据为2.4％和22.5％），并消除了超出日期范围的条目。
结论：尽管SQN提取的数据被广泛认为是国家外科质量改进计划的金标准，完全自动化提取实验室值可能有优势，包括与SASQN提取或验证值的高度一致性，以及较低的误差率和超出日期的数据。
方法：我们开发了使用逻辑观察标识符名称和代码从VA公司数据仓库中提取十个术前实验室值和测量日期的方法。自动(A)和SA信息提取在协议方面进行了比较，符合数据定义，接近手术，和不幸。
结果：对于具有A和SA实验室值的手术，十个变量的组内相关系数在0.90到0.98之间。对于几个变量，A方法导致数据丢失率低得多(例如，高密度脂蛋白的缺失数据为2.4％和22.5％），并消除了超出日期范围的条目。
结论：尽管SQN提取的数据被广泛认为是国家外科质量改进计划的金标准，完全自动化提取实验室值可能有优势，包括与SASQN提取或验证值的高度一致性，以及较低的误差率和超出日期的数据。
