JMIR Publications

ABSTRACT

Background:

One critical hurdle for clinical trial recruitment is the lack of an efficient method for identifying subjects who meet 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 analyzed 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.

Objective:

Our objective was to evaluate the 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.

Methods:

The ACTES was fully integrated into the clinical research coordinator (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 post-evaluation usability surveys collected from the CRCs.

Results:

Compared to manual screening, use of ACTES reduced the patient screening time by 34% (P<0.0001). The saved time was redirected to other work-related activities that streamlined teamwork among the CRCs (P <0.05). The quantitative assessments showed that automated screening improved the numbers of subjects screened, approached and enrolled by more than 10%, suggesting the potential of ACTES in streamlining recruitment workflow. The post-evaluation surveys indicated that the system was a good computerized solution with satisfactory usability.

Conclusions:

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 approach and enrollment. The post-evaluation surveys suggested that the system was a good computerized solution with satisfactory usability.