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      Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop

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          Abstract

          This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

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          An overview of clinical decision support systems: benefits, risks, and strategies for success

          Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.
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            Advances and Open Problems in Federated Learning

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              Implementing Measurement-Based Care in Behavioral Health

              Measurement-based care (MBC) is the systematic evaluation of patient symptoms before or during an encounter to inform behavioral health treatment. Despite MBC's demonstrated ability to enhance usual care by expediting improvements and rapidly detecting patients whose health would otherwise deteriorate, it is underused, with typically less than 20% of behavioral health practitioners integrating it into their practice. This narrative review addresses definitional issues, offers a concrete and evaluable operationalization of MBC fidelity, and summarizes the evidence base and utility of MBC. It also synthesizes the extant literature's characterization of barriers to and strategies for supporting MBC implementation, sustainment, and scale-up.
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                Author and article information

                Contributors
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                Journal
                Schizophrenia Bulletin
                Oxford University Press (OUP)
                0586-7614
                1745-1701
                March 01 2023
                March 22 2023
                March 22 2023
                March 01 2023
                March 22 2023
                March 22 2023
                : 49
                : Supplement_2
                : S86-S92
                Article
                10.1093/schbul/sbac215
                10031727
                36946526
                f36a4f00-a426-415f-883a-254f65e020cf
                © 2023

                https://academic.oup.com/pages/standard-publication-reuse-rights

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