A Mobile App for Patients and Those Who Care About Them

Barish, Greg; Aralis, Hilary; Elbogen, Eric B.; Lester, Patricia

doi:10.1145/3329189.3329248

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…The data used by digital decision support systems in the context of PTSD is diverse. Voice data ( 35 , 45 , 46 , 55 ), text data ( 38 , 48 , 50 ), checklists and questionnaires ( 32 , 33 , 37 , 41 – 43 , 52 , 53 , 59 ), bio signals ( 32 , 33 , 36 , 44 , 45 , 51 , 57 ) and electronic medical records ( 34 , 47 , 56 ) as well as secondary data from other clinical studies ( 31 , 40 , 49 , 54 ) are used. One article used the choices made by a virtual avatar in a role-playing game as input data ( 39 ).…”

Section: Resultsmentioning

confidence: 99%

“…Research focusing on predicting PTSD or its symptoms was most common ( n = 11). Six papers focused on screening ( 35 , 38 , 45 , 46 , 50 , 55 ) and six on treatment ( 32 , 36 , 43 , 51 , 53 , 56 ). Four papers investigated the diagnosis of PTSD ( 37 , 41 , 52 , 60 ) and five focused on monitoring PTSD ( 33 , 35 , 56 , 58 , 59 ).…”

Section: Resultsmentioning

confidence: 99%

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

2022

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ObjectiveOver 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.MethodsBased 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.ResultsResearch 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.ConclusionBased 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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