Abstract: A method and apparatus for predicting a health benefit for an individual is provided. Outcomes from a first simulation on a set of simulated individuals reflecting a population are stored and used to determine a first risk function and corresponding cost values. Outcomes from a second simulation on a set of simulated individuals reflecting having a healthcare intervention are stored and used to determine a second risk function reflecting the intervention and corresponding cost values of the intervention. A benefit function is derived from the difference of the first and second risk functions. A cost function that describes the cost of the intervention is derived from the respective cost values. The derived benefit function and cost function are used to predict the corresponding benefit and cost of the healthcare intervention for a given individual. Individuals can be ranked by degree of expected benefit.

Abstract: A method comprises receiving a prediction request that comprises a target patient population definition; in response to receiving the prediction request, performing in real-time: parsing the prediction request to identify the target patient population definition; mapping the one or more target patient population characteristics to a function of one or more input variables of a particular dataset, from a plurality of datasets; computing a weighted subset of patients; based, at least in part, on the target patient population definition and the particular dataset; computing the prediction data based on the weighted subset of patients; returning the prediction data.

Abstract: A method comprises receiving a prediction request that comprises a population definition and one or more healthcare treatment criteria specifying a treatment scenario; in response to receiving the prediction request, performing in a real-time: parsing the prediction request to identify the population definition and the one or more healthcare treatment criteria; mapping the one or more healthcare treatment criteria to a function of one or more input variables to determine a particular dataset, from a plurality of datasets; based, at least in part, on the population definition and the particular dataset, determining a response surface; determining prediction data by estimating, using the response surface which approximates the healthcare simulation model, simulation results that using the healthcare simulation model would yield; returning the prediction data.

Abstract: An apparatus for creating a graphical representation of personalized healthcare information is provided. A data processing apparatus that has one or more processors is disclosed. Query component logic receives a request comprising patient identification information. Query execution logic, based on the patient information determines a first metric and one or more intervention metrics. The first metric represents a current health related metric for the patient, while the intervention metrics represent a predicted health related metric, assuming that the patient conforms to the intervention. A graphical representation of the two metrics is created.

Abstract: A method of determining a quality of care provided by a healthcare provider to individuals in a population is provided. A data processing apparatus that has one or more processors is disclosed. Data representing biomarkers for individuals in a population is received. Baseline and present risks are determined. Risk reduction values are determined. Based on the current risk reduction, a quality score is determined. A scale is created, and the quality score is mapped to the scale. The global quality score of the disclosure provides numerous benefits over past performance measures.

Abstract: A method and apparatus for predicting a health benefit for an individual is provided. Outcomes from a first simulation on a set of simulated individuals reflecting a population are stored and used to determine a first risk function and corresponding cost values. Outcomes from a second simulation on a set of simulated individuals reflecting having a healthcare intervention are stored and used to determine a second risk function reflecting the intervention and corresponding cost values of the intervention. A benefit function is derived from the difference of the first and second risk functions. A cost function that describes the cost of the intervention is derived from the respective cost values. The derived benefit function and cost function are used to predict the corresponding benefit and cost of the healthcare intervention for a given individual. Individuals can be ranked by degree of expected benefit.

Abstract: A method for simulating a clinical trial includes: selecting a trial procedure for a simulated trial corresponding to the clinical trial; generating a population of subjects for the simulated trial; searching the population of subjects to determine acceptable subjects for the simulated trial; selecting subjects for the simulated trial from the acceptable subjects; simulating the trial procedure for the selected subjects; and collecting trial data for the simulated trial from the simulated trial procedure.

Abstract: A method for generating a continuous mathematical model of a feature common to subjects in a subject group includes selecting a sample data set from each subject in the subject group, selecting a set of expansion functions to be used in the representation of the sample data set, mathematically expanding each member of the sample data set in the form of a summation of results of multiplying each the expansion function in the set of expansion functions by a different mathematical parameter wherein the expanding determines a value for each of the different mathematical parameters, deriving a corresponding distribution function for each of the mathematical parameters, and generating the continuous mathematical model of the feature from the derived distribution functions and the expansion functions. In this way, the model is continuous in time, incorporates dependencies between various parameters, and allows for creation of simulated subjects having pertinent features occurring in real subjects.