We describe the development, implementation, and evaluation of a novel twinning approach: the Twinning Partnership Network (TPN). Twinning is a well-known approach to peer learning that has been used in a variety of settings to build organizational capacity. Although twinning takes many forms, the heart of the approach is that institutions with shared characteristics collaborate via sharing information and experiences to achieve a specific goal. We adapted a twinning partnership strategy developed by the World Health Organization to create a network of like-minded health institutions. The key innovation of the TPN is the network, which ensures that an institution always has a high-performing peer with whom to partner on a specific topic area of interest. We identified 10 hospitals and 30 districts in Rwanda to participate in the TPN. These districts and hospitals participated in a kickoff workshop in which they identified capacity gaps, clarified goals, and selected twinning partners. After the workshop, districts and hospitals participated in exchange visits, coaching visits, and virtual and in-person learning events. We found that districts and hospitals that selected specific areas and worked on them throughout the duration of the TPN with their peers improved their performance significantly when compared with those that selected and worked on other areas. Accreditation scores improved by 5.6% more in hospitals selecting accreditation than those that did not. Districts that selected improving community-based health insurance coverage improved by 4.8% more than districts that did not select this topic area. We hypothesize that these results are due to senior management's interest and motivation to improve in these specific areas, the motivation gained by learning from high-performing peers with similar resources, and context-specific knowledge sharing from peer hospitals and districts.
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http://dx.doi.org/10.9745/GHSP-D-23-00280 | DOI Listing |
NPJ Digit Med
December 2024
GenHotel, Laboratoire Européen de Recherche Pour La Polyarthrite Rhumatoïde, University Paris-Saclay, University Evry, Evry, France.
Rheumatoid arthritis is a complex disease marked by joint pain, stiffness, swelling, and chronic synovitis, arising from the dysregulated interaction between synoviocytes and immune cells. Its unclear etiology makes finding a cure challenging. The concept of digital twins, used in engineering, can be applied to healthcare to improve diagnosis and treatment for complex diseases like rheumatoid arthritis.
View Article and Find Full Text PDFPediatr Res
November 2024
Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK.
Objective: To examine the relationship between etiologically-based preterm birth sub-groups and early postnatal growth according to gestational age at birth.
Methods: Prospective, multinational, cohort study involving 15 hospitals that monitored preterm newborns to hospital discharge. Measures/exposures: maternal demographics; etiologically-based preterm birth sub-groups; very, moderate and late preterm categories, and feeding.
BMJ Open
November 2024
Division of Population Health, Health Services Research & Primary Care, The University of Manchester, Manchester, UK.
Br J Psychiatry
October 2024
Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Comput Biol Med
December 2024
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, United Kingdom; UK Dementia Research Institute Care Research and Technology Centre, Imperial College, London, United Kingdom.
Background: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates.
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