Systems and methods for predicting healthcare related risk events

Abstract

A system for predicting healthcare risk events including the process of accessing patient data associated with one or more patents, accessing geographic and healthcare system data, filtering the patient data, geographic data, and healthcare system data into clean data, and applying a predictive risk model to the clean data to generate patient profile data and to identify a portion of the patients susceptible to one or more risk events.

Description

REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit of and priority to: U.S. Provisional Application No. 60 / 628,476, filed on Nov. 16, 2004, entitled “Healthcare Surgical and Diagnostic Event Risk Predictive Modeling Analytic System Incorporating Unwarranted Geographic Treatment Pattern Variation Data”; U.S. Provisional Application No. 60 / 628,504, filed on Nov. 16, 2004, entitled “Healthcare Financial Risk Predictive Modeling Analytic System Incorporating Unwarranted Geographic Treatment Pattern Variation Data”; and U.S. Provisional Application No. 60 / 628,212, filed on Nov. 16, 2004, entitled “Healthcare Risk Predictive Modeling Analytic System Incorporating Unwarranted Geographic Treatment Pattern Variation Data.” This application is co-pending with Docket No. FMSF-P01-002, filed Nov. 16, 2005, and entitled “Systems and Methods for Predicting Healthcare Related Financial Risk.” The entire contents and teachings of the above referenced applications are incor...

AI Technical Summary

Benefits of technology

[0007] By predicting risk events, the invention enables interested parties to establish intervention plans to mitigate the occurrence of risk events for patients. Interested parties include, for example, healthcare providers, insurers, and payors. By mitigating the occurrence of risk events, the invention mitigates the costs or financial risks associated with such risk events. Risk events and financial risks may be collectively referred to as healthcare related risk outcomes.

[0009] The patient data may include information such as medical claims data, pharmacy claims data, referral post hospital discharge data, health risk assessment and functional status data, laboratories values, pre-notification or authorization data, and other risk factor data. The geographic / healthcare system factors may include practice pattern variation data, supply-sensitive factor data, healthcare system factor data, and other geographic and healthcare system factor data. In particular, the invention advantageously utilizes unwarranted healthcare / geographical treatment pattern variation data to more accurately predict which patients are more likely to incur certain risk events. An unwarranted healthcare / geographic treatment pattern variation is any variation in treatments across different geographic regions and healthcare systems that is not caused by patient preferences or characteristics.

[0011] According to one advantage, the invention enables interested parties to predict likely healthcare costs for an upcoming period. According to certain implementations, the invention enables the projection of healthcare costs over a period of at least about 3 months, 6 months, 9 months, 12 months, or greater than about 12 months.

[0012] According to another advantage, the invention enables interested parties to take action to limit healthcare costs by, for example, providing early intervention plans to patients and / or interested parties that prevent or mitigate the occurrence of risk events. The prevention of certain risk events may encourage interested parties such as healthcare insurers to pay for or cover the cost of such early intervention programs.

Problems solved by technology

Numerous countries throughout the world are facing an unprecedented rise in healthcare costs affecting both healthcare providers and employers.

One major component of healthcare costs is costs associated with surgery.

Another component of healthcare costs is costs associated with diagnostics.

However, prior attempts at predictive healthcare models have focused on resource utilization, rather than the likelihood that an individual will undergo a specific surgical or diagnostic procedure.

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