Integrating Data Analytics and Decision Support Systems in Public Health Management

Abstract

For better data-driven decision-making and better health results, it is important for public health management to include data analytics and decision support systems (DSS). This abstract talks about why combining these tools is important and how they might affect public health management.Data analytics is an important part of public health because it uses big sets of data to find useful information for making decisions. By looking at patterns, trends, and connections in health data, public health managers can find new health problems, make good use of resources, and keep an eye on how well measures are working.As an addition to data analytics, decision support systems offer tools and models that make the decision-making process easier. Algorithms and models are used by these systems to look at data, make suggestions, and weigh possible results. This helps public health managers make smart choices in settings that are complicated and changeable.There are several perks to using both data analytics and DSS together in public health management. It makes decisions more accurate and reliable by giving real-time data and suggestions based on proof. It also helps plan and allocate resources better by finding groups at high risk and directing actions more effectively.Putting these tools together also helps public health managers handle public health situations better, like disease attacks or natural disasters. Using data analytics and DSS, public health agencies can quickly figure out what's going on, put resources where they're needed most, and keep real-time track of how measures are working.

DOI: https://doi.org/10.52710/seejph.495