Conceptual Modeling of Algorithm Parameterization and Results Considering Volatile Data Requirements:A Case Study in the Context of Primary Healthcare

Context: Conceptual modeling in software projects is often affected by volatile data requirements, which influence design, logic, and performance. Flexible data modeling is essential to adapt to these changes, especially in healthcare applications where diverse, evolving data types are common. Ensuring adaptability in these settings supports operational efficiency and timely decision-making. Problem: Frequent updates in the requirements of Primary Healthcare (PHC) systems increase maintenance costs and cause operational issues, especially when systems need to meet new legislative or operational demands. These changes can disrupt database schemas and impact software development costs. Proposed Solution: This paper introduces six schema alternatives designed to model algorithm parameters and outputs flexibly, reducing the impact of changing requirements on system structure. These adaptable schemas aim to minimize the need for frequent reconfigurations. IS Theory: The research is based on the Theory of Information Systems Flexibility, which focuses on creating adaptable data structures to ensure long-term efficiency in dynamic settings. This theoretical foundation aligns well with PHC’s evolving data needs, requiring resilient and adaptable systems. Method: A prescriptive approach was used, combining theoretical evaluation of schema options with a case study in a real-world PHC project. This mixed-method approach provided insights into schema flexibility and usability. Summary of Results:We concluded that the proposed database schema is highly effective for adapting to new data requirements, allowing algorithm data and results to be stored as JSON attributes. This flexibility in data inputs and outputs supports evolving PHC requirements. Contributions and Impact on IS: This study introduces a database schema for managing volatile data requirements in healthcare IS and reducing IS development costs.