Monitoring, Learning, and Evaluation
inSupply Health has a strategic priority of becoming a learning organization that is committed to generating knowledge while collaborating with our end users. inSupply specializes in developing theories of change and action; designing and implementing monitoring, learning, and evaluation (MLE) strategies; visualizing data and creating decision-support tools; routine program monitoring; implementation research; and evaluating the impact of projects and interventions.
If AI technology will be well implemented it will bring huge positive impact in not only health sector but other sectors as well.
Data: Capture, Management, Analysis, and Visualization
inSupply has expertise in both qualitative and quantitative health supply chain data collection and analysis. Over the years, we have developed our data collection tools and artifacts and honed our user-centered research skills. We foster data visibility and analytics as the foundation for data use culture through development of free/affordable, accessible, and user-friendly dashboards with action-oriented reports.
Data: Capture, Management, Analysis, and Visualization Use Cases
Use case coming soon
Use case coming soon
Use case coming soon
Assessments and Routine Monitoring, Learning, and Evaluation
We design contextually fit research and evaluation approaches for all of our projects. We have extensive experience measuring and documenting progress toward outcomes. We use data to report to our partners and funders, support monitoring and continuous learning, and to maximize health and supply chain outcomes.
Assessments and Routine Monitoring, Learning, and Evaluation Use Cases
Strategies to strengthen community health provision through CHVs in the ASAL (Developmental Evaluation Wave 2)
Supply Chain Data Use Theory of Change and Theory of Action
Understanding the Barriers to Successful Implementation of CBD in the ASAL Regions (Developmental Evaluation Wave 3)
Use-focused Monitoring, Learning, and Evaluation
Use-focused MLE recognizes that traditional monitoring and evaluation approaches do not always capture the complexities associated with system changes and uncertain environments. inSupply creates adaptable learning systems to support data use and application to programming. Examples:
- Development evaluation.
- Adaptive learning.
- Complexity awareness monitoring techniques (e.g., ripple effects mapping, outcome harvesting, and most significant change).
These techniques help us identify unanticipated outcomes and effects of our programs and understand what is working and why, and what needs to be adjusted and how.
Use-focused Monitoring, Learning, and Evaluation Use Cases
Developmental Evaluation of Community Health Models in ASAL Counties in Kenya (Developmental Evaluation Wave 1)
Measuring the Complicated and Complex: Incorporating Complexity-Aware Methods in Monitoring, Evaluation & Learning
Outcome Harvesting Synthesis Report
Machine Learning – Artificial Intelligence
inSupply helps health supply chain leaders in East African countries understand and navigate artificial intelligence and machine learning systems. We ensure that the adoption and ingration of these systems into logistics management information systems are context-specific and sustainable. inSupply can assess needs, build step-by-step adoption processes, analyze gaps, mobilize required resources, and build partnerships, all while ensuring stakeholder engagement.
Machine Learning and Artificial Intelligence Use Cases
Theory of Change & Theory of Action for Strengthening of Healthcare Supply Chain through AI
Implementing AI in Tanzanian Health Supply Chain
Artificial Intelligence and Health Supply Chains in Tanzania Panel Discussion