Meaney Peter Andrew, Hokororo Adolfine, Masenge Theopista, Mwanga Joseph, Kalabamu Florence Salvatory, Berg Marc, Rozenfeld Boris, Smith Zachary, Chami Neema, Mkopi Namala, Mwanga Castory, Agweyu Ambrose
Department of Pediatrics, Stanford University School of Medicine, Pediatrics, Palo Alto, CA, USA.
Department of Pediatrics, Catholic University of Health and Allied Sciences Bugando, Pediatrics, Mwanza, Tanzania.
Digit Health. 2023 Jul 24;9:20552076231180471. doi: 10.1177/20552076231180471. eCollection 2023 Jan-Dec.
Globally, inadequate healthcare provider (HCP) proficiency with evidence-based guidelines contributes to millions of newborn, infant, and child deaths each year. HCP guideline proficiency would improve patient outcomes. Conventional (in person) HCP in-service education is limited in 4 ways: reach, scalability, adaptability, and the ability to contextualize. Adaptive e-learning environments (AEE), a subdomain of e-learning, incorporate artificial intelligence technology to create a unique cognitive model of each HCP to improve education effectiveness. AEEs that use existing internet access and personal mobile devices may overcome limits of conventional education. This paper provides an overview of the development of our AEE HCP in-service education, Pediatric Acute Care Education (PACE). PACE uses an innovative approach to address HCPs' proficiency in evidence-based guidelines for care of newborns, infants, and children. PACE is novel in 2 ways: 1) its patient-centric approach using clinical audit data or frontline provider input to determine content and 2) its ability to incorporate refresher learning over time to solidify knowledge gains. We describe PACE's integration into the Pediatric Association of Tanzania's (PAT) Clinical Learning Network (CLN), a multifaceted intervention to improve facility-based care along a single referral chain. Using principles of co-design, stakeholder meetings modified PACE's characteristics and optimized integration with CLN. We plan to use three-phase, mixed-methods, implementation process. Phase I will examine the feasibility of PACE and refine its components and protocol. Lessons gained from this initial phase will guide the design of Phase II proof of concept studies which will generate insights into the appropriate empirical framework for (Phase III) implementation at scale to examine effectiveness.
在全球范围内，医疗保健提供者（HCP）对循证指南的掌握不足，每年导致数百万新生儿、婴儿和儿童死亡。HCP对指南的掌握程度提高将改善患者预后。传统的（面对面的）HCP在职教育在四个方面存在局限性：覆盖面、可扩展性、适应性以及情境化能力。自适应电子学习环境（AEE）作为电子学习的一个子领域，融入人工智能技术，为每个HCP创建独特的认知模型，以提高教育效果。利用现有互联网接入和个人移动设备的AEE可能会克服传统教育的局限性。本文概述了我们的AEE HCP在职教育项目——儿科急性护理教育（PACE）的发展情况。PACE采用创新方法来提高HCP在新生儿、婴儿和儿童护理循证指南方面的掌握程度。PACE在两个方面具有创新性：1）其以患者为中心的方法，利用临床审计数据或一线提供者的意见来确定内容；2）其能够随着时间推移纳入复习学习以巩固知识收获。我们描述了PACE如何融入坦桑尼亚儿科学会（PAT）的临床学习网络（CLN），这是一项多方面的干预措施，旨在沿着单一转诊链改善机构护理。通过共同设计原则，利益相关者会议修改了PACE的特点，并优化了其与CLN的整合。我们计划采用三阶段混合方法实施流程。第一阶段将检验PACE的可行性，并完善其组成部分和方案。从这一初始阶段获得的经验教训将指导第二阶段概念验证研究的设计，该研究将深入了解（第三阶段）大规模实施以检验有效性的适当实证框架。
