Abstract: Snapshot data may be received indicative of a current state of resources that deliver healthcare to a plurality of patients associated with a healthcare enterprise. The received snapshot data may be automatically used to initialize a healthcare enterprise simulation model. The healthcare enterprise simulation model may then be executed to automatically generate a predicted future state of the resources at a predetermined point in time. Some embodiments may automatically suggest mitigation strategies based on simulated scenarios reflecting potential bottlenecks and appropriate actions that may be taken by the enterprise. The system may continuously and automatically monitor forecast accuracy to detect potential anomalies.

Abstract: The present disclosure relates approaches that may be used to analyze data from hospital records to identify deficiencies in the operation of the hospital. In certain implementations, features of the data may be evaluated in conjunction with performance indicators to identify root causes associated with the deficiencies. In further implementations, identification of root causes of deficiencies identified in the historical data may be used to generate recommendations for changes to the operation of the hospital. In further implementations, events may be predicted based on the identification of a features or features within the current data that is indicative of a pending problem or event.

Abstract: A system for representing information and decision flow requirements in a transactional business process is provided. The system comprises an information gathering object and a decision object. The information gathering object represents a piece of information used in the transactional business system and the decision object represents a satisfying condition for achieving an objective associated with the transactional business system. The system further comprises one or more information channels that link the information gathering objects to the one or more decision objects. The information channels represent the information and decision flow requirements within the transactional business system.

Abstract: A robust process for automating the tuning and maintenance of decision-making systems is described. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.

Abstract: A robust system for automating the tuning and maintenance of decision-making systems is described. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.

Abstract: A process for at least a partial underwriting of insurance policies is described. Based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made. This decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.

Abstract: A system for at least a partial underwriting of insurance policies is described. based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made. this decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.

Abstract: A robust system for automating the tuning and maintenance of decision-making systems is described. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.

Abstract: A process for at least a partial underwriting of insurance policies is described. Based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made. This decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.

Abstract: A robust process for automating the tuning and maintenance of decision-making systems is described. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.

Abstract: A system for at least a partial underwriting of insurance policies is described. based on the similarity to previous insurance applications, a decision on the current request for underwriting may be made this decision-making process represents an analogical approach to the placement of an insurance application to an underwriting category, whereby a given insurance application request is compared to previous requests.

Abstract: A process to structure and summarize the key information required by automated decision-making systems for insurance underwriting is described. The automated underwriting system may be based on rules corresponding to underwriting components, wherein based on the degree of satisfaction of each rule, a component may be assigned to a category, and based on the category for each component, the insurance application may be assigned an underwriting category, or the automated underwriting system may be based on an evaluation of the similarity of a given application to previous application requests, to decide an underwriting category. Most of the key information required for automated insurance underwriting is structured and standardized, except for the Attending Physician Statement (APS), which is almost as unique as each individual physician.

Abstract: A system to structure and summarize the key information required by automated decision-making systems for insurance underwriting is described. The automated underwriting system may be based on rules corresponding to underwriting components, wherein based on the degree of satisfaction of each rule, a component may be assigned to a category, and based on the category for each component, the insurance application may be assigned an underwriting category, or the automated underwriting system may be based on an evaluation of the similarity of a given application to previous application requests, to decide an underwriting category. Most of the key information required for automated insurance underwriting is structured and standardized, except for the Attending Physician Statement (APS), which is almost as unique as each individual physician.