Ni Yizhao, Bermudez Monica, Kennebeck Stephanie, Liddy-Hicks Stacey, Dexheimer Judith
Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
JMIR Med Inform. 2019 Jul 24;7(3):e14185. doi: 10.2196/14185.
One critical hurdle for clinical trial recruitment is the lack of an efficient method for identifying subjects who meet the eligibility criteria. Given the large volume of data documented in electronic health records (EHRs), it is labor-intensive for the staff to screen relevant information, particularly within the time frame needed. To facilitate subject identification, we developed a natural language processing (NLP) and machine learning-based system, Automated Clinical Trial Eligibility Screener (ACTES), which analyzes structured data and unstructured narratives automatically to determine patients' suitability for clinical trial enrollment. In this study, we integrated the ACTES into clinical practice to support real-time patient screening.
This study aimed to evaluate ACTES's impact on the institutional workflow, prospectively and comprehensively. We hypothesized that compared with the manual screening process, using EHR-based automated screening would improve efficiency of patient identification, streamline patient recruitment workflow, and increase enrollment in clinical trials.
The ACTES was fully integrated into the clinical research coordinators' (CRC) workflow in the pediatric emergency department (ED) at Cincinnati Children's Hospital Medical Center. The system continuously analyzed EHR information for current ED patients and recommended potential candidates for clinical trials. Relevant patient eligibility information was presented in real time on a dashboard available to CRCs to facilitate their recruitment. To assess the system's effectiveness, we performed a multidimensional, prospective evaluation for a 12-month period, including a time-and-motion study, quantitative assessments of enrollment, and postevaluation usability surveys collected from the CRCs.
Compared with manual screening, the use of ACTES reduced the patient screening time by 34% (P<.001). The saved time was redirected to other activities such as study-related administrative tasks (P=.03) and work-related conversations (P=.006) that streamlined teamwork among the CRCs. The quantitative assessments showed that automated screening improved the numbers of subjects screened, approached, and enrolled by 14.7%, 11.1%, and 11.1%, respectively, suggesting the potential of ACTES in streamlining recruitment workflow. Finally, the ACTES achieved a system usability scale of 80.0 in the postevaluation surveys, suggesting that it was a good computerized solution.
By leveraging NLP and machine learning technologies, the ACTES demonstrated good capacity for improving efficiency of patient identification. The quantitative assessments demonstrated the potential of ACTES in streamlining recruitment workflow and improving patient enrollment. The postevaluation surveys suggested that the system was a good computerized solution with satisfactory usability.
临床试验招募面临的一个关键障碍是缺乏一种有效的方法来识别符合入选标准的受试者。鉴于电子健康记录（EHR）中记录的数据量巨大，工作人员筛选相关信息的工作量很大，尤其是在所需的时间范围内。为便于识别受试者，我们开发了一种基于自然语言处理（NLP）和机器学习的系统——自动临床试验资格筛选器（ACTES），该系统可自动分析结构化数据和非结构化叙述，以确定患者是否适合参加临床试验。在本研究中，我们将ACTES整合到临床实践中，以支持实时患者筛选。
本研究旨在前瞻性、全面地评估ACTES对机构工作流程的影响。我们假设，与人工筛选过程相比，使用基于EHR的自动筛选将提高患者识别效率，简化患者招募工作流程，并增加临床试验的入组人数。
ACTES完全整合到辛辛那提儿童医院医疗中心儿科急诊科（ED）临床研究协调员（CRC）的工作流程中。该系统持续分析当前ED患者的EHR信息，并推荐临床试验的潜在候选人。相关患者资格信息实时显示在CRC可访问的仪表板上，以方便他们进行招募。为评估该系统的有效性，我们进行了为期12个月的多维度前瞻性评估，包括时间与动作研究、入组的定量评估，以及从CRC收集的评估后可用性调查。
与人工筛选相比，使用ACTES将患者筛选时间减少了34%（P<.001）。节省的时间被重新分配到其他活动中，如与研究相关的行政任务（P=.03）和与工作相关的对话（P=.006），这些活动简化了CRC之间的团队协作。定量评估表明，自动筛选分别将筛选、接触和入组的受试者数量提高了14.7%、11.1%和11.1%，表明ACTES在简化招募工作流程方面具有潜力。最后，ACTES在评估后调查中的系统可用性量表得分为80.0，表明它是一个良好的计算机化解决方案。
通过利用NLP和机器学习技术，ACTES在提高患者识别效率方面表现出良好的能力。定量评估证明了ACTES在简化招募工作流程和提高患者入组率方面的潜力。评估后调查表明，该系统是一个具有令人满意的可用性的良好计算机化解决方案。
