Abstract: Various health management systems may benefit from tools for appropriate identification of target patients. For example, various population health management approaches may benefit from systems and methods for optimal health assessment and optimal preventative program development. A method can include obtaining a simple risk measure score for a patient. The method can also include obtaining an evidence-based score for the patient. The method can further include comparing the simple risk measure score to the evidence-based score. The method can additionally include identifying the patient as a target for preventative medicine based on a discrepancy revealed by the comparison.

Abstract: A method of obtaining a virtual provider cluster is provided. The method includes obtaining a medical effectiveness per member, obtaining a preliminary virtual provider cluster, for each preliminary virtual provider cluster, calculating a MedicalEffectiveness as: MedicalEffectiveness=mean of the MedicalEffectiveness per member for all members associated with the preliminary virtual cluster; for each preliminary virtual provider cluster, calculating a RiskAdjustedCostEfficiency as RiskAdjustedCostEfficiency=sum of all MemberExpectedClaimAmount/sum of all MemberAllowedClaimAmount; and obtaining a virtual provider cluster by updating each preliminary virtual provider cluster with its corresponding MedicalEffectiveness and corresponding RiskAdjustedCostEfficiency.

Abstract: An enhanced risk management method is provided in which a diverse set of inputs, such as demographic variables, risk adjustment factors (RAF) of previous years, and claims of previous years, are used in the training of a prediction model configured to predict both a standard RAF based on the assumption that a healthcare system in question continues its current, possibly suboptimal, operations, and an improved RAF based on an idealized workflow in which all of a member's Hierarchical Condition Category (HCC) codes are captured appropriately at the earliest time possible.

Abstract: Various areas of medicine and healthcare may benefit from improvements in the identification of and mitigation of health risks. For example, medicine and healthcare may benefit from systems and methods that can mine literature to select and analyze the risk factor(s) contributing to the development, progression and management of common health conditions. A method can include receiving an input health condition. The method can also include scoring the health condition based on a plurality of risk factor sources to generate a health score. The method can further include providing the health score and at least one remediation goal based on the risk factor sources.

Abstract: Various health management systems may benefit from tools for appropriate identification of target patients. For example, various population health management approaches may benefit from systems and methods for optimal health assessment and optimal preventative program development. A method can include obtaining a simple risk measure score for a patient. The method can also include obtaining an evidence-based score for the patient. The method can further include comparing the simple risk measure score to the evidence-based score. The method can additionally include identifying the patient as a target for preventative medicine based on a discrepancy revealed by the comparison.

Abstract: Certain embodiments of the present invention relate generally to using machine learning or other automated techniques to among other things, identify, estimate, and/or predict patient health conditions. Furthermore, certain embodiments of the present invention are related to health interventions performed to reduce the risk of developing diseases and health conditions. This risk reduction improves the overall health of the individual and/or the population and helps reduce healthcare costs.

Abstract: Various areas of medicine and healthcare may benefit from improvements in the identification of and mitigation of health risks. For example, medicine and healthcare may benefit from systems and methods that can mine literature to select and analyze the risk factor(s) contributing to the development, progression and management of common health conditions. A method can include receiving an input health condition. The method can also include scoring the health condition based on a plurality of risk factor sources to generate a health score. The method can further include providing the health score and at least one remediation goal based on the risk factor sources.

Abstract: Risk engines and related methods may benefit patients and their physicians. For example, patients may benefit from being able to determine their personal genetic, environmental, and behavioral risks. Moreover, physicians may be able to provide statistically-driven individual recommendations based on a risk engine's determination of such risks. A method can include selecting one or more candidate genetic variants associated with a phenotype from the scientific literature. The method can also include scoring a genetic association between the one or more candidate genetic variants and the phenotype. The method can further include selecting one or more high-scoring genetic variants. Selecting a best genetic variant within each of at least one linkage disequilibrium (LD) block can also be included in the method. The method can additionally include calculating risk associated with the best genetic variant from the at least one LD block.

Abstract: In one aspect, the invention provides a method of enriching a pool of nucleic acid duplexes for heteroduplexes containing sequences that vary from those of a reference DNA population. A mixture of test sequences, that is, nucleic acids suspected of containing one or more mutations or rare alleles, and corresponding reference sequences are denatured and reassociated so that duplexes form comprising heteroduplexes and homoduplexes. In accordance with one aspect of the invention, whenever a subset of heteroduplexes contains mismatched sequences, the mismatches are corrected so that the sequences of strands from the test population are preserved, or selected. The preserved or selected sequences may then be denatured and reassociated with additional sequences from the reference population to once again form duplexes comprising homoduplexes and heteroduplexes, thereby generating an enriched population of variant sequences.