Hospitals in Victoria's South West, including public health agencies under the South West Alliance of Rural Health and Barwon Health in Geelong, are set to roll out a data platform capable of real-time analysis using AI, machine learning, as well as business and clinical intelligence. The health organisations will be deploying the IRIS for Health platform by global tech provider InterSystems. The data platform, according to InterSystems's website, is specifically engineered to extract value from healthcare data. It is a standards-based platform that is able to read and write Health Level 7's Fast Healthcare Interoperability Resources (HL7 FHIR) for developing healthcare applications. It is also capable of ingesting, processing and storing transaction data "at high rates" while simultaneously processing high volume analytic workloads involving historical and real-time data. While the health providers have interconnected systems, including clinical and patient administration systems, specialist healthcare applications and data analytics solutions, they don't have a single data repository supporting real-time data analysis.
A federal rule that requires health care providers to offer patients free, convenient and secure electronic access to their personal medical records went into effect earlier this year. However, providing patients with access to clinician notes, test results, progress documentation and other records doesn't automatically equip them to understand those records or make appropriate health decisions based on what they read. "Medicalese" can trip up even the most highly educated layperson, and studies have shown that low health literacy is associated with poor health outcomes. University of Notre Dame researcher John Lalor, an assistant professor of information technology, analytics and operations at the Mendoza College of Business, is part of a team working on a web-based natural language processing system that could increase the health literacy of patients who access their records through a patient portal. NoteAid, a project based at the University of Massachusetts Amherst, conveniently translates medical jargon for health care consumers.
Several artificial intelligence algorithms developed by Epic Systems, the nation's largest electronic health record vendor, are delivering inaccurate or irrelevant information to hospitals about the care of seriously ill patients, contrasting sharply with the company's published claims, a STAT investigation found. Employees of several major health systems said they were particularly concerned about Epic's algorithm for predicting sepsis, a life-threatening complication of infection. The algorithm, they said, routinely fails to identify the condition in advance, and triggers frequent false alarms. Some hospitals reported a benefit for patients after fine-tuning the model, but that process took at least a year. Unlock this article by subscribing to STAT and enjoy your first 30 days free!
Niki Trigoni discusses how Navenio has helped hospitals during the pandemic and shares her thoughts on digital transformation. Niki Trigoni is a professor of computer science at the University of Oxford where she leads the Cyber Physical Systems group. She has 15 years of experience in intelligent sensor systems and has won several awards for her group's work on indoor and underground positioning. She is also a founder of the Centre for Doctoral Training in autonomous and intelligent machines and systems, which aims to deliver highly trained individuals versed in the underpinning sciences of robotics, computer vision, wireless embedded systems, machine learning, control and verification. Currently Trigoni is the chief technology officer of Navenio, an AI-led indoor location-based platform that aims to improve workforce efficiency in hospitals.
Healthcare innovation has helped healthcare providers offer better care and unlock new ways to enhanced treatment for larger population groups. Technology advancements such as Artificial Intelligence and machine learning can offer innovative solutions to the healthcare sector by improving care delivery options and automating tasks that can reduce administrative burden. The Healthcare Innovation Forum discusses how machine learning and AI have revolutionized healthcare through efficient data analysis which has facilitated the decision-making process. By integrating the power of AI and machine learning the healthcare ecosystem can benefit greatly through automation of manual tasks, analyzing large data to improve health outcome levels, and lowering healthcare costs. According to Business Insider, 30% of healthcare costs are related to administrative and operational tasks.
Apollo Hospitals confidently asserts that AI in healthcare is more of a need than a luxury. AI solutions and AI-powered machines are the crucial pieces of hospital infrastructure today. Reportedly, around 90% of hospitals, globally, have artificial intelligence strategies in place. Such a surge in AI adoption and integration in healthcare is justified and amplified by the COVID-19 outbreak. Sean Lane, the CEO of Olive has noted that it is incredibly promising to witness the growth of AI adoption in healthcare.
Most of us have had the experience of sending a text or email that came across sounding insensitive or angry, even though that wasn't our intent. Unfortunately, the lack of social cues in such messaging makes it much easier to be misinterpreted. Depending on the communication, this can lead to misunderstandings, hurt feelings or worse. That's a shortcoming Bellevue, Wash.-based mpathic wants to correct using empathic AI. Drawing on insights and datasets assembled over the past decade, mpathic has set out to promote human connection and understanding in the workplace.
"Like all new technology, artificial intelligence…can also be misused and cause harm", warmed Tedros Adhanom Ghebreyesus, Director-General of the World health Organization (WHO). To regulate and govern AI, WHO published new guidance that provides six principles to limit the risks and maximize the opportunities intrinsic to AI for health. Artificial Intelligence (#AI) holds enormous potential for improving the health of millions of people around, but only if ethics & human rights are put at the heart of its design, deployment, & use. WHO's Ethics and governance of artificial intelligence for health report points out that AI can be and, in some wealthy countries is already being, used to improve the speed and accuracy of diagnosis and screening for diseases; assist with clinical care; strengthen health research and drug development; and support diverse public health interventions, including outbreak response and health systems management. AI could also empower patients to take greater control of their own health care and enable resource-poor countries to bridge health service access gaps.
Society is entering the age of artificial intelligence. Significant players in every industry are implementing narrow artificial intelligence (NAI) to improve their business processes. As a consequence, no element of the global insurance business model will be untouched. Most insurance product lines will need to be reengineered to reflect the new risks arising out of the adoption and deployment of NAI. Insurers looking to take advantage of the opportunities that will result from the adoption of NAI or looking to mitigate the unintended risks associated with NAI will have to do research or partner with experts.
The trade-off between widespread technology adoption and responsible use often lies on the spectrum of privacy. When it comes to technologies fueled by data, such as artificial intelligence (AI), it's even harder to strike the balance between equitable access and inherent risk. This is felt heavily in the healthcare industry, as regulations around information sharing are generally more stringent than those for other verticals. Because of laws like HIPAA, healthcare has had a head start in changing its approach to handling personally identifiable information (PII) and other sensitive information, while still leveraging technology and working with third parties to streamline processes. And they've figured out how to do this without sharing their valuable data. This is contradictory to the long-held belief that SaaS companies require customer data to improve services and get accurate, unbiased insights--it's simply not the case.