Andy Man Yeung Tai, Jane J Kim, Jim Schmeckenbecher, Vanessa Kitchin, Johnston Wang, Alireza Kazemi, Raha Masoudi, Hasti Fadakar, Frank Iorfino, Reinhard Michael Krausz
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In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.</p><p><strong>Results: </strong>A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS.</p><p><strong>Discussion: </strong>The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models.</p><p><strong>Conclusion: </strong>CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.</p>","PeriodicalId":15997,"journal":{"name":"Journal of evaluation in clinical practice","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical decision support systems in addiction and concurrent disorders: A systematic review and meta-analysis.\",\"authors\":\"Andy Man Yeung Tai, Jane J Kim, Jim Schmeckenbecher, Vanessa Kitchin, Johnston Wang, Alireza Kazemi, Raha Masoudi, Hasti Fadakar, Frank Iorfino, Reinhard Michael Krausz\",\"doi\":\"10.1111/jep.14069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders.</p><p><strong>Methods: </strong>Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.</p><p><strong>Results: </strong>A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. 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Clinical decision support systems in addiction and concurrent disorders: A systematic review and meta-analysis.
Introduction: This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders.
Methods: Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.
Results: A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS.
Discussion: The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models.
Conclusion: CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.
期刊介绍:
The Journal of Evaluation in Clinical Practice aims to promote the evaluation and development of clinical practice across medicine, nursing and the allied health professions. All aspects of health services research and public health policy analysis and debate are of interest to the Journal whether studied from a population-based or individual patient-centred perspective. Of particular interest to the Journal are submissions on all aspects of clinical effectiveness and efficiency including evidence-based medicine, clinical practice guidelines, clinical decision making, clinical services organisation, implementation and delivery, health economic evaluation, health process and outcome measurement and new or improved methods (conceptual and statistical) for systematic inquiry into clinical practice. Papers may take a classical quantitative or qualitative approach to investigation (or may utilise both techniques) or may take the form of learned essays, structured/systematic reviews and critiques.