Health Catalyst introduced Touchstone at HIMSS18 and, in so doing, described it as a performance discovery, prioritization, benchmarking and recommendation tool. "Touchstone is built from the ground up on the latest AI and software from Silicon Valley," said Dale Sanders, President of Technology, Health Catalyst. "Touchstone's recommendation engine, which borrows from Amazon and Netflix, gives you not just comparative benchmarks but recommendations to improve your performance against benchmarks." The technology includes risk models based on artificial intelligence and anomaly detection algorithms that hospitals can use to pinpoint underperforming areas. Touchstone performs risk-adjusted benchmarking by culling data in claims, cost-accounting systems, EHRs, external benchmarks and operations to risk-adjust benchmarking, to "guide users to the data and analyses of greatest relevance to their work and to the organization's goals," the company said.
"IoT helps cities to predict accidents and crime as well as gives doctors real-time insight into information from pacemakers or biochips," said Ahmed Banafa of San Jose State University at a recent webinar. "IoT optimizes productivity across industries through on equipment and machinery, creates truly smart homes with connected appliances, and provides critical communication between self-driving cars."
Artificial intelligence (AI) today is the new frontier in the digital transformation journey enterprises have already embarked on. But adoption to solve real problems and drive business outcomes has been slow. Driving up adoption is critical to unlock the real promise of AI and is going to depend on how we approach AI. And that opportunity is in front of us thanks to industry-optimized augmented intelligence.
Marketing typically has the largest discretionary budget in any organization because of the variety of activities we do, but now it also has the largest discretionary technology budget. That shift of dollars away from IT has been causing tensions for some time, but marketers now must be at the head of the table when purchasing everything from CRM, to business intelligence and analytics tools, to ecommerce platforms, and of course the website. Just like technology, customer experience budget and planning will move more towards marketing--as will customer satisfaction KPIs. The entire customer journey from pre-sale to customer advocacy is part of the overall brand experience. "Predictive analytics driven by AI and machine learning are going to change the way we do just about everything" One of the biggest obstacles marketers still run into is resistance to change.
The Industrial Internet of Things has already started changing the way businesses have been handling their operations or at least has made them comprehend its potential, showing what it can do for them. But we are yet to realize the real value of IIoT, which can be gained by blending it with machine learning.
When considering a startup, especially an early-stage startup, investors want to conduct as much due diligence as possible. What little data they can gather is scattered all over different sources including Crunchbase, LinkedIn, Pitchbooks, company websites, etc. Consolidating this data takes a great amount of time and effort. Furthermore, the data sets can be incomplete or biased depending on the search queries -- imagine overlooking a keyword. To make the due diligence process fairer and less cumbersome for investors, various platforms are using machine learning (ML) to pull together information about startups from all available resources to help investors assess companies and investment opportunities. But where machine learning really shines is in the interplay of data-driven insights that are qualified by human intuition and personal experience.
The digital revolution has brought with it a new way of thinking about manufacturing and operations. Emerging challenges associated with logistics and energy costs are influencing global production and associated distribution decisions. Significant advances in technology, including big data analytics, AI, Internet of Things, robotics and additive manufacturing, are shifting the capabilities and value proposition of global manufacturing. Total delivered cost must be analyzed to determine the best places to locate sources of supply, manufacturing and assembly operations around the world. In other words we need a digital transformation.