School of Nursing, Columbia University, New York, New York, USA.
Department of Neurology, Columbia University, New York, New York, USA.
J Am Med Inform Assoc. 2021 Dec 28;29(1):197-206. doi: 10.1093/jamia/ocab228.
We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process.
Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards of quality for reporting systematic reviews, a protocol for study eligibility was developed a priori and registered in the PROSPERO database. Using predetermined inclusion criteria, studies published from database inception through February 2021 were identified from 5 databases. The Joanna Briggs Institute Critical Appraisal Checklist for Quasi-experimental Studies was adapted to determine the study quality and the risk of bias of the included articles.
Eleven studies representing 8 unique NLP systems met the inclusion criteria. These studies demonstrated moderate study quality and exhibited heterogeneity in the study design, setting, and intervention type. All 11 studies evaluated the NLP system's performance for identifying eligible participants; 7 studies evaluated the system's impact on time efficiency; 4 studies evaluated the system's impact on workload; and 2 studies evaluated the system's impact on recruitment.
NLP systems in clinical research eligibility prescreening are an understudied but promising field that requires further research to assess its impact on real-world adoption. Future studies should be centered on continuing to develop and evaluate relevant NLP systems to improve enrollment into clinical studies.
Understanding the role of NLP systems in improving eligibility prescreening is critical to the advancement of clinical research recruitment.
我们进行了一项系统评价，以评估自然语言处理（NLP）系统在提高临床研究招募过程中资格筛选准确性和效率方面的效果。
根据系统评价和荟萃分析的首选报告项目（PRISMA）质量标准制定了研究资格的方案，并在 PROSPERO 数据库中进行了预先注册。使用预定的纳入标准，从 5 个数据库中确定了从数据库开始到 2021 年 2 月发表的研究。采用 Joanna Briggs 研究所的准实验研究批判性评估清单来确定纳入文章的研究质量和偏倚风险。
有 11 项研究代表 8 个独特的 NLP 系统符合纳入标准。这些研究的研究质量为中等，研究设计、设置和干预类型存在异质性。所有 11 项研究都评估了 NLP 系统识别合格参与者的性能；7 项研究评估了系统对时间效率的影响；4 项研究评估了系统对工作量的影响；2 项研究评估了系统对招募的影响。
临床研究资格筛选中的 NLP 系统是一个研究不足但有前途的领域，需要进一步研究来评估其在实际应用中的影响。未来的研究应集中于继续开发和评估相关的 NLP 系统，以提高临床试验的入组率。
了解 NLP 系统在改善资格筛选中的作用对于临床研究招募的进展至关重要。
