Statistical financial system and method to value patient visits to healthcare provider organizations for follow up prioritization

A hardware storage device operating within a computing architecture that includes at least one processor and the hardware storage device, wherein the hardware storage device includes code embodied therein, the code, when executed by the at least one processor causes the computing architecture to improve how healthcare services are managed and provisioned by facilitating generation and selective invocation of an optimized fast query that is generated using an attribute pattern derived from previously aggregated healthcare service data and that is used to provide high performance statistical analysis when evaluating new healthcare service data, the code comprising:

• an instruction for reading historical healthcare visit data associated with one or more healthcare provider visits, the healthcare provider data including at least one previously generated attribute pattern associated with one or more past healthcare provider visits, each healthcare provider visit comprising a combination of professional services and healthcare items that are to be provided to a patient at the healthcare provider visit, each professional service and healthcare item having an associated monetary value;
• an instruction for performing a statistical analysis on the historical healthcare visit data;
• an instruction for creation of attribute patterns using statistical variables, the attribute patterns comprising one or more visit-related variables associated with the professional services and healthcare items that are to be provided as part of the healthcare provider visit, wherein:
• the visit-related variables comprise a selected subset of independent and dependent variables used when calculating a value for the current healthcare provider visit,
• the independent variables are selected based on their predictive power according to the at least one previously generated attribute pattern,
• a higher predictive power is manifest where the difference between a predicted monetary value and a real monetary value has an optimal Sharpe ratio, a minimum standard deviation, and an average approaching zero,
• the attribute pattern is determined according to one or more rules executed within an analytical and rules engine that provides a configurable way to augment machine instructions from a compiled language, the analytical and rules engine being configured to take into account environment-specific variables that influence the attribute pattern determination,
• the machine instructions are executed in the analytical and rules engine along with the one or more rules;
• and
• a data representation of the attribute pattern is stored in the hardware storage device;
• an instruction for accepting healthcare data inputs including healthcare data inputs for at least one current healthcare visit;
• an instruction for generating a fast query that is optimized to specifically perform one or more attribute pattern queries on the determined attribute pattern, the optimized fast query being structured so that, when executed, it produces a result set that is used to subsequently identify a match between one or more previously generated attribute patterns and the determined attribute pattern, and wherein for the match to be identified based on the fast query's result set, a relationship between the one or more previously generated attribute patterns and the determined attribute pattern is required to satisfy a designated statistical quality level, wherein the result set is generated by executing the optimized fast query on the data representation of the attribute pattern such that the data representation stored in the hardware storage device is accessed during execution of the optimized fast query;
• an instruction to analyze said healthcare data inputs for data exceptions and issues;
• an instruction to raise alerts on said data exceptions;
• an instruction to use the result set to identify the match by comparing said healthcare data inputs including the at least one previously generated attribute pattern against said attribute patterns resulting in an assignment of a predicted monetary value, a weight, or a priority to said healthcare visits including the healthcare visit, or to the alerts, or to both, the weight or priority indicating the order in which follow-up tasks performed after the healthcare provider visit are to be carried out for the healthcare provider visit.

A computer system comprising the following:

• one or more processors;
• and
• one or more computer-readable storage media having stored thereon computer-executable instructions that are executable by the one or more processors to cause the computer system to improve how healthcare services are managed and provisioned by facilitating generation and selective invocation of an optimized fast query that is generated using an attribute pattern derived from previously aggregated healthcare service data and that is used to provide high performance statistical analysis when evaluating new healthcare service data by causing the computer system to:
• instantiate an interface layer that provides an interface for a user to interact with a remote client, including accessing, via the remote client, one or more portions of healthcare provider data associated with a healthcare provider visit, the healthcare provider data including at least one previously generated attribute pattern associated with one or more past healthcare provider visits, the healthcare provider visit comprising a combination of professional services and healthcare items that are to be provided to a patient at the healthcare provider visit, each professional service and healthcare item having an associated monetary value;
• determine an attribute pattern that includes one or more visit-related variables associated with the professional services and healthcare items that are to be provided as part of the healthcare provider visit, wherein:
• the visit-related variables comprise a selected subset of independent and dependent variables used when calculating a value for the current healthcare provider visit based on one or more historical healthcare provider visits,
• the independent variables are selected based on their predictive power according to the at least one previously generated attribute pattern,
• a higher predictive power is manifest where a difference between a predicted monetary value and a real monetary value has an optimal Sharpe ratio, a minimum standard deviation, and an average approaching zero,
• the attribute pattern is determined according to one or more rules executed within an analytical and rules engine that provides a configurable way to augment machine instructions from a compiled language, the analytical and rules engine being configured to take into account environment-specific variables that influence the attribute pattern determination,
• the machine instructions are executed in the analytical and rules engine along with the one or more rules;
• and
• a data representation of the attribute pattern is stored in the computer system's one or more computer-readable storage media;
• after the attribute pattern is determined, generate a fast query that is optimized to specifically perform one or more attribute pattern queries on the determined attribute pattern, the optimized fast query being structured so that, when executed, it produces a result set that is used to subsequently identify a match between one or more previously generated attribute patterns and the determined attribute pattern, and wherein for the match to be identified based on the fast query's result set, a relationship between the one or more previously generated attribute patterns and the determined attribute pattern is required to satisfy a designated statistical quality level;
• after generating the result set by executing the optimized fast query on the data representation of the attribute pattern such that the data representation stored in the computer system's one or more computer-readable storage media is accessed during execution of the optimized fast query, use the result set to identify the match by comparing the healthcare provider data associated with the healthcare provider visit including the at least one previously generated attribute pattern to the determined attribute pattern to match the at least one previously generated attribute pattern with the newly determined attribute pattern in order to assign a monetary value, a weight, or a priority to the healthcare provider visit;
• and
• based on the comparison of the healthcare provider data to the determined attribute pattern, and prior to the healthcare provider visit taking place, calculate a predicted monetary value, a weight or a priority for the healthcare provider visit, the predicted monetary value, weight or priority indicating the order in which follow-up tasks performed after the healthcare provider visit are to be carried out for the healthcare provider visit.