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Tag: big data

Big Data and the Future of Health Analytics

Article Excerpt: One of the most prominent changes in healthcare has been the advent of a digital revolution in the industry. In what has been described as the “Uberization” of healthcare, key players have attempted to leverage the rapid developments in technology to disrupt patient care delivery and gain a competitive advantage. Healthcare systems and providers have now adopted electronic health records, remote monitoring systems, telemedicine, and other technologies to transform patient care. This transformation has seen health data extensively stored, shared, accessed, analyzed, and used in digital platforms, including wearable devices, smartphone apps, medical devices, and AI-driven models. Consequently, this shift has provided healthcare systems and other stakeholders access to a digital universe with large volumes of useful information that is integral to driving topline results and improving healthcare outcomes.

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Article Source: Corporate Wellness Magazine


How Wearables Can Fight Addictions

Article Excerpt: As technology progresses and continues to rid us of many of our natural limitations and ailments, it is often accused of spawning new problems. For instance, the proliferation of the internet, and the dark web, in particular, has made addictive opioids more accessible than before. Although news like these show modern technology in a bad light, the positive impact made by technology is much more significant and should not be ignored. In fact, modern technological applications like AI, big data, and IoT are offering us solutions to most of our illnesses, saving thousands of lives every year.

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Article Source: BBN Times


Risk Factors for opioid relapse differ between men and women

Article Excerpt: A new USC study finds that risk factors for relapse following treatment for opioid use disorder vary significantly by gender, a discovery that may result in better targeted treatment with lasting results. The study, recently published in the journal Addiction, is the first to use machine learning techniques to process large data sets and identify risk factors for relapse… The study authors say future relapse prevention treatment research should explore ways to mitigate these specific and different vulnerabilities for men and women. In addition, they believe machine learning approaches should be more widely integrated into addiction research to better understand the complexities of how demographic, psychological and behavioral variables may increase the odds of relapse.

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Article Source: USC News


Using Big Data to Identify Genetic, Neural Bases for Substance Use Disorder

Article Excerpt: One of the challenges for researchers studying SUDs (substance use disorders) is that there might be different underlying mechanisms or pathways that cause someone to become addicted to a certain substance, and the specific neurological and genetic factors that account for heterogeneous clinical manifestation are poorly understood. Professor Jinbo Bi in the Department of Computer Science and Engineering at the University of Connecticut has received a $1.7 million grant from the National Institute on Drug Abuse to develop machine learning algorithms to help identify SUD subcategories based on clinical, neuroimaging, and genetic data.

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Article Source: Mirage News


Using Analytics to Attack Addiction

Article Excerpt: Substance abuse disorder, or addiction, is a disease affecting more than 20 million Americans. Talk to anyone who works with those who are battling addiction and they will tell you that only through understanding all of the unique circumstances someone faces can you determine which treatment methods have the best chance of leading to long-term sobriety and health. The renowned Minnesota based Hazelden Betty Ford Foundation has taken that approach in treating its patients, but recently its leaders realized they had not applied that same thinking to the hundreds of thousands of patient records accumulated at its facilities over the years. To do that, they turned to the Carlson School’s Analytics Lab. During a semester-long project, a team of five Masters of Business Analytics students used artificial intelligence to analyze more than 250,000 records in search of data patterns.

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Article Source: University of Minnesota News and Events


How BIg Data Can Save Lives in the Opioid Crisis

Article Excerpt: The use of big data to address the opioid epidemic in Massachusetts poses ethical concerns that could undermine its benefits without clear governance guidelines that protect and respect patients and society, a University of Massachusetts Amherst study concludes… Special consideration should be given to people with opioid use disorder, the study emphasizes. “When considering big data policies and procedures, it may be useful to view individuals with OUD as a population whose status warrants added protections to guard against potential harms,” the paper concludes.

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Article Source: Technology Networks. Original story from University of Massachusetts Amherst.


COVID-19 risk and outcomes in patients with substance use disorders: Analyses from electronic health records in the United States

Wang Q, Kaelber D, Xu R, Volkow N. (2020). COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States. Molecular Psychiatry. doi: 10.1038/s41380-020-00880-7

Researchers conducted a retrospective case-control study of electronic health record (EHR) data from 73,099,850 U.S. adults to analyze the risks and outcomes for COVID-19 among patients with substance use disorder (SUD). Read More


Big Data Takes on the Silent Epidemic of Undiagnosed Behavioral Health

Article Excerpt: Prior to the COVID-19 pandemic, a study found that almost half of Americans will experience an episode of mental illness in their lives, but may never get the diagnosis and treatment they need. In a more recent finding, over 25% of American adults now meet the criteria for a diagnosis of severe mental distress…However, it is now possible to identify those with unmanaged or undermanaged health conditions due to underlying behavioral and social barriers, and proactively reach out to them with an individualized approach. Advances in machine learning have opened up the ability to analyze medical claims and other data to predict the probability of an underlying behavioral health condition – like anxiety, depression, or substance abuse – and to tailor outreach appropriately to encourage engagement.

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Article Source: HIT Consultant


Real-Time Data Are Essential for COVID-19. They’re Just as Important for the Opioid Overdose Crisis

Article Excerpt: The public has benefited from seeing the spread of the Covid-19 pandemic in the moment. Although it is scary seeing the numbers of diagnoses and deaths rising day by day, these data help bring clarity and accountability to an ongoing crisis that requires both. It is time to bring this kind of real-time outcome data to America’s addiction crisis and make it available to the public. It’s the only way of knowing if what we’re doing to address the problem is making a difference.

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Article Source: Stat News