Abstract: Systems and methods for automated interventions to persons identified as being of risk of falling are provided. A subset of members is identified which are associated with at least one of a plurality of falls predictors. At least one falls prediction algorithm is applied to a subset of said medical claims data associated with the subset of members to generate a falls risk score for each of member of the subset. At least one intervention is assigned to each of member of the subset having an assigned risk score above any of several predetermined risk score thresholds which are automatically and electronically initiated based, at least in part, on member data.

Abstract: The disclosed computerized system and method facilitates predicting the onset of diabetes or symptom progression in those patients already suffering from the disease. The computerized system and method applies steps to segment the population by predefined member characteristics. Once segmented, the computerized system and method applies a plurality of prediction models to the segmented population data to provide a ranking of members of the population that indicates the likelihood of onset or progression of diabetes for each member.

Abstract: A computerized system and method for automatically estimating the likelihood of having a fall leading to a fracture/dislocation within a specified period is described, and comprises a predictive model for guiding patients to the right course of treatment and encouraging discussions with their doctors for better outcomes. The system and method extracts member's health information from health administrative claims data, including clinical and pharmacy data, and estimates the probability of a fall for that member. Patients with high risk scores are selected for various clinical programs and interventions to manage their health conditions and reduce their likelihood of falling.

Abstract: A computerized system and method for automatically estimating the likelihood of having a fall leading to a fracture/dislocation within a specified period is described, and comprises a predictive model for guiding patients to the right course of treatment and encouraging discussions with their doctors for better outcomes. The system and method extracts member's health information from health administrative claims data, including clinical and pharmacy data, and estimates the probability of a fall for that member. Patients with high risk scores are selected for various clinical programs and interventions to manage their health conditions and reduce their likelihood of falling.

Abstract: The disclosed computerized system and method facilitates predicting the onset of diabetes or symptom progression in those patients already suffering from the disease. The computerized system and method applies steps to segment the population by predefined member characteristics. Once segmented, the computerized system and method applies a plurality of prediction models to the segmented population data to provide a ranking of members of the population that indicates the likelihood of onset or progression of diabetes for each member.

Abstract: A computerized back surgery predictive model identifies a risk population for back surgery and assigns a severity level to members of the risk population. High risk members are informed of preference-sensitive surgeries and alternative treatment options. The model focuses on members of the population with back condition related claims and is trained using data for members with primary diagnoses associated with various types of visits, procedures, and treatments for back pain. In an example embodiment, the model is applied to member populations to predict a first back surgery (e.g., spinal fusion, kyphosplasty, vertebroplasty, or decompression surgery) within one year after identified triggers. Predictors are historical risk factors from a broad set of data sources. Members are scored monthly to allow for continuous monitoring of the changing risk of back surgery and to allow timely intervention. The model may be tailored for different populations such as commercial and Medicare populations.

Abstract: The present invention is a method of predicting the likelihood that chronic kidney disease will result in end stage renal disease requiring dialysis. The method uses various indicators comprising information specific to an individual as well as information representing characteristics of a population including demographic information, health care and prescription insurance claims, and involvement in various programs designed to improve the health of a user. The method applies a predictive algorithm to these indicators in order to derive a risk score indicating an individual's risk of dialysis.

Abstract: A computerized system and method comprises a predictive model for estimating the probability of a patient hospital readmission. The computerized system and method is useful for identifying patients at risk of hospital readmission and further identifying an intervention to mitigate the risk and reduce the likelihood that the patient returns to the hospital. The computerized system method collects and analyzes historical health data from administrative claims data and current health data collected in real-time from medical records at the point of treatment. Signals indicating readmission are extracted from the health data that is collected. The signals are evaluated by the model software application to estimate a probability that a patient will be readmitted to the hospital. Patients with a high readmissions probability score are selected for clinical programs and interventions that help them manage their health conditions and problems and reduce the likelihood that they return to the hospital.

Abstract: A computerized system and method for automatically estimating the likelihood of having a fall leading to a fracture/dislocation within a specified period is described, and comprises a predictive model for guiding patients to the right course of treatment and encouraging discussions with their doctors for better outcomes. The system and method extracts member's health information from health administrative claims data, including clinical and pharmacy data, and estimates the probability of a fall for that member. Patients with high risk scores are selected for various clinical programs and interventions to manage their health conditions and reduce their likelihood of falling.

Abstract: A method for determining a suitable configuration for an elevator system for a building that includes acquiring building related information and passenger use information. Elevator system performance requirements based on elevator system passenger numbers are selected based on this information followed by selecting a set of elevator system characteristic variables that are desired to be at optimal values which are processed along with the information and performance requirements to provide an optimal solution.

Abstract: A method of determining a solution to a problem is provided. The method comprises defining a problem graphically using an entity-relationship modeling notation comprising at least one entity, at least one attribute, and at least one relationship, wherein the at least one entity and the at least on relationship are capable of being designated as a decision element. The method also comprises mapping the graphical modeling notation to algebraic form, and passing the algebraic form to a mathematical solver adapted to solve the algebraic form of the problem.

Abstract: Integrated modeling and symbolic manipulation is leveraged for the derivation, construction, maintenance, and reuse of application-independent models. Related models created for different applications share a common ancestry and maintain model consistency while enabling the models to share information about the process. Environment independent proper ancestor models (PAMs) are created for generic components of a process, such as a distillation or flash column. Models underlying an application may be comprised of various representations of multiple such processes corresponding to many different physical components. PAMs contain symbolic representations of different sub processes that occur within a process or component. A user makes assumptions about the component, by indicating how sub processes are to be considered or not considered. PAMs are then modified in accordance with the assumptions to derive a specific environment model (SEM).

Abstract: An novel recipe that embodies a higher level description than currently used in the industry is described. The novel recipe is referred to herein as an “advanced recipe.” A traditional recipe consists of a general set of instructions called a ‘recipe’ which is used to make a product. An advanced recipe integrates the intent behind these set of instructions. A novel knowledge management system for capturing, integrating, and using broad knowledge is also described below. Some embodiments of the knowledge management system enable modification of recipes for maximizing the overall processing objectives. An example embodiment of the knowledge management system comprises a Knowledge Builder, a Structured Knowledge Repository, and a Decision Maker.

Abstract: Integrated modeling and symbolic manipulation is leveraged for the derivation, construction, maintenance, and reuse of application-independent models. Related models created for different applications share a common ancestry and maintain model consistency while enabling the models to share information about the process. Environment independent proper ancestor models (PAMs) are created for generic components of a process, such as a distillation or flash column. Models underlying an application may be comprised of various representations of multiple such processes corresponding to many different physical components. PAMs contain symbolic representations of different sub processes that occur within a process or component. A user makes assumptions about the component, by indicating how sub processes are to be considered or not considered. PAMs are then modified in accordance with the assumptions to derive a specific environment model (SEM).

Abstract: In order to determine a lowest utility cost relative to a plurality of utility rate structures and a Contract Base Load, a plurality of utility costs are computed such that each of the utility costs corresponds to a different combination of one of rate structures and the Contract Base Load. These computations are based on an objective function. A rate structure and Contract Base Load combination corresponding to the lowest utility cost is presented to a utility customer who may then negotiate a utility contract based on the present information. If desired, the computations may also be based on various on-site generation options.