Machine learning (ML) refers to a set of computational algorithms that apply statistical modeling to a specific task. Think of a task as a question or an input. The algorithm uses logic applied to that question to generate an answer or output. To emphasize the distinction between AI and ML, remember that AI refers to computational systems which mimic human behavior—ML refers to specific types of algorithms. Systems are built from algorithms; algorithms work inside of systems.
MHAOnline.com Features - What's Happening in Healthcare Administration?
This features section explores career paths, professors to know, industry changes, and other forces shaping the experience of online MHA students. These features cover the realities of pursuing an online degree, including applications tips, internship requirements, scholarship prospects, and advice for finding a job upon graduation.
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Artificial intelligence is not an easy technology to implement, even on a limited scale for a controlled pilot. The processes to evaluate, implement, validate, and scale AI tools within a healthcare application are especially complex and resource-intensive.
In 2018, approximately 30 percent of the world’s data volume was being generated by the healthcare industry. Electronic health records, remote patient monitoring, wearable gadgets, and other advancements have almost certainly edged up the share even further.
The last Baby Boomer turns 65 in 2030. That’s also the year when one in five Americans will be over 65, and an estimated nine million will be over the age of 85—a nearly 50 percent increase from 2020.
Professional certification in a particular area of healthcare administration not only proves that the holder is competent in a specialized area, it also shows one’s commitment to continued education, professional networking, and industry-recognized best practices.
Today’s healthcare administrators face stormy waters. Public health emergency funding is ending, while inflation has kept costs high. Meanwhile, understaffing creates its own vicious cycle, increasing burnout and worsening working conditions, making recruitment and retention even more difficult.
The way in which the United States finances, delivers, and regulates care in nursing home settings is unsustainable (NASEM 2023). Immediate action is necessary to correct decades of underinvestment and unaccountability. Advocates are heartened that the issue is receiving significant attention from the federal government and the general public, but disagreements remain over addressing the underlying issues.
It’s time to revolutionize long-term care. This segment of the healthcare system has endured enormous challenges over the last few years. Over 200,000 long-term care facility residents and staff died from Covid-19 during the pandemic. The Baby Boomer generation has entered old age, a demographic shift that reinforces how important long-term care and nursing home care are. Administrators and other staff in long-term care have had to repeatedly make do with too few resources and too much regulation.
Learn how ChatGPT and other new AI chatbots do that by slashing the time clinicians and administrators spend on paperwork, giving physicians more time to interact with their patients. Then, find out how AI is specifically cutting the time spent on insurance authorizations and administrative reporting, two of the leading causes of clinician burnout. Finally, discover why healthcare administrators and clinicians who understand how to use AI platforms effectively will enjoy competitive advantages in the job market, plus more promotions and raises.