Health Informatics and Clinical Decision Support in Psychiatric Care for Vulnerable Populations

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Mental Health and Psychosocial Well-being".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1244

Editors


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Guest Editor
1. National Center for PTSD, Behavioral Science Division at VA Boston Healthcare System, Boston, MA 02130, USA
2. Department of Psychiatry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
Interests: suicide prevention; PTSD, psychosis; addiction; digital health; clinical decision support; informatics; healthcare delivery

E-Mail Website
Guest Editor
1. National Center for PTSD at the VA Boston Healthcare System, Boston, MA 02130, USA
2. Department of Psychiatry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
Interests: PTSD; substance use disorders; military sexual trauma; treatment development; clinical trials; equity in healthcare; dissemination and implementation

E-Mail Website
Guest Editor
Department of Applied Mathematics for Information and Communications Technologies, ETSISI, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: machine learning; natural language processing; applied artificial intelligence in mental health care

Special Issue Information

Dear Colleagues,

Psychiatric conditions—including posttraumatic stress disorder, psychosis, depression, substance use disorders, and suicide risk—remain leading contributors to global morbidity and mortality. These challenges disproportionately affect vulnerable populations, including veterans, women, racial and ethnic minorities, individuals living in poverty, and communities with limited healthcare access. Despite advances in psychiatric research, gaps persist in translating evidence into timely, equitable, and patient-centered care.

Health informatics offers powerful tools to close these gaps. Clinical decision support systems, natural language processing, AI-driven risk stratification, and digital patient-reported outcomes can inform high-stakes psychiatric care decisions and guide interventions in real-world settings. Yet many innovations remain insufficiently validated, poorly adapted to diverse populations, or underutilized in clinical practice.

This Special Issue of Healthcare invites theoretical, empirical, and review papers that advance the science and practice of health informatics in psychiatric care for vulnerable populations. We especially encourage contributions that emphasize validation of tools, integration into health systems, patient-centered design, and approaches that address equity and inclusiveness in mental health decision-making. By bringing together interdisciplinary perspectives, this Special Issue seeks to bring informatics solutions to improve psychiatric care and outcomes for those most in need.

Dr. Amar D. Mandavia
Dr. Anne N. Banducci
Dr. Enrique Gutiérrez
Guest Editors

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Keywords

  • health informatics
  • clinical decision support
  • psychiatric care
  • vulnerable populations
  • suicide prevention
  • posttraumatic stress disorder
  • psychosis and thought disorder
  • substance use disorders
  • patient-centered care
  • digital health equity
  • veterans’ health
  • women’s mental health
  • minority health disparities
  • suicide prevention

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Published Papers (1 paper)

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20 pages, 489 KB  
Systematic Review
Linguistic Markers in At-Risk Mental States Using Natural Language Processing: A Systematic Review
by Yuhan Zhang, Alba Carrió, Julia Sevilla-Llewellyn-Jones, Enrique Gutiérrez, Ana Calvo, Jose-Blas Navarro and Ana Barajas
Healthcare 2026, 14(8), 999; https://doi.org/10.3390/healthcare14080999 - 10 Apr 2026
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Abstract
Background/Objectives: In recent years, research on psychosis has increasingly focused on prevention, aiming to implement early interventions that mitigate or reduce its impact. Within this framework, the analysis of linguistic markers in individuals with at-risk mental states (ARMS) has proven valuable for [...] Read more.
Background/Objectives: In recent years, research on psychosis has increasingly focused on prevention, aiming to implement early interventions that mitigate or reduce its impact. Within this framework, the analysis of linguistic markers in individuals with at-risk mental states (ARMS) has proven valuable for identifying those at risk and predicting psychosis onset. Artificial intelligence tools, particularly natural language processing (NLP), have emerged as effective resources for detecting these language-based indicators. This study aims to synthesize the existing scientific evidence on linguistic markers analyzed through NLP techniques in individuals with ARMS. Methods: A systematic review following the PRISMA 2020 protocol was conducted. Three databases (PubMed, PsycInfo, and Scopus) were searched for published articles from their inception to October 2025. Rayyan software was used to manage references and article downloads. Out of ninety initial search results, fifteen studies involving 1313 participants from diverse groups were included in the review. Results: The findings indicated that alterations in semantic coherence, syntactic complexity, referential cohesion, and speech/content poverty differentiated ARMS individuals from healthy controls. Several of these markers, analyzed with NLP methods, predicted the onset of psychosis with accuracy levels ranging from 79% to 100%, although these findings should be interpreted with caution due to the significant methodological heterogeneity and variability in sample sizes across the included studies. Conclusions: NLP techniques offer a powerful approach for detecting language alterations that distinguish ARMS individuals and provide meaningful predictions of psychosis onset, highlighting their potential as a complement to traditional clinical assessments for early identification and prevention. Full article
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