ReHouSED: A novel measurement of Veteran housing stability using natural language processing

Chapman, A; Jones, Audrey L.; Kelley, A. Taylor; Jones, Barbara E.; Gawron, Lori M.; Montgomery, Ann Elizabeth; Byrne, Thomas; Suo, Ying; Cook, James; Pettey, Warren; Peterson, Kelly; Jones, Makoto; Nelson, Richard E.

doi:10.1016/j.jbi.2021.103903

Cited by 21 publications

(18 citation statements)

References 11 publications

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“…There may be ways to improve identification of housing instability, such as more widespread use of screeners like the HSCR, better training for providers on how to screen and identify housing instability, and more open ways for patients to self-identify or self-refer for housing assistance. There are also new advanced technologies such as natural language processing that can mine free-text provider notes to detect cases of housing instability [18,19] and may present new ways to extract information that may be relevant to researchers and providers.…”

Section: Plos Onementioning

confidence: 99%

Developing an operational definition of housing instability and homelessness in Veterans Health Administration’s medical records

Tsai

Szymkowiak²,

Jutkowitz

2022

PLoS ONE

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The main objective of this study was to examine how homelessness and housing instability is captured across data sources in the Veterans Health Administration (VHA). Data from 2021 were extracted from three data repositories, including the Corporate Data Warehouse (CDW), the Homeless Operations Management System (HOMES), and the Homeless Management Information System (HMIS). Using these three data sources, we identified the number of homeless and unstably housed veterans across a variety of indicators. The results showed that the use of diagnostic codes and clinic stop codes identified a large number of homeless and unstably housed veterans, but the use of HOMES and HMIS data identified additional homeless and unstably housed veterans to provide a complete count. A total of 290,431 unique veterans were identified as experiencing homelessness or housing instability in 2021 and there was regional variability in how homelessness and housing stability were captured across data sources, supporting the need for more uniform ways to operationalize these conditions. Together, these findings highlight the and encourage use of all available indicators and data sources to identify homelessness and housing instability in VHA. These methodologies applied to the largest healthcare system in the U.S. demonstrate their utility and possibilities for other healthcare systems. Transparent practices about data sources and indicators used to capture homelessness and housing instability should be shared to increase uniform use.

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Section: Plos Onementioning

confidence: 99%

Developing an operational definition of housing instability and homelessness in Veterans Health Administration’s medical records

Tsai

Szymkowiak²,

Jutkowitz

2022

PLoS ONE

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“…Housing status was extracted from clinical notes in the EHR using a previously validated NLP system called ReHouSED 4 . Briefly, ReHouSED is a rule-based NLP system that takes as input a clinical note and extracts phrases representing concepts related to housing status, assigns linguistic attributes such as negation and temporality, and infers a document-level classification that represents the overall housing status of the Veteran according to the note.…”

Section: Extracting Housing Status From the Ehrmentioning

confidence: 99%

“…In recent years, Electronic Health Records (EHR) have increasingly been utilized as a data source for studying homelessness and other adverse SDoH. [1][2][3][4][5][6][7][8][9][10] Many of these studies have utilized natural language processing (NLP), a set of techniques for extracting information from unstructured clinical texts. Improved measurement of housing instability offers a number of benefits to researchers and policymakers such as the ability to study risk factors for becoming homeless or the effectiveness of homelessness interventions.…”

Section: Introductionmentioning

confidence: 99%

Using natural language processing to study homelessness longitudinally with electronic health record data subject to irregular observations

Chapman

Scharfstein

Montgomery

et al. 2023

Preprint

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The Electronic Health Record (EHR) contains information about social determinants of health (SDoH) such as homelessness. Much of this information is contained in clinical notes and can be extracted using natural language processing (NLP). This data can provide valuable information for researchers and policymakers studying long-term housing outcomes for individuals with a history of homelessness. However, studying homelessness longitudinally in the EHR is challenging due to irregular observation times. In this work, we applied an NLP system to extract housing status for a cohort of patients in the US Department of Veterans Affairs (VA) over a three-year period. We then applied inverse intensity weighting to adjust for the irregularity of observations, which was used generalized estimating equations to estimate the probability of unstable housing each day after entering a VA housing assistance program. Our methods generate unique insights into the long-term outcomes of individuals with a history of homelessness and demonstrate the potential for using EHR data for research and policymaking.

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“…Natural Language Processing (NLP) can be used to extract relevant data from clinical text. Examples include adverse events, [6][7][8][9][10][11][12] social determinants of health, [13][14][15][16][17] and infectious disease surveillance. [18][19][20][21][22][23][24] However, a major barrier to using NLP for research is interoperability.…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care

Chapman

Peterson

Nevers

et al. 2022

Preprint

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ObjectiveTo evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system which extracts diagnostic assertions of pneumonia in different clinical notes and institutions.Materials and MethodsAn NLP system was designed to identify assertions of pneumonia in three types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs. Interoperability was assessed using data from the University of Utah.ResultsThe NLP system was comprised of 782 rules and achieved moderate-to-high performance in all three note types in VA (precision/recall/f1: emergency=88.1/86.0/87.1; radiology=71.4/96.2/82.0; discharge=88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency=84.7/94.3/89.3; radiology=79.7/100.0/87.9; discharge=65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency=89.3/94.3/91.7; radiology=87.0/100.0/93.1; discharge=75.0/95.1/83.4).ConclusionNLP can be used to accurately identify the diagnosis of pneumonia in different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.

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ReHouSED: A novel measurement of Veteran housing stability using natural language processing

Cited by 21 publications

References 11 publications

Developing an operational definition of housing instability and homelessness in Veterans Health Administration’s medical records

Developing an operational definition of housing instability and homelessness in Veterans Health Administration’s medical records

Using natural language processing to study homelessness longitudinally with electronic health record data subject to irregular observations

Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care

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