Detection of practice pattern trends through Natural Language Processing of clinical narratives and biomedical literature.
Clinical knowledge, best evidence, and practice patterns evolve over time. The ability to track these changes and study practice trends may be valuable for performance measurement and quality improvement efforts. The goal of this study was to assess the feasibility and validity of methods to generate and compare trends in biomedical literature and clinical narrative. We focused on the challenge of detecting trends in medication usage over time for two diseases: HIV/AIDS and asthma. Information about disease-specific medications in published randomized control trials and discharge summaries at NewYork-Presbyterian Hospital over a ten-year period were extracted using Natural Language Processing. This paper reports on the ability of our semi-automated process to discover disease-drug practice pattern trends and interpretation of findings across the biomedical and clinical text sources.
Chen
Elizabeth S
ES
Partners HealthCare System, Wellesley, MA, USA.
Stetson
Peter D
PD
Lussier
Yves A
YA
Markatou
Marianthi
M
Hripcsak
George
G
Friedman
Carol
C
D018927:Anti-Asthmatic Agents / Q000627:therapeutic use; D019380:Anti-HIV Agents / Q000627:therapeutic use; D001249:Asthma / Q000188:drug therapy*; D001330:Electronic Data Processing; D005240:Feasibility Studies; D015658:HIV Infections / Q000188:drug therapy*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D008499:Medical Records; D009323:Natural Language Processing; D010351:Patient Discharge; D010818:Practice Patterns, Physicians' / Q000639:trends*; D016032:Randomized Controlled Trials as Topic; D012189:Retrospective Studies
D018927:Anti-Asthmatic Agents; D019380:Anti-HIV Agents