Influence of Interoperable Health Information Systems, Real-Time Data Dashboards, and Predictive Intelligence on the Effectiveness of Public Health Early Warning and Surveillance Systems
Rush University Medical Center, Chicago, IL, USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 16(01), 1307-1324
Publication history:
Received on 15 September 2022; revised on 25 October 2022; accepted on 31 October 2022
Abstract:
Digital health technologies are moving faster, and real-time, multi-source data is becoming easier to get. Because of this, we are much better prepared for public health emergencies. To find and respond to outbreaks, we need traditional public health monitoring systems, but they often have problems that make it hard to make quick and good decisions during health emergencies. Some of these problems are delays in reporting, broken data architectures, and not being able to do much with the data. This study looks at how interoperable health information systems, real-time data dashboards, and predictive analytics affect how well public health early warning and surveillance systems work. The Research is based on the idea that adding digital health technologies to surveillance systems can greatly improve how quickly, accurately, and broadly public health responds to new threats. Systematic review methodology in accordance with PRISMA 2020 guidelines. Thorough searches of PubMed, Scopus, Web of Science, and JSTOR produced peer-reviewed empirical studies published from 2000 to 2021. After eliminating duplicates and undergoing a two-step screening and eligibility assessment, 155 studies satisfied the inclusion criteria and were incorporated into a qualitative narrative synthesis. The evaluation concentrated on evidence pertaining to system interoperability, real-time data visualization, automated detection algorithms, and predictive analytics within the framework of public health surveillance. The results show that health information systems that can work together are needed for easy sharing of data between clinical, laboratory, syndromic, and environmental data sources. This will make surveillance more complete and the data more accurate. Real-time data platforms improve situational awareness by giving different stakeholders timely, useful information that is specific to their needs. This makes it easier to coordinate quick responses and allocate resources effectively. Predictive intelligence, which includes machine learning, time-series analysis, and spatial modelling, makes it much easier to find outbreaks and speeds up response times by finding new patterns and anomalies that traditional methods might miss. The report also states that there are still problems with data quality, system interoperability, staff capacity, infrastructure limitations, and privacy and governance issues, especially in low- and middle-income countries. The proof shows how important it is to have strong technical architectures, thorough governance frameworks, and a long-term commitment to digital surveillance systems to get the most out of them. This study shows that adding interoperable systems, real-time analytics, and predictive intelligence in a planned way is very important for improving early warning systems for public health and getting ready for future health emergencies.
Keywords:
Public health surveillance; Early warning systems; Health information interoperability; Real-time data analytics; Predictive intelligence; Digital health technologies; Outbreak detection; Syndromic surveillance; Health informatics; Emergency preparedness
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0