Background 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. Objective 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. Methods 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. Results 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. 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 enrollment. The postevaluation surveys suggested that the sy...

The adult mammalian brain contains at least two populations of neural stem/precursor cells (NPCs) which are set a side during development for the function of repair and replacement throughout life (Lim & Alvarez-Bullya, 2016;Obernier & Alvarez-Bullya, 2019). One population is present in the ventricular-subventricular zone (SVZ) on the walls of the lateral ventricle. The neuroblasts generated by these NPCs migrate to the olfactory bulbs which are a considerable distance rostrally by chain migration along a course called the rostral migratory stream (RMS). There they become incorporated into the olfactory circuitry as functional interneurons contributing to fine odor discrimination. It is estimated that many thousands of new olfactory neurons are produced each day in the rodent brain.The second population of NSCs is present in the dentate gyrus of the hippocampus and pass through a series of developmental stages to become incorporated into the hippocampal circuitry as dentate granule neurons (Goncalves et al., 2016). Here they are involved in learning, memory and pattern separation.Both populations of NSCs have been investigated with the aim of improving the human condition. In the hippocampus, for example, methods to improve their rate of production, rate of differentiation or slowing their rate of loss during ageing are of considerable interest since if this were to translate to humans the implications for learning and memory in elderly populations or in neurological disease could be highly significant. In this regard, several growth factors and signaling molecules such as Wnt, Shh, BMP, and Notch play crucial roles in the promotion of NSC

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.