Cobuccio Ludovico Gennaro, Faivre Vincent, Tan Rainer, Vonlanthen Alan, Beynon Fenella, Barchichat Emmanuel, Fresco Alain, Girard Quentin, Ucak Sinan, Schaufelberger Sylvain, Mtebene Ibrahim Evans, Agrea Peter, Kalisa Emmanuel, Levine Gillian A, Norris Martin, Renggli Sabine, Miauton Alix, Cleveley Lisa, Keitel Kristina, Thabard Julien, D'Acremont Valérie, Kulinkina Alexandra V
Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
BMC Med Inform Decis Mak. 2025 Jul 4;25(1):249. doi: 10.1186/s12911-025-03077-6.
Sub-optimal healthcare quality in low-resource settings is attributed in part to poor adherence to clinical guidelines. Clinical decision support systems (CDSS) help to integrate guideline-based algorithms into logical workflows and improve adherence to evidence-based recommendations, and hence quality of care. However, the process of translating paper-based guidelines into electronic algorithmic formats is often complex, inefficient, expensive, and error-prone due to reliance on advanced software development skills and clinical knowledge.
In response to these challenges, we developed open-source software called the Medical Algorithm Suite (medAL-suite), consisting of four components, with a primary goal of increasing efficiency, accuracy, and transparency of CDSS creation by giving experienced clinicians, rather than software developers, greater control over the process. At the heart of the software suite is the medAL-creator that allows clinicians to design algorithms using a code-free drag-and-drop interface. Algorithms are subsequently automatically deployed in medAL-reader to service level clinicians in health facilities. CDSS implementers use medAL-data and medAL-hub to manage configuration, versioning, and deployment.
Since its development, the medAL-suite has been used to digitalize complex primary care guidelines and deployed in large-scale clinical studies in Tanzania, Rwanda, Kenya, Senegal, and India, leading to notable outcomes such as the reduction of inappropriate antibiotic prescriptions and improvement in care quality. Over 300,000 pediatric outpatient consultations have been completed in Rwanda and Tanzania to date using the digital algorithm.
The medAL-suite focused on democratized development, process-centric design, point-of-care utility, touch-screen interface, low cost, and low power consumption to contribute to sustainable digital systems in low-resource settings. Important future developments and adaptations as the software evolves should emphasize interoperability and scalability, primarily via integrating CDSS functionality into electronic medical records for a streamlined user experience that supports improved service quality at the point-of-care.
Not applicable.
资源匮乏地区医疗质量欠佳部分归因于对临床指南的依从性差。临床决策支持系统（CDSS）有助于将基于指南的算法整合到逻辑工作流程中，并提高对循证建议的依从性，从而提升医疗质量。然而，由于依赖先进的软件开发技能和临床知识，将纸质指南转化为电子算法格式的过程往往复杂、低效、昂贵且容易出错。
为应对这些挑战，我们开发了名为医学算法套件（medAL-suite）的开源软件，它由四个组件组成，主要目标是通过让经验丰富的临床医生而非软件开发人员对流程有更大控制权，来提高CDSS创建的效率、准确性和透明度。该软件套件的核心是medAL-creator，它允许临床医生使用免代码的拖放界面来设计算法。算法随后会自动部署到medAL-reader中，供医疗机构的一线临床医生使用。CDSS实施人员使用medAL-data和medAL-hub来管理配置、版本控制和部署。
自开发以来，medAL-suite已被用于将复杂的初级保健指南数字化，并在坦桑尼亚、卢旺达、肯尼亚、塞内加尔和印度的大规模临床研究中进行部署，取得了显著成果，如减少了不适当的抗生素处方并改善了医疗质量。迄今为止，卢旺达和坦桑尼亚已使用该数字算法完成了超过30万次儿科门诊咨询。
medAL-suite专注于民主化开发、以流程为中心的设计、床边实用性、触摸屏界面、低成本和低功耗，以助力资源匮乏地区的可持续数字系统。随着软件的不断发展，未来重要的改进和调整应主要通过将CDSS功能集成到电子病历中，以实现简化的用户体验，从而在床边支持提高服务质量，重点强调互操作性和可扩展性。
不适用。
