Opioid Crisis and Data Analytics: Preventing Overdoses through Predictive Models
Vedamurthy Gejjegondanahalli Yogeshappa, Architect, Leading Health Technology, Dallas, United States
Jayanna Hallur, Sr. Manager, Richmond-VA
Praveen Kuruvangi Parameshwara, Sr. Data Scientist, SFO-CA
Abstract
The opioid problem continues to be something that is quite widespread in its effects on the population and contributes to thousands of deaths by overdose each year. Even after concerted efforts being made by governments and healthcare systems, deaths resulting from opioids continue to present a very difficult nut to crack. One perfect solution could be the deployment of data analytics to be able to prevent overdose incidents before they happen. This journal article focuses on the attempt to introduce a new concept in the healthcare and law enforcement areas for finding high-risk people and areas. It also talks about how the application of algorithms such as machine learning and natural language processing, among others, are of help in identifying abusive patterns, prescription anomalies or socioeconomic risks that come with prescription. The article describes the expected advantages of real-time monitoring, data aggregation from various sources, including EHRs, PDMPs, and social media, and the development of per-geography and demographic methods and models. The research also addresses ethical aspects of using data as well as privacy issues and a probability of bias in a predictive model, insisting on reporting all the methods used and frequent checks to avoid possible misapplications. Additionally, it assesses the involvement of healthcare provider implementation, data science, and policy in preventing the opioid crisis. In this paper, several advanced machine learning techniques, which include decision trees and random forests, as well as the more complex deep learning algorithms, show how the identification of effective early interventions, which are often hard to design, can help reduce overdose and enhance patient outcomes. As with any analytical approach to a particular problem, we have strengths and weaknesses when applying data analytics to the opioid crisis. Machine learning algorithms themselves have been shown to be highly accurate at predicting those who may become opioid users; however, their implementation in practice entails embedding models into the current healthcare frameworks, stakeholder coordination, and addressing ethical issues. The conclusion insists on the further development of research in the sphere of predictive analytics in cases of opioid overdose, as well as the legal regulation of patient rights.
Keywords
Opioid crisis, Data Analytics, Predictive models, Machine learning, Healthcare data, Public health
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