ArticleEvidence-based mental health2019
Implementing a digital clinical decision support tool for side effects of antipsychotics: a focus group study.
Article in Evidence-based mental health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Informing the development of a decision aid: Expectations and wishes from service users and psychiatrists towards a decision aid for antipsychotics in the inpatient setting.Health expectations : an international journal of public participation in health care and health policy · 2023Pooled it
- Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence.Journal of medical Internet research · 2023Pooled it
- Antidepressants in Children and Adolescents: Meta-Review of Efficacy, Tolerability and Suicidality in Acute Treatment.Frontiers in psychiatry · 2020Pooled it
- Developing a Novel Digital Tool for Personalised Antipsychotic Prescribing in People Living With Dementia: The Views of Australian Clinicians.Dementia (London, England) · 2026Article
- Navigating the complexity of AI adoption in psychotherapy by identifying key facilitators and barriers.Npj mental health research · 2026Article
- Review
- Do Decision Support Tools Decrease the Prevalence of Hospital-Acquired Venous Thromboembolisms When Compared to Clinical Judgement? A Single-Center Pre-Post Study.Journal of clinical medicine · 2024Article
- Transdiagnostic clustering and network analysis for questionnaire-based symptom profiling and drug recommendation in the UK Biobank and a Korean cohort.Scientific reports · 2024Article
- On the Road to Individualizing Pharmacotherapy for Adolescents and Adults with Schizophrenia - Results from an Expert Consensus Following the Delphi Method.Neuropsychiatric disease and treatment · 2024Article
- Shared Decision Making Assistant (SDMA) and other digital tools for choosing antipsychotics in schizophrenia treatment.European archives of psychiatry and clinical neuroscience · 2023Article
- Psychometric evaluation of the Decision Support Tool for Functional Independence in community-dwelling older people.Journal of population ageing · 2022Article
- Shared and Supported Decision Making in Medication in a Mental Health Setting: How Far Have We Come?Community mental health journal · 2021Article
- Ensuring patient and public involvement in the transition to AI-assisted mental health care: A systematic scoping review and agenda for design justice.Health expectations : an international journal of public participation in health care and health policy · 2021Article
- Fostering Appropriate Antibiotic Use in a Complex Intervention: Mixed-Methods Process Evaluation Alongside the Cluster-Randomized Trial ARena.Antibiotics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundIn medicine, algorithms can inform treatment decisions by combining the most up-to-date evidence about side effect profiles of medications, which are comparable in efficacy. Their use provides opportunities for improved shared clinician-patient decision-making when initiating therapy. We designed a decision support tool (DST) that incorporated the latest evidence regarding antipsychotic side effects. The tool allowed patients to select one side effect commonly associated with antipsychotics that they wished to avoid; the tool then provided a list of suggested medications and ones to avoid.
objectiveTo explore qualitatively the acceptability and usefulness of the DST from the perspectives of patients and psychiatrists.
methodsThis qualitative study took place at a mental health and community hospital in Oxford, UK, in 2018. Four patients/carers and four psychiatrists were recruited to two focus groups to explore their perceptions of the tool. Data were thematically analysed.
findingsFindings demonstrated a high degree of acceptability and potential usability of the DST for patients and psychiatrists. The main themes to emerge relating to the DST were 'prescribing preferences and practices', 'consideration and awareness of side effects', 'app content, layout and accessibility', 'influence on clinical practice' and 'role in decision-making'.
conclusionsA proof-of-concept clinical study will incorporate the recommendations produced from the findings into the tool's design. CLINICAL IMPLICATIONS: Digital DSTs provide opportunities for the most up-to-date information on medication side effects to be used as the basis for shared clinician-patient decision-making. This tool has the potential to improve adherence to psychiatric medication, with benefits to clinical outcomes and healthcare resourcing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.