A Time-and-Motion Study of Clinical Trial Eligibility Screening in a Pediatric Emergency Department.

OBJECTIVE: Challenges with efficient patient recruitment including sociotechnical barriers for clinical trials are major barriers to the timely and efficacious conduct of translational studies. We conducted a time-and-motion study to investigate the workflow of clinical trial enrollment in a pediatric emergency department. METHODS: We observed clinical research coordinators during 3 clinically staffed shifts. One clinical research coordinator was shadowed at a time. Tasks were marked in 30-second intervals and annotated to include patient screening, patient contact, performing procedures, and physician contact. Statistical analysis was conducted on the patient enrollment activities. RESULTS: We conducted fifteen 120-minute observations from December 12, 2013, to January 3, 2014 and shadowed 8 clinical research coordinators. Patient screening took 31.62% of their time, patient contact took 18.67%, performing procedures took 17.6%, physician contact was 1%, and other activities took 31.0%. CONCLUSIONS: Screening patients for eligibility constituted the most time. Automated screening methods could help reduce this time. The findings suggest improvement areas in recruitment planning to increase the efficiency of clinical trial enrollment.

An end-to-end hybrid algorithm for automated medication discrepancy detection.

BACKGROUND: In this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients' discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data. METHODS: We collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children's Hospital Medical Center between 1/1/2010 and 12/31/2013. A double-annotated, gold-standard set of medication reconciliation data was created for this collection. We then developed a hybrid algorithm consisting of three processes: (1) a ML algorithm to identify medication entities from clinical notes, (2) a rule-based method to link medication names with their attributes, and (3) a NLP-based, hybrid approach to match medications with structured prescriptions in order to detect medication discrepancies. The performance was validated on the gold-standard medication reconciliation data, where precision (P), recall (R), F-value (F) and workload were assessed. RESULTS: The hybrid algorithm achieved 95.0%/91.6%/93.3% of P/R/F on medication entity detection and 98.7%/99.4%/99.1% of P/R/F on attribute linkage. The medication matching achieved 92.4%/90.7%/91.5% (P/R/F) on identifying matched medications in the gold-standard and 88.6%/82.5%/85.5% (P/R/F) on discrepant medications. By combining all processes, the algorithm achieved 92.4%/90.7%/91.5% (P/R/F) and 71.5%/65.2%/68.2% (P/R/F) on identifying the matched and the discrepant medications, respectively. The error analysis on algorithm outputs identified challenges to be addressed in order to improve medication discrepancy detection. CONCLUSION: By leveraging ML and NLP technologies, an end-to-end, computerized algorithm achieves promising outcome in reconciling medications between clinical notes and discharge prescriptions.

Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.

The objective of this study was to determine whether the Food and Drug Administration's Adverse Event Reporting System (FAERS) data set could serve as the basis of automated electronic health record (EHR) monitoring for the adverse drug reaction (ADR) subset of adverse drug events. We retrospectively collected EHR entries for 71 909 pediatric inpatient visits at Cincinnati Children's Hospital Medical Center. Natural language processing (NLP) techniques were used to identify positive diseases/disorders and signs/symptoms (DDSSs) from the patients' clinical narratives. We downloaded all FAERS reports submitted by medical providers and extracted the reported drug-DDSS pairs. For each patient, we aligned the drug-DDSS pairs extracted from their clinical notes with the corresponding drug-DDSS pairs from the FAERS data set to identify Drug-Reaction Pair Sentences (DRPSs). The DRPSs were processed by NLP techniques to identify ADR-related DRPSs. We used clinician annotated, real-world EHR data as reference standard to evaluate the proposed algorithm. During evaluation, the algorithm achieved promising performance and showed great potential in identifying ADRs accurately for pediatric patients.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.

OBJECTIVES: (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials. DATA AND METHODS: We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial-patient matches and a reference standard of historical trial-patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed. RESULTS: Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial-patient matches. DISCUSSION AND CONCLUSION: By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.