Insufficient participant recruitment is one of the leading causes of early study termination and wasted research resources. Delayed or poor recruitment of target participants in stipulated time remains an enduring problem that leads to increased study costs and reduced power of clinical trials. Efficient identification and recruitment of eligible participants is considered a key factor to the success of clinical trials and one of its major challenges throughout the last decades. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.Ĭlinical trials are essential to advance clinical health care and evidence-based medicine. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. Only a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains. Resultsīased on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach. We used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal’s data repository that are pre-annotated with Unified Medical Language System (UMLS). Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment. The aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. ![]() This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Screening for eligible patients continues to pose a great challenge for many clinical trials.
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