The primary focus of these initiatives is on health care providers, helping them develop treatment approaches that are most effective for individual patients. One consortium of hospitals, researchers, and a startup, for example, is conducting "Project Survival" to identify effective biomarkers for pancreatic cancer.3 In other firms, real-world data sources are being used to identify molecules that might be particularly effective (or ineffective) in clinical trials. Another long-term challenge to be addressed by the life sciences and health care industry is collaboration and integration of data. Project Survival, for example--an effort to find a pancreatic cancer biomarker--involves collaboration among a big data drug development startup (Berg Health), an academic medical center (Beth Israel Deaconess in Boston), a nonprofit (Cancer Research and Biostatistics), and a network of oncology clinicians and researchers (the Pancreatic Research Team).
"We hope this will improve quality of care for hospital patients and will bring significant cost savings to hospital systems." So instead of relying on roads for transportation, doctors, nurses, and technicians can send and receive test samples and results via the drone, and the system's accompanying app. Using the Matternet system, a technician would package the blood sample in a standardized box bearing a QR code. In countries where Doctors Without Borders work, medical drone delivery is crucial since roads and safe, timely passage are rarely an option.
Franz Inc., in partnership with Montefiore Health System, is bringing the data lake to health IT using Franz's semantic graph database technology. Until its venture into the healthcare and pharmaceutical industries over the past few years, the 31-year-old Oakland, Calif., company had done business mainly in the worlds of national defense and intelligence, into which it sold its artificial intelligence-based triple store database that uses semantic, instead of relational, database technology. Using RDF technology, triple stores are a way to manage, manipulate and query many triples. Unlike most relational databases' linear representation and analysis of data, Franz's semantic graph database technology employs visual and spatial charting with which users can graphically see data elements and their relationships.
In effect, Live Data Map acts as a knowledge graph and metadata repository, and can help automation of data discovery and preparation tasks. Informatica company leaders pointed to Live Data Map as one among several signs of the company's commitment to innovation. They indicated the company has worked to improve performance of the open source Titan graph database on which Live Data Map, originally discussed at last year's edition of the conference, was built. He suggested Live Data Map and other technology activity at Informatica World 2016 show a company still very much in the game.
Most chatbots use multiple technologies: natural language processing, knowledge management and sentiment analysis. Typically, the natural language processing will identify the intent of a question with some level of confidence and then, based on the confidence level, the chatbot will either ask a follow-up or disambiguate the question for the user. In addition to natural language processing technology, chatbots typically also rely on knowledge management systems. AI chatbots have been used with varying levels of success in healthcare to date, addressing use-cases including helping consumers select a benefit plan, providing customer service responses, helping triage symptoms, and guiding consumers to resources.
"Knowledge from systematically analyzing missed opportunities in correct or timely diagnosis will inform improvements and create a learning health system for diagnosis," Dr. Singh says. The network, known as Pride, short for Primary Care Research in Diagnostic Errors, plans to identify, analyze and classify diagnostic errors and delays with the help of electronic medical records, to develop and share interventions that can overcome diagnostic errors and delays, especially in primary care. It also plans to help doctors avoid ordering unnecessary and wasteful tests by developing "principles of conservative diagnosis," says Gordon Schiff, associate director of Brigham and Women's division of general internal medicine and quality and safety director at Harvard Medical School's Center for Primary Care. In response, the project plans to develop and test "loop-closing" tools for electronically tracking doctors' recommendations of tests and procedures that aren't carried out.
Freed from human-dictated logic, modern AI systems use multi-layered neural networks to store and categorize information in their own ways, and find their own "organic" ways of generalizing from examples, finding relationships, categorizing data and finding patterns. Poor data quality or training can result in biased outcomes -- essentially, a poorly educated computer that will not be a good problem solver going forward. Address the black box: The black box nature of AI systems is not simply an interesting feature; rather, it creates a set of novel issues in terms of risk allocation. In addition, modern AI systems may create insights that present acute sensitivity concerns, and AI functionalities may create new relationships among data owners.
Developed by the London-based HR company Saberr, it asks about workplace dynamics and provides the team with reports. A unit within the UK's National Health Service is trialling it, as are 10 companies, including Unilever and Logitech. "Team members start by saying hello to CoachBot, and are then asked about who they are and what they do," says Tom Marsden, Saberr's CEO. Questions Coachbot asks include "Is your team productive?"
The AI consultant, dubbed "Frankie", joined NIB's customer service team in a bid to help provide convenient, timely responses to health cover-related customer enquiries. "The idea behind it is really so we can bring a greater level of choice and a greater level of service to our customers," Mills said, noting that Frankie's responsibilities will be built on as the cognitive learning kicks in. In parallel to that, Mills said NIB is also developing some AI and chatbot technology for its Australian domestic health insurance business and is currently running a pilot based on Amazon Web Services' (AWS) Lex, with the final bot expected to be launched in the near future. "We have made a heavy investment in cloud; a lot of our digital footprint -- our systems of engagement -- are delivered through an AWS platform," Mills explained.
But a type of artificial intelligence known as deep learning could soon help medical experts pinpoint problems faster and more accurately, says Dr. Michalski, executive director of the Boston-based Center for Clinical Data Science at Massachusetts General Hospital and Brigham and Women's Hospital. Deep learning includes algorithms, or computer programs, that search for, identify and analyze problems without direction from people, though many humans still guide the algorithms today. More hospitals, universities and tech companies are training and testing systems that use AI, including deep learning, to see how they might be used to improve the medical diagnosis process. "A lot of what we're doing right now is taking what has been proven in the lab and trying to commercialize it," says Steve Tolle, global vice president and chief strategist for IBM Watson Health Imaging.