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.

Some days ago I was interviewing a candidate for a data-related position: after a couple of technical questions I asked him what algorithm he would have used to have a reliable starting point for a random classification problem. I was just curious to understand how used he was in doing some data science and if he knew some state-of-the-art algorithms and techniques. He told me that he would have gone with a simple decision tree because it's somehow easy to explain and interpret. That answer surprised me a little: I mean, why a decision tree in 2019 when you can get way better and, above all, more robust results using more advanced algorithms? As always happens, once you notice something you see it everywhere, and from that day I keep seeing and reading here and there blog posts about interpretability, explicability and how all of these concepts are connected to machine learning and trust.

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise, and is further challenged by today's often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts, have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems.

In this paper, we introduce an extension of our presented cognitive-based emotion model [27][28]and [30], where we enhance our knowledge-based emotion unit of the architecture by embedding a fuzzy rule-based system to it. The model utilizes the cognitive parameters dependency and their corresponding weights to regulate the robot's behavior and fuse their behavior data to achieve the final decision in their interaction with the environment. Using this fuzzy system, our previous model can simulate linguistic parameters for better controlling and generating understandable and flexible behaviors in the robots. We implement our model on an assistive healthcare robot, named Robot Nurse Assistant (RNA) and test it with human subjects. Our model records all the emotion states and essential information based on its predefined rules and learning system. Our results show that our robot interacts with patients in a reasonable, faithful way in special conditions which are defined by rules. This work has the potential to provide better on-demand service for clinical experts to monitor the patients' emotion states and help them make better decisions accordingly.

This paper formally compares some central notions from two well-known formalisms for rule-based argumentation, DeLP and ASPIC+. The comparisons especially focus on intuitive adequacy and inter-translatability, consistency, and closure properties. As for differences in the definitions of arguments and attack, it turns out that DeLP's definitions are intuitively appealing but that they may not fully comply with Caminada and Amgoud's rationality postulates of strict closure and indirect consistency. For some special cases, the DeLP definitions are shown to fare better than ASPIC+. Next, it is argued that there are reasons to consider a variant of DeLP with grounded semantics, since in some examples its current notion of warrant arguably has counterintuitive consequences and may lead to sets of warranted arguments that are not admissible. Finally, under some minimality and consistency assumptions on ASPIC+ arguments, a one-to-many correspondence between ASPIC+ arguments and DeLP arguments is identified in such a way that if the DeLP warranting procedure is changed to grounded semantics, then DeLP notion of warrant and ASPIC+'s notion of justification are equivalent. This result is proven for three alternative definitions of attack.