This community innovation has been accepted at the 2025 DHIS2 Annual Conference
Scenario Planning for Next Best Action in Edo Stat
Effective resource allocation and strategic decision making are critical challenges in development initiatives, where dynamic and often unpredictable conditions can significantly influence outcomes. Digital Health Information Software 2 (DHIS2) is widely used for data management and analytics; however, it can benefit from enhanced predictive and scenario planning capabilities to further help decision makers transform data into actionable strategies. Existing tools lack interactive predictive features that enable rapid scenario testing and real time forecasting to enhance decision making. Recognizing the need for more robust, evidence based decision support, this work integrates machine learning driven scenario modeling within DHIS2, equipping decision makers with advanced analytics for scenario based decision support. By embedding predictive analytics, organizations can harness real time data to simulate future conditions, anticipate risks, and optimize resource distribution, workforce deployment, and intervention planning. The system enables stakeholders to explore what if scenarios, assessing the impact of key variables such as population trends, workforce density, and service availability on different intervention strategies. To implement this capability, a machine learning (ML) module was developed within a newly created DHIS2 application, allowing for scenario based planning in teacher allocation, healthcare workforce distribution, early warning systems, and health insurance enrollment impact analysis. The ML algorithms leverage historical, operational and geospatial datasets to forecast potential outcomes when key explanatory variables are adjusted. The interactive interface enables decision makers to construct scenarios using adjustable parameters and dropdown selections, while the embedded predictive logic calculates outcome projections and enables comparative analyses. Preliminary deployments in select health and education programs demonstrated that users could rapidly generate viable projections for campaign strategies, workforce distribution, and intervention modeling. The simple and intuitive interface significantly reduced the learning curve, while the embedded predictive algorithms provided actionable insights into potential performance improvements and risk mitigation strategies. Early feedback highlighted improved communication among stakeholders who could now visually and quantitatively discuss future uncertainties. Integrating scenario planning and predictive analytics within DHIS2 elevates the platform from a data repository and visualization platform to a proactive decision-support system. By providing decision-makers with real-time, data-driven insights, the solution promotes more effective resource allocation and prompt reactions to new challenges. This strategy carries significant ramifications for health and development, providing a scalable framework to predict future scenarios, optimize planning procedures, and ultimately improve the effectiveness of interventions and policies.
Primary Author: Muhammad Abiodun Sulaiman
Keywords:
DHIS2, resource allocation, decision-making, predictive analytics, scenario planning, machine learning, real-time data, intervention strategies, workforce distribution, health insurance enrollment, predictive logic, geospatial datasets, scenario modeling, outcome projections, risk mitigation.