title: An end-to-end hybrid algorithm for automated medication discrepancy detection.
abstract: 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.
  
  
  CONCLUSIONS
  By leveraging ML and NLP technologies, an end-to-end, computerized algorithm achieves promising outcome in reconciling medications between clinical notes and discharge prescriptions.
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.|Chief Medical Information Officer, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
authors.forename: Stephen Andrew
authors.initials: SA
authors.lastname: Spooner
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.
authors.forename: Megan
authors.initials: M
authors.lastname: Kaiser
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.
authors.forename: Nataline
authors.initials: N
authors.lastname: Lingren
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.
authors.forename: Jessica
authors.initials: J
authors.lastname: Robbins
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.
authors.forename: Todd
authors.initials: T
authors.lastname: Lingren
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.
authors.forename: Huaxiu
authors.initials: H
authors.lastname: Tang
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA.|James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
authors.forename: Imre
authors.initials: I
authors.lastname: Solti
authors.affiliation: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229-3039, USA. Yizhao.Ni@cchmc.org.
authors.forename: Yizhao
authors.initials: Y
authors.lastname: Ni
mesh_terms: D000328:Adult; D000465:Algorithms; D011307:Drug Prescriptions / Q000592:standards*; D006801:Humans; D000069550:Machine Learning; D059065:Medication Reconciliation / Q000592:standards*; D009323:Natural Language Processing; D010351:Patient Discharge / Q000592:standards*