Computerized clinical decision support systems for prescribing in primary care: Characteristics and implementation impact. Scoping review and evidence and gap maps
This study aimed to conduct a scoping review and evidence and gap maps to characterize Clinical Decision Support Systems (CDSS) in primary care, evaluate their implementation and maintenance levels, and identify evidence gaps. Methods: A literature search covering January 2010 to May 2023 was conduc...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/170423 |
| Acceso en línea: | https://hdl.handle.net/11441/170423 https://doi.org/10.1016/j.hlpt.2025.100976 |
| Access Level: | acceso abierto |
| Palabra clave: | evidence and gap maps computerized clinical decision support systems electronic prescribing decision support system primary healthcare |
| Sumario: | This study aimed to conduct a scoping review and evidence and gap maps to characterize Clinical Decision Support Systems (CDSS) in primary care, evaluate their implementation and maintenance levels, and identify evidence gaps. Methods: A literature search covering January 2010 to May 2023 was conducted across various databases. Inclusion criteria encompassed studies involving real patients with detailed descriptions of CDSS, including both comparative and descriptive designs within primary care settings. Two independent reviewers screened the references, while four researchers independently extracted data, which included demographics, main findings, and system descriptions. The results were presented using interactive evidence and gap maps. Results: Among 1,447 initial citations, 75 studies met the selection criteria. The identified types of CDSS included adherence to guidelines/local protocols (45 %), antibiotic prescription (16 %), suitability (15 %), and others. Only one system was classified as "intelligent," while 39 % received a complexity rating of 4 on a scale from 1 to 5. Assessment of various outcomes across the studies revealed health outcomes (20 %), economy/resource use (13 %), potentially inappropriate prescription (61 %), adherence to local guidelines/protocols (12 %), and acceptance/use (40 %). Two maps were created: The first one displayed the type of CDSS linked to the type of results measured. The second one showed the type of CDSS and their most relevant characteristics. Data were represented in a dynamic bubble diagram. Conclusion: Current evidence regarding CDSS in primary care is limited and heterogeneous. The identified systems exhibit relative complexity but are not classified as intelligent, primarily focusing on improving prescribing practices through clinical guidelines or prescription aid tools. The outcomes most frequently assessed included potentially inappropriate prescriptions and acceptance/use. The evidence and gap maps provide a user-friendly format for visualizing existing evidence and identifying research gaps in the implementation of CDSS within primary care. |
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