MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. AVAILABILITY AND IMPLEMENTATION: Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

Scoliosis is a spinal curvature that most frequently affects adolescents. Posterior spinal fusion surgery is required to correct the deformity in patients with severe scoliosis. Surgeons frequently use radiographic measurements and patient reported outcomes to aid in surgical treatment and monitor patient rehabilitation. Shriners Hospitals for Children is a large healthcare system caring for a significant percentage of pediatric patients with scoliosis. Surgeons from SHC-Greenville and SHC-Lexington have recorded data from more than 1,000 individual scoliosis patients. However, these collected data are usually dispersed across individual healthcare sites, necessitating the development of an integrated clinical data repository for data sharing and management. In this paper, we established a standardized research data repository with FHIR resources to harmonize multi-modal patient data from multiple clinical sites. Additionally, a FHIR-compliant application with a web-based user interface was prototyped to enable clinicians and researchers to access scoliosis patient data within our integrated and standardized research repository. Patient cohort definitions can be used to search these records using the same FHIR application. This standardized data-sharing framework and healthcare information system can be applied to multi-site and multimodality studies for clinical and research purposes, with the ultimate goal of improving the quality of patient care.