Evidence map›Paper›PMID 39854062›Full record

ArticleSuicide & life-threatening behavior2025

Computing 3-Step Theory of Suicide Factor Scores From Veterans Health Administration Clinical Progress Notes.

Esther L Meerwijk, Asqar S Shotqara, Andrea K Finlay, Ruth M Reeves, Suzanne R Tamang, Mark A Ilgen, Alex H S Harris

Abstract read
In one paragraph

Article in Suicide & life-threatening behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Esther L MeerwijkVA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, California, USA.ORCID https://orcid.org/0000-0002-5367-0978
Asqar S ShotqaraVA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, California, USA.
Andrea K FinlayVA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, California, USA.
Ruth M ReevesVA Tennessee Valley Healthcare System, Nashville, Tennessee, USA.
Suzanne R TamangVA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, California, USA.
Mark A IlgenVA Health Systems Research, Center for Clinical Management Research, Ann Arbor, Michigan, USA.
Alex H S HarrisVA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, California, USA.

Funding

HSRD VA I01 HX003122HSRD VA IK6 HX002840HSRD VA IK6 HX002841HSRD VA IK6 HX003906U.S. Veterans Health Administration Research & Development Research Career Scientist award 14 332 (Sox HarrisU.S. Veterans Health Administration Research & Development Research Career Scientist award 19 333 (Ilgen)U.S. Veterans Health Administration Research & Development SDR 19-345
6 · The paper itself

Abstract

backgroundLiterature on how to translate information extracted from clinical progress notes into numeric scores for 3-step theory of suicide (3ST) factors is nonexistent. We determined which scoring option would best discriminate between patients who will attempt or die by suicide and patients with neither suicidal ideation nor attempts, and we tested hypotheses related to the 3ST.

methodsWe used terminology-driven natural language processing (NLP) to extract information from Veterans Health Administration (VHA) clinical progress notes. Counts of those extractions served as input to evaluate candidate scoring options for each 3ST factor (psychological pain, hopelessness, connectedness, capability for suicide). Logistic regression models adjusted for common demographic characteristics were used to test the 3ST hypotheses.

resultsOptimal contrasts between groups were obtained with P - A for psychological pain, hopelessness, and capability for suicide, and LIMITATIONS: Additional research is necessary to verify whether our conclusions hold in a cohort that is more reflective of the general VHA population.

conclusionTerminology-driven 3ST factor scores discriminate patients who attempt or die by suicide from patients without suicidal ideation or attempts. Our results corroborate the validity of the 3ST for VHA patients.

Indexed as

Natural Language ProcessingSuicideVeteransAdultAgedFemaleHumansMaleMiddle AgedSuicidal IdeationSuicide, AttemptedUnited StatesUnited States Department of Veterans Affairs3‐step theory of suicidecontrolled vocabularyelectronic health recordsnatural language processingpsychological painsuicideveterans

Identifiers

PMID39854062
PMCPMC12456412

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