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Development of pediatric acute care education (PACE) An adaptive electronic learning (e-learning) environment for healthcare providers in Tanzania

By: Contributor(s): Material type: TextTextLanguage: English Series: ; Digital Health Volume 9 January-December 2023Publication details: Mwanza, Tanzania : Catholic University of Health and Allied Sciences [CUHAS-Bugando] : 2023Description: Pages 01-02; Includes ReferencesISSN:
  • 2055-2076
  • Online 2055-2076
Subject(s): Online resources: Summary: Abstract : 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.
Item type: RESEARCH ARTICLES
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Item type Current library Status Barcode
RESEARCH ARTICLES MWALIMU NYERERE LEARNING RESOURCES CENTRE-CUHAS BUGANDO Not for loan 20240610124813.0
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Abstract :

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.

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