With all the industries being disrupted by the data deluge and the technologies associated with it, it was just a matter of time before healthcare too would be engulfed. Given the very nature of this sector where a humongous amount of data is being generated on an everyday basis, and accurate decision-making in real-time is the key, one can understand that the implications of AI and data can be profound. A sector which generally helps in saving people's lives and helps improving the health of the populace will benefit greatly by technology like AI. AI use cases in healthcare are straight out of science fiction novels when one reads them, but they have already become a part the healthcare ecosystem. With AI becoming so common, one can't help but deep dive into 10 such use cases which are revolutionizing the way caregivers are doling out their services to the patients in the new patient-centric ecosystem. Today, most of the medical records are stored electronically.
As the world is booming with technology, why we should leave the healthcare sector behind. As technologies like Artificial Intelligence and Augmented Reality is revolutionizing various industry sectors, it definitely plays a significant role in healthcare future. As healthcare is one of the world's fastest as well as largest growing industry and adding technology will make a bigger and better difference. However, reports suggest that the healthcare IT market is currently a USD 187.6 billion market. With a 15.8% forecast CAGR growth, the Healthcare IT market is expected to reach 390.7 USD billion market shares by 2024.
The European Commission is launching a tender for two studies to survey and analyse progress on the digital transformation of the health and care in the EU, in particular with regard to citizens' access to their electronic health records (EHR) in the EU Member States and the development, adoption and use of artificial intelligence (AI) technologies in the health and care sector in the EU. The call for tender includes two specific studies (i.e. The maximum amount allocated to the studies is EUR 300 000 for Lot I and EUR 200 000 for Lot II. The closing date of the call is 23 September 2019 at 15:00. The detailed tender specifications and more information about the procedure and eligibility conditions are available on TED.
Administration embitters the life of medical professionals and significantly lengthens their shifts. You couldn't lure a prospective medical student into entering the profession if you said that half of the physicians' average workdays are spent entering data into EHRs and conducting clerical work, while just 27% is spent with actual patients. Could smart algorithms change this disappointing picture for the better? Technological development and research done so far tell us that they can. In the future, AI can help cut back on medical administration and thus free up the valuable time of doctors.
RenalytixAI is a developer of artificial intelligence (AI) enabled clinical diagnostic solutions for kidney disease, one of the most common and costly chronic medical conditions globally. RenalytixAI's solutions are being designed to make significant improvements in kidney disease risk assessment, clinical care and patient stratification for drug clinical trials. RenalytixAI's technology platform will draw from distinct sources of patient data, including large electronic health records, predictive blood-based biomarkers and other genomic information for analysis by learning computer algorithms. RenalytixAI intends to build a deep, unique pool of kidney disease-related data for different AI-enabled applications designed to improve predictive capability and clinical utility over time. In 2019, RenalytixAI expects to launch KidneyIntelXTM, an artificial intelligence in vitro diagnostic product intended to support physician decision making by improving identification, prediction, and risk stratification of patients with progressive kidney disease.
I am a broadly interdisciplinary artificial intelligence researcher specializing in natural language processing and methods inspired by cognition and the brain. I apply these to application areas in science and health care. A central focus of my science research is on how we can teach computers question answering in the form of passing standardized science exams, as written. In particular, I focus on methods of automated inference that generate explanations for why the answer is correct, largely using graph-based methods. In terms of health care, I study how we can use natural language processing and inference to improve electronic health records and improve nurse communication, as well as detect potentially dangerous clinical events before they happen.
Artificial intelligence (AI) is not new, but creative new projects are highlighting the adaptability and the broad utility of this technology. A recent entrant to the AI arena is RCM Brain, a startup that is focused on Revenue Cycle Management (RCM) for providers in the healthcare industry. Launched this past June, the RCM Brain solution is an AI-driven RCM rules engine and workflow automation platform built to help medical providers and third-party billing companies better manage healthcare claims processing. RCM software manages a range of functions associated with claims – including scrubbing Electronic Medical Records (EMR) systems with a bot called BETH – as it engages with data from across multiple legacy billing systems, claims data clearinghouses, and insurance (payer) platforms. According to RCM Brain president and founder John Williamson, revenue cycle management is a complex process, which may involve coding the claims with right procedure and diagnosis code (there may be up to 150 pieces of information on a claim), submitting claims to right insurance company, assuring quality of the data input, accounts receivable, including managing claims denial or no response at all, and matching bank account records with what is being paid by the insurance company, what has been paid, and what is still owed – a "triage of information" that Williamson call'posting'.
Eric Topol sees a future in which doctors use artificial intelligence (AI) to analyze billions of pieces of medical, social, genetic, and environmental information--continuously and automatically collected--to produce diagnoses and treatments individually tailored to the patient in front of them. It's a future in which algorithms digest the peculiarities of your gut's microbiome (advising, say, strawberry Danishes rather than banana nut muffins) and in which the medication prescribed for your particular flavor of diabetes was discovered by a machine that sifted through trillions of molecules to identify the most promising chemical compounds. It's a future with AI-powered electronic medical assistants--Alexas with medical school degrees--who listen to your visit, place orders, schedule follow-up, and generate the required documentation. Topol, a cardiologist, geneticist, and director of the Scripps Research Translational Institute, is the author of three books about the digitalization of health care. His most recent, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, is an exhaustive tour-de-force review of the past, present, and future of AI in medicine,brought to life with compelling personal anecdotes about his life as a patient, physician, husband, and son.
This post could also be called "everything I learned during the first year of my PhD while working with a massive electronic health record data set." Here, I'll overview the difference between categorical and quantitative variables, and I'll explain how to prepare tabular data for a neural network model, including: Best of all, broadly-applicable code is provided to do all of the above on your tabular data! A categorical variable is a variable that takes on different category names. Quantitative variables have numerical values that usually represent the result of a measurement. If your variable is the NAME of a laboratory test, then that's a nominal (categorical) variable because the values it can take on are different names of different lab tests, e.g.