Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower. We've been trying to get a handle on who has actually adopted AI/ML and to what extent. So we've been combing through these great new data sources from the good folks at McKinsey in their AI Adoption Survey, and Stanford's Human-Centered AI Institute 2018 AI Index which we used in our previous reporting. But one thing kept bothering me.

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. Think of how efficiently (or not) Gmail detects spam emails, or how good text-to-speech has become with the rise of Siri, Alexa, and Google Home. Machine Learning is an enormous field, and today we'll be working to analyze just a small subset of it. Supervised learning is one of Machine Learning's subfields. The idea behind Supervised Learning is that you first teach a system to understand your past data by providing many examples to a specific problem and desired output.

Manually checking models for compliance against building regulation is a time-consuming task for architects and construction engineers. There is thus a need for algorithms that process information from construction projects and report non-compliant elements. Still automated code-compliance checking raises several obstacles. Building regulations are usually published as human readable texts and their content is often ambiguous or incomplete. Also, the vocabulary used for expressing such regulations is very different from the vocabularies used to express Building Information Models (BIM). Furthermore, the high level of details associated to BIM-contained geometries induces complex calculations. Finally, the level of complexity of the IFC standard also hinders the automation of IFC processing tasks. Model chart, formal rules and pre-processors approach allows translating construction regulations into semantic queries. We further demonstrate the usefulness of this approach through several use cases. We argue our approach is a step forward in bridging the gap between regulation texts and automated checking algorithms. Finally with the recent building ontology BOT recommended by the W3C Linked Building Data Community Group, we identify perspectives for standardizing and extending our approach.

The blockchain wave is gathering strength. More and more enterprises are embarking on concrete initiatives, either alone or in collaboration with their peers, their business partners, their clients, or their suppliers. Most of the use cases I've seen so far, apart from a couple of exceptions, are exploratory and mostly focus on using blockchain as a shared and trusted database of assets and transactions. The really interesting use cases that realize the exponential and disruptive benefit of the blockchain are yet to come. The most visionary players have revolutionary use cases in their roadmaps, but right now, enterprises are taking foundational steps necessary to pave the way for innovative use cases that might disrupt entire industries.

CHICAGO--(BUSINESS WIRE)--AHIMA booth 904--Change Healthcare (Nasdaq: CHNG) today introduced Charge Capture Advisor, a new cloud-based addition to the company's portfolio of Revenue Integrity Solutions. The solution uses Change Healthcare Artificial Intelligence to identify potentially missing charges for services that providers actually performed before claims are submitted. The result: more complete capture of services rendered without additional time and effort by hospital revenue integrity teams. Working alongside providers' existing health information system (HIS), coding, billing, and manual processes as part of a comprehensive charge-capture strategy, Charge Capture Advisor brings the power of AI to help increase detection of missing charges to drive complete claims, accelerate cash flow, and optimize revenue. "Providers are still falling short of their charge-capture potential, despite using the most sophisticated rules-based systems and meticulous manual audits," said Nick Giannasi, Ph.D., executive vice president and chief AI officer, Change Healthcare.

An employer in Spain may not be able to fire a worker caught on a surveillance camera doing something prohibited if the company hasn't informed workers about the video system and its purpose, according to a recent trial court decision. In a case involving an employee fired after a security camera captured him in a parking-lot fight after work hours, a Pamplona labor court ruled that the video evidence was inadmissible under the European Union's General Data Protection Regulation (GDPR) and case law from the European Court of Human Rights (ECHR). "The judgment is of great interest since it is the first ruling by a Spanish court on the validity that can be given to the evidence of video recordings after the publication of the new Spanish Data Protection Law and also an interpretation of the new European Data Protection Regulation," according to a blog post from Manuel Vargas of Barcelona's Marti & Associats law firm. Under Spain's own data-protection law, employers who record a worker doing something illegal are considered to have fulfilled their duty to inform so long as they have posted a sign identifying a video surveillance zone, Vargas wrote. He also noted that recent case law from the Spanish Supreme Court endorses the idea that employers aren't obligated to notify workers that they plan to use video cameras to monitor their activity for possible disciplinary purposes.

Providers continue to fall short of their charge-capture potential despite having rules-based systems and manual audits, an executive at an industry vendor contends. "It's estimated that missing charges and associated reimbursement--combined with audit and recovery efforts--cost providers the equivalent of 1 percent of annual revenue," says Nick Giannasi, executive vice president of Change Healthcare. Industry vendors are designing products to help provider organizations improve their ability to capture charges. For example, Change Healthcare is unveiling a product called Charge Capture Advisor that uses artificial intelligence to identify potentially missing charges for services that providers perform before claims are submitted. The company contends that the result is more complete capture of services rendered without imposing additional time and effort by hospital revenue integrity teams.

Intelligent bots use artificial intelligence to accomplish specific tasks by identifying user intent from text or voice conversations. They use a process called entity extraction, leveraging natural language understanding to extract key pieces of information. Most of the intelligent chatbots today are powered by natural language processing and machine learning. This enables them to get smarter and identify the key elements of a conversation to provide smart replies. Some of the most common places these chatbots are used are answering questions, playing music or making reservations.

Change Healthcare introduced Charge Capture Advisor, a new cloud-based addition to the company's portfolio of Revenue Integrity Solutions. The solution uses Change Healthcare Artificial Intelligence to identify potentially missing charges for services that providers actually performed before claims are submitted. The result: more complete capture of services rendered without additional time and effort by hospital revenue integrity teams. Working alongside providers' existing health information system (HIS), coding, billing, and manual processes as part of a comprehensive charge-capture strategy, Charge Capture Advisor brings the power of AI to help increase detection of missing charges to drive complete claims, accelerate cash flow, and optimize revenue. "Providers are still falling short of their charge-capture potential, despite using the most sophisticated rules-based systems and meticulous manual audits," said Nick Giannasi, Ph.D., executive vice president and chief AI officer, Change Healthcare.

One of the current problems in decision support from Artifici al Intelligence systems is the lack of explanations. When a system is making decisions in critical co ntexts and those decisions may have an impact on people's life like in the medical or legal domains, then explanations turn to be crucial, especially if we expect that a domain expert relies on the obtaine d answers. One of these situations from the medical domain where explanations have a crucial role is the process of donor-patient matching in an organ transplantation unit. This process starts when a new o rgan is received and consists in selecting a patient among those included in a waiting list for transplan tation. The transplantation unit is expected to follow an objective policy that takes into account medica l parameters and is experimentally supported by the existing records, but more importantly, this decisio n must be easily reproducible and explicable in a comprehensible way for other agents potentially involved, since it may have life-critical consequences at personal, medical and legal levels. Typically, this deci sion is taken in terms of a set of numerical weights (the impact of weights variation is studied in [7]). Although different classification systems based on Artificial Neural Networks (ANNs) are being propose d (see for instance [2] for the case of liver transplantation) their decisions rely on a black box whose b ehaviour is not explicable in human terms. In this paper, we present a rule interpreter, web-liver, designed for assisting the medical experts in the donor-patient matching of a liver transplantation un it, using the case scenario from the Digestive F. Aguado et al.