Predictive Analytics in Healthcare: Big Data, Better Decisions

The healthcare systems worldwide are moving towards the concept of predictive analytics, using data on patients for better and effective treatment and to organize usage of resources effectively. Given the exponential growth in digitalization and electronic health records (EHRs), machine learning (ML) and big data analytical models present the greatest forms of predictive health care. Hence, this comprehensive review will endeavor to make an evidence based, up-to-data compilation of past, current and future findings on data analytics applications in the domain of predictive healthcare. Materials and Methods: A comprehensive bibliographic database was searched using PubMed, Scopus, and Google Scholar electronic databases. Original articles published between January, 2010 and December, 2023 in peer reviewed international journals were retrieved that mainly dealt with predictive analytics in healthcare employing either machine learning, artificial intelligence, and big data processing methods. The general and specific data sources, the techniques used fo analysis, the clinical use of the method and the efficiency results were obtained. Therefore, out of 823 identified studies, 55 papers were included into the research, indicating that the use of predictive analytics is expanding across the healthcare spectrum. These sources included EHR, claim data, gensomic data and wearable data. Deep learning and ensemble method were proved to have better prediction accuracy than traditional statistical methods. Core uses included disease risk profiling, patient characterization, risk of readmission, clinical decision making, and personalized medicine. Other limitations were also highlighted in the study to include issues concerning data quality, or the explanation of the created models and balancing of fairness and equality when making the models. Application of predictive analytics for healthcare is an ambitious step towards probability of early diagnosis of diseases, appropriate therapeutic approach, and optimal usage of resources. Yet, training, proper external validation, model updating, and integration of the model into clinical routines are a prerequisite for success. Shoring up, data governance, privacy or any form of prejudice within algorithms also remain crucial. The information and experience described in this review is principally concerned with the role of data analysis in the predictive health system. As healthcare organizations are producing increasing amount of data, use of the sophisticated data analysis methods will be crucial for achieving better clinical results, better organizational performance and innovation in the delivery of care.