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Predictive analytics is an component of advanced analytics that uses historical data along with statistical modelling, data mining, and machine learning to forecast future events (What Is Predictive Analytics? | IBM, 2023).
An increase in long-term investment in creating new technologies that use artificial intelligence and machine learning to predict future events (possibly in real time) to improve people's health is indicative of the current interest in predictive analytics for bettering health care. For the purposes of diagnosis and prognosis, predictive algorithms, also known as clinical prediction models, have historically been used to identify individuals with an increased likelihood of disease (Ben Van Calster et al., 2019).
A more thorough examination of the prevalence of disease, implementation gaps and disparities in healthcare systems, and population subgroups may be possible with the utilisation of big data sources. For instance, by employing small-area analysis, we may be able to identify isolated instances of disparities in the application of health interventions that are frequently hidden by analyses carried out on larger geographic units, like states or counties (Khoury et al., 2018).
References:
1. What is predictive analytics? | IBM. (2023). Ibm.com. //www.ibm.com/topics/predictive-analytics
2. Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019). Predictive analytics in health care: how can we know it works?. Journal of the American Medical Informatics Association : JAMIA, 26(12), 1651–1654. //doi.org/10.1093/jamia/ocz130
3. Khoury, M. J., Engelgau, M. M., Chambers, D. A., & Mensah, G. A. (2018). Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health? Public Health Genomics, 21(5-6), 244–250. //doi.org/10.1159/000501465
Posted : January 23, 2024 1:27 pm
In recent years, the field of healthcare has witnessed a remarkable transformation in the ever-advancing realm of deep learning and artificial intelligence. These technologies have emerged as powerful tools in disease diagnosis, personalized treatment, predictive analytics, and medical research, promising to revolutionize patient care and the healthcare industry [1].
Increasingly, a large volume of health- and non-health-related data from multiple sources is becoming available that has the potential to drive precision implementation. The term “big data” is often used as a buzzword to refer to large data sets that require new approaches to manipulation, analysis, interpretation, and integration. Such data include genomic and other biomarkers, as well as sociodemographic, environmental, geographic, and other information. Our ability to improve population health depends to a large extent on collecting and analyzing the best available population-level data on the burden and causes of disease distribution, as well as on the level of uptake of evidence-based interventions that can improve health for all [2]. In an era of personalized medicine, predictive algorithms are used to make clinical management decisions based on individual patient characteristics (rather than on population averages) and to counsel patients. The rate at which new algorithms are published shows no sign of abating, particularly with the increasing availability of Big Data, medical imaging, routinely collected electronic health records, and national registry data [3].
Predictive analytics refers to the skills, practices, applications, and techniques used to analyze current and historical data to predict future
or even unknown occurrences and patterns. Predictive analytics can be utilized and sustained by an innovation ecosystem in which multiple actors (e.g., hospital patients, healthcare professionals, hospital management, general practitioners, health inspectorates, and health insurers), supported by information systems (IS) and existing medical data analytics pipelines, collaboratively gather, exchange and analyze the extensive flow of data necessary for its operability. Thereby establishing favorable deployment conditions (e.g., circumstances under which deployment is deemed effective) for predictive analytics and jointly creating an innovative focal offering. Predictive analytics is particularly relevant for hospitals from a medical and
managerial perspective. From a medical perspective, predictive analytics can be used to, for instance, practice evidence-based medicine, identify risk factors, and test intervention strategies, resulting in improved and more equitable health outcomes for patients. From a managerial perspective, predictive analytics can be used to, for instance, exert quality control, manage hospital capacity, and streamline healthcare paths, generating an improved and more equitable health service experience for patients. However, despite the many opportunities, hospitals have not yet fully grasped the value of predictive analytics and limited research has been conducted on the role of predictive analytics in fostering patient agility and patient value in hospitals [4].
or even unknown occurrences and patterns. Predictive analytics can be utilized and sustained by an innovation ecosystem in which multiple actors (e.g., hospital patients, healthcare professionals, hospital management, general practitioners, health inspectorates, and health insurers), supported by information systems (IS) and existing medical data analytics pipelines, collaboratively gather, exchange and analyze the extensive flow of data necessary for its operability. Thereby establishing favorable deployment conditions (e.g., circumstances under which deployment is deemed effective) for predictive analytics and jointly creating an innovative focal offering. Predictive analytics is particularly relevant for hospitals from a medical and
managerial perspective. From a medical perspective, predictive analytics can be used to, for instance, practice evidence-based medicine, identify risk factors, and test intervention strategies, resulting in improved and more equitable health outcomes for patients. From a managerial perspective, predictive analytics can be used to, for instance, exert quality control, manage hospital capacity, and streamline healthcare paths, generating an improved and more equitable health service experience for patients. However, despite the many opportunities, hospitals have not yet fully grasped the value of predictive analytics and limited research has been conducted on the role of predictive analytics in fostering patient agility and patient value in hospitals [4].
Public health and implementation scientists explore strategies for improving the uptake of evidence-based health interventions that target multiple levels (from patient/person level to provider, system, community, and policy interventions) [5].
References:
[1] Kanchan Naithani, & Tiwari, S. (2023). Deep Learning for the Intersection of Ethics and Privacy in Healthcare. Advances in Systems Analysis, Software Engineering, and High Performance Computing Book Series, 154–191. //doi.org/10.4018/978-1-6684-8531-6.ch008
[2] Dolley, S. (2018). Big Data’s Role in Precision Public Health. Frontiers in Public Health, 6. //doi.org/10.3389/fpubh.2018.00068
[3] Shah ND , Steyerberg EW, Kent DM. Big data and predictive analytics: recalibrating expectations. JAMA2018; 3201: 27–8.
[4]Damien S.E. Broekharst, van, Ooms, W., Helms, R. W., & Roijakkers, N. (2023). Deploying predictive analytics to enhance patient agility and patient value in hospitals: A position paper and research proposal. Healthcare Analytics, 3, 100141–100141. //doi.org/10.1016/j.health.2023.100141
[5] Chambers DA. Increasing connectivity between implementation science and public health. Advancing methodology, evidence integration, and sustainability. Annu Rev Public Health. 2018 Apr;39(1):1–4. //doi.org/10.1146/annurev-publhealth-110717-045850 [PubMed]0163-7525
Posted : January 29, 2024 12:34 pm