A Systematic Survey of Distributed Decision Support Systems in Healthcare

The global Internet of Medical Things (IoMT) market is growing at a Compound Annual Growth Rate (CAGR) of 17.8%, a testament to the increasing demand for IoMT in the health sector. However, more IoMT devices mean an increase in the volume and velocity of data received by healthcare decision-makers, leading many to develop Distributed Decision Support Systems (DDSSs) to help them make accurate and timely decisions. This research is a systematic review of DDSSs in healthcare using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The study explores how advanced technologies such as Artificial Intelligence (AI), IoMT, and blockchain enhance clinical decision-making processes. It highlights key innovations in DDSSs, including hybrid imaging techniques for comprehensive disease characterization. It also examines the role of Case-Based Reasoning (CBR) frameworks in improving personalized treatment strategies for chronic diseases like diabetes mellitus. It also presents challenges of applying DDSSs in the healthcare sector, such as security and privacy, system integration, and interoperability issues. Finally, it discusses open issues as future research directions in the field of DDSSs in the healthcare sector, including data structure standardization, alert fatigue for healthcare workers using DDSSs, and the lack of adherence of emerging technologies like blockchain to medical regulations.