title: Detecting earlier indicators of homelessness in the free text of medical records.
abstract: Early warning indicators to identify US Veterans at risk of homelessness are currently only inferred from administrative data. References to indicators of risk or instances of homelessness in the free text of medical notes written by Department of Veterans Affairs (VA) providers may precede formal identification of Veterans as being homeless. This represents a potentially untapped resource for early identification. Using natural language processing (NLP), we investigated the idea that concepts related to homelessness written in the free text of the medical record precede the identification of homelessness by administrative data. We found that homeless Veterans were much higher utilizers of VA resources producing approximately 12 times as many documents as non-homeless Veterans. NLP detected mentions of either direct or indirect evidence of homelessness in a significant portion of Veterans earlier than structured data.
authors.affiliation: University of Utah, Department of Internal Medicine, Division of Epidemiology.
authors.forename: Andrew
authors.initials: A
authors.lastname: Redd
authors.affiliation: University of Utah, Department of Internal Medicine, Division of Epidemiology.
authors.forename: Marjorie
authors.initials: M
authors.lastname: Carter
authors.affiliation: University of Utah, Department of Internal Medicine, Division of Epidemiology.
authors.forename: Guy
authors.initials: G
authors.lastname: Divita
authors.affiliation: University of Utah, Department of Internal Medicine, Division of Epidemiology.
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.affiliation: University of Utah, Department of Internal Medicine, Division of Epidemiology.
authors.forename: Miland
authors.initials: M
authors.lastname: Palmer
authors.affiliation: University of Utah, Department of Internal Medicine, Division of Epidemiology.
authors.forename: Matthew
authors.initials: M
authors.lastname: Samore
authors.affiliation: University of Utah, Department of Internal Medicine, Division of Epidemiology.
authors.forename: Adi V
authors.initials: AV
authors.lastname: Gundlapalli
mesh_terms: D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification* / Q000458:organization & administration; D006703:Ill-Housed Persons / Q000145:classification* / Q000706:statistics & numerical data; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D014481:United States; D018875:Vocabulary, Controlled