Hernandez Adrian V, Roman Yuani M, White C Michael
School of Pharmacy, University of Connecticut Evidence-based Practice Center, Storrs, CT, USA.
Department of Pharmacy, Hartford Hospital, Hartford, CT, USA.
J Gen Intern Med. 2020 Nov;35(Suppl 2):802-807. doi: 10.1007/s11606-020-06098-1. Epub 2020 Aug 17.
The Agency for Healthcare Research and Quality (AHRQ) could devote resources to collate and assess quality improvement studies to support learning health systems (LHS) but there is no reliable data on the consistency of data extraction for important criteria.
We identified quality improvement studies and evaluated the consistency of data extraction from two experienced independent reviewers at three time points: baseline, first revision (where explicit instructions for each criterion were created), and final revision (where the instructions were revised). Six investigators looked at the data extracted by the two systematic reviewers and determined the extent of similarity on a scale of 0 to 10 (where 0 represented no similarity and 10 perfect similarity). There were 42 assessments for baseline, 42 assessments for the first revision, and 42 assessments for the final revision. We asked two LHS participants to assess the relative value of our criteria.
The consistency of extraction improved from 1.17 ± 1.85 at baseline to 6.07 ± 2.76 after revision 1 (P < 0.001) and to 6.81 ± 1.94 out of 10 for the final revision (P < 0.001). However, the final revision was not significantly improved over the first revision (P = 0.14). One key informant rated the difficulty in finding and using quality improvement studies a 6 (moderately difficult) while the other a 4 (moderately difficult). When asked how valuable it would be if AHRQ found and collated the demographic information about the health systems and the interventions used in published quality improvement studies, they rated it a 9 (highly valuable) and a 6 (moderately valuable).
Creating explicit instructions for extracting data for quality improvement studies helps enhance the consistency of data extraction. This is important because it is difficult for LHS to vet these quality improvement studies on their own and they would value AHRQ's support in that regard.
医疗保健研究与质量局(AHRQ)可以投入资源整理和评估质量改进研究,以支持学习型卫生系统(LHS),但对于重要标准的数据提取一致性,尚无可靠数据。
我们确定了质量改进研究,并在三个时间点评估了两名经验丰富的独立评审员的数据提取一致性:基线期、首次修订(针对每个标准创建明确说明)和最终修订(说明进行了修订)。六名研究人员查看了两名系统评审员提取的数据,并在0至10的量表上确定相似程度(其中0表示无相似性,10表示完全相似)。基线期有42次评估,首次修订有42次评估,最终修订有42次评估。我们请两名LHS参与者评估我们标准的相对价值。
提取一致性从基线期的1.17±1.85提高到首次修订后的6.07±2.76(P<0.001),最终修订时达到6.81±1.94(满分10分)(P<0.001)。然而,最终修订与首次修订相比,没有显著改善(P = 0.14)。一名关键信息提供者将查找和使用质量改进研究的难度评为6分(中等困难),另一名评为4分(中等困难)。当被问及AHRQ查找并整理已发表质量改进研究中有关卫生系统的人口统计学信息和所采用干预措施的价值时,他们分别将其评为9分(非常有价值)和6分(中等有价值)。
为质量改进研究的数据提取创建明确说明有助于提高数据提取的一致性。这很重要,因为LHS自身难以审核这些质量改进研究,他们会重视AHRQ在这方面的支持。