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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Asthma clinical decision support systems in primary care: an updated scoping review of implementation
Holly Tibble1, Bohee Lee2, Imogen Skene3,4
1Usher Institute, University of Edinburgh, Edinburgh, UK. htibble@ed.ac.uk.
Abstract:
Despite advances in digital health technologies, the integration of clinical decision support systems (CDSSs) into routine primary care for asthma has been extremely limited. Asthma CDSSs employ diverse approaches, from prescribing support to risk prediction, but it remains unclear which are most likely to achieve sustained, real-world impact. Our objectives were to determine the mechanisms of action, clinical practice integration approaches, outputs and outcomes of asthma CDSSs in recent literature. Five electronic databases (Embase via Ovid, PubMed, CENTRAL, the Health Technology Assessment (HTA) Database, and the ISRCTN registry of clinical trials) were searched to identify papers published between 2012 and 2024 describing pilot studies, feasibility studies, or clinical trials, of primary care-based asthma CDSS. Two independent reviewers screened the retrieved literature and extracted the data on study designs, interventions, outcomes and mechanisms of action, and results. Across 18 included trials, interventions demonstrated substantial heterogeneity in mechanisms, integration methods, and targeted clinical behaviours. Although some studies showed improvements in adherence to prescribing best practices and the delivery of personalised action plans, most reported modest or declining system use over time and inconsistent effects on asthma control or severe attack outcomes. Continued progress will depend on integrating behavioural theory, improving workflow compatibility, and generating rigorous evidence to guide the development of CDSSs with genuine potential for sustainable impact.
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