During your stay in a hospital, computer systems are collecting and analyzing all sorts of data about you. In the background of all the beeping and gadgetry, an electronic medical record contains thousands of bits of information about your medical history, vital signs and laboratory results. Sentara Healthcare is now deploying artificial intelligence to use that data to stop patients from contracting life-threatening sepsis. Earlier this year the system launched a sepsis prediction tool that alerts doctors and nurses when a patient is at risk of developing the deadly infection. The tool "looks at relationships in order to predict what might happen in the future," said Dr. David Mohr, Sentara's vice president of clinical informatics and transformation.
New article says that to take full advantage of deep-learning solutions in healthcare, the US and China should collaborate, not compete. In a new commentary article, titled'It Takes a Planet', Eric Topol, MD, of Scripps Research and Kai-Fu Li, PhD, CEO of the China-based tech investment firm Sinovation Ventures have argued for more collaboration between China and the US on artificial intelligence (AI) development. This comes in the wake of the US government ordering the AI company iCarbonX in China to divest its majority ownership stake in the Massachusetts-based company PatientsLikeMe. "Chinese academics and companies already have unfettered access to personal health data," they write. "To compete in AI health, US companies will need access to clinical data on a similar scale. How will that be possible if the current isolationist policy continues?"
Many hospitals are facing trouble with low compliance rates of usage reporting inside the operating room, inaccurate charge capture, meeting FDA requirements regarding digital updates to the patient's file and countless coding errors. These problems all have the potential to cause a financial loss. Hospitals use advanced software solutions to improve processes, streamline workflow and optimize resources. By 2026, the healthcare information industry is forecasted to grow by 8.2%. Yet while these solutions specialize in data management and analyze procurement processes, they are not suited to the specific needs and work conditions in hospital operating rooms, resulting in deficient data collection.
Technology has been the greatest phenomenon in transforming industries and behaviors across the globe. The healthcare industry has seen technology inspire progress and innovation. The Healthcare IT Market is expected to be $390.7 Billion by 2024 and is growing at a faster rate than the GDP of most countries. It costs nearly $250 Billion to process 30 Billion healthcare transactions each year. The Healthcare expenditures for the U.S is projected to be $5.4 Trillion by 2024 representing an average annual rate of 19.6% of GDP.
The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients'in the wild' and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare.
Artificial intelligence can help transform health care by improving diagnosis, treatment, the delivery of patient care, and the efficiency of healthcare systems. But AI is only as good as the data it's built on. AI developers in the health sector are facing multiple challenges including limited access to health data, poor data quality, and concerns over the ethical use of data for AI. The Office of the Chief Technology Officer (CTO) in the U.S. Department of Health and Human Services (HHS) is now exploring the potential for a departmentwide AI strategy to facilitate the development of AI for health. As one important first step, the HHS Office of the CTO and the Center for Open Data Enterprise (CODE) co-hosted a roundtable in April to gather input from stakeholders outside of HHS.
The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning (a subset of artificial intelligence) plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. As computer scientist Sebastian Thrum told the New Yorker in a recent article titled "A.I. Versus M.D., "Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful." Despite warnings from some doctors that things are moving too fast, the rate of progress keeps increasing. And for many, that's as it should be. "AI is the future of healthcare," Fatima Paruk, CMO of Chicago-based Allscripts Analytics, said in 2017. She went on to explain how critical it would be in the ensuing few years and beyond -- in the care management of prevalent chronic diseases; in the leveraging of "patient-centered health data with external influences such as pollution exposure, weather factors and economic factors to generate precision medicine solutions customized to individual characteristics"; in the use of genetic information "within care management and precision medicine to uncover the best possible medical treatment plans." "AI will affect physicians and hospitals, as it will play a key role in clinical decision support, enabling earlier identification of disease, and tailored treatment plans to ensure optimal outcomes," Paruk explained. "It can also be used to demonstrate and educate patients on potential disease pathways and outcomes given different treatment options.
A visit to the doctor seems one-on-one. But how will that feeling change when the data gleaned from that interaction takes on unprecedented value? It's a question that doctors and health regulators are grappling with as algorithms learn how to spot pneumonia, and health data becomes the chaff needed to train artificial intelligence. "Previously, the patient is agreeing to supply their very intimate personal information ... to the doctor to help with the diagnosis and management of their own health," said Jacob Jaremko, an associate professor in radiology and diagnostic imaging at the University of Alberta. You provide, for your own care, for your own benefit ... your data."
Take a look around any big hospital and you'll find plenty of imposing technology: surgical robots, artificial organs, wireless brain sensors, three-dimensional imaging visualizations. But you have to look harder to find what's been touted for years as the future of medicine: artificial intelligence, or the use of computers to reason, learn and make critical decisions in patient care, with little or no human involvement. The medical field's lofty dreams of unleashing the power of artificial intelligence to transform medicine have yet to materialize in a major way. The thought of replacing doctors with machines remains a science-fiction fantasy. Even so, health-information experts say artificial intelligence has its place and can perform valuable tasks, from helping doctors identify diseases earlier to matching call-center customers at an insurance company with the person most qualified to help.
An advanced research arm of the U.S. government's intelligence community is looking to develop AI capable of tracking people across a vast surveillance network. As reported by Nextgov, the Intelligence Advanced Research Projects Activity (IARPA) has put out a call for more information on developing an algorithm that can be trained to identify targets by visually analyzing swaths of security camera footage. The goal, says the request, is to be able to identify and track subjects across areas as large as six miles in an effort to reconstruct crime scenes, protect military operations, and monitor critical infrastructure facilities. To develop the technology, IARPA will collect nearly 1,000 hours of video surveillance from at least 20 camera networks and then, using that sample, test various algorithms effectiveness. The agency's interest in AI-based surveillance technology mirrors a broader movement from governments and intelligence communities around the globe, many of whom have ramped up efforts to develop and scale systems.