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Cited by 40Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareElectronic Health Records Systems

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A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder

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Bertl, Markus, Metsallik, Janek, Ross, Peeter·2022·Frontiers in Psychiatry
DOI 10.3389/fpsyt.2022.923613PMC PMC9396247
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Abstract

Objective: Over the last decade, an increase in research on medical decision support systems has been observed. However, compared to other disciplines, decision support systems in mental health are still in the minority, especially for rare diseases like post-traumatic stress disorder (PTSD). We aim to provide a comprehensive analysis of state-of-the-art digital decision support systems (DDSSs) for PTSD. Methods: Based on our systematic literature review of DDSSs for PTSD, we created an analytical framework using thematic analysis for feature extraction and quantitative analysis for the literature. Based on this framework, we extracted information around the medical domain of DDSSs, the data used, the technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level. Extracting data for all of these framework dimensions ensures consistency in our analysis and gives a holistic overview of DDSSs. Results: Research on DDSSs for PTSD is rare and primarily deals with the algorithmic part of DDSSs (n= 17). Only one DDSS was found to be a usable product. From a data perspective, mostly checklists or questionnaires were used (n= 9). While the median sample size of 151 was rather low, the average accuracy was 82%. Validation, excluding algorithmic accuracy (like user acceptance), was mostly neglected, as was an analysis concerning possible user groups. Conclusion: Based on a systematic literature review, we developed a framework covering all parts (medical domain, data used, technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level) of DDSSs. Our framework was then used to analyze DDSSs for post-traumatic stress disorder. We found that DDSSs are not ready-to-use products but are mostly algorithms based on secondary datasets. This shows that there is still a gap between technical possibilities and real-world clinical work.

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  • Digital decision-support systems in broader mental health settings may help structure clinical reasoning, reduce uncertainty, and support more consistent decision-making.

    “Such systems may help structure clinical reasoning, reduce uncertainty, and support more consistent decision-making [14,15].”

    From A Digital Tool for Culturally Responsive Reflection and Case Conceptualization in First-Line Services to Support Children With a Refugee Background: Development and Usability Study (2026) · cites this paper as [14]

    The Introduction (page unavailable) describes DDSSs as organizing and analyzing information and helping evaluate assumptions, which supports the idea that they may structure clinical reasoning. The review focuses on PTSD, however, and our copy does not establish that these systems reduce uncertainty or make decisions more consistent across broader mental health settings. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Metadata

Authors
Bertl, M., Metsallik, J., & Ross, P.
Affiliations
Tallinn University of Technology (EE)
Corresponding author
Bertl, Markus
ORCID
Venue
Frontiers in Psychiatry
Publisher
Frontiers Media SA
Published
2022-08-09
Volume
13
Pages
923613-923613
ISSN
1664-0640 (ISSN-L)
DOI
10.3389/fpsyt.2022.923613
PMC
PMC9396247
OpenAlex
W4292452320
Cited by
40
References
7
Citations by year
2026: 9 · 2025: 8 · 2024: 11 · 2023: 10 · 2022: 2
FWCI
0.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Psychiatry (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
post-traumatic stress disorder, digital decision support systems, artificial intelligence, mental health, clinical decision support, analytical framework, validation, user groups, rare diseases

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