Abstract
Recently, big data is becoming the key to improve the future healthcare. The era of big biomedical data comes with significant challenges in querying, storage, visualize, and analyze the available petabytes of biomedical data, which makes healthcare industry a data-driven field. Currently, the available Concurrent Risk Model (CRM) is limited to the availability of patient episodes that are sensitivity to its cost. Herein, we propose a novel hierarchical data mining based on functional networks to develop a new CRM. This new risk score evaluates the last twelve-month period of patients’ expected risk/cost/severity/illness burden/disease intervention using both medical and drugs claim-based predictors: diagnoses, medications (yes/no), and demographics. Our novel CRM predicts $50,000 permember- per month (PMPM) tracks risk trends over time for any particular group, especially severe chronic diseases. Our CRM model has R2=0.57 in comparison with the best results of Society of Actuaries.
Highlights
The U.S healthcare spending is 15.3% of its GDP and is projected to grow on an average of 6.7% annually over the period 2007-2017, reaching 19.5% of the U.S GDP by 2017 [1,2,3]
We can only get the benefit of this available big biomedical data when we have adequate computational analytics tools to build future decision systems
We develop the Concurrent Risk Model (CRM) using medical and Rx claim-based big data: diagnoses, drugs and demographic variables
Summary
The U.S healthcare spending is 15.3% of its GDP and is projected to grow on an average of 6.7% annually over the period 2007-2017, reaching 19.5% of the U.S GDP by 2017 [1,2,3]. The current healthcare and biomedical industry have greatly benefitted from the recent development in information technology and medical devices, since it has to process massive quantities of biomedical data on a daily basis. The digital electronic clinical/ drug data are increasing exponentially and it is expected increase to reach to 25 Exabyte by 2020 [6,7]. Due to the recent development in biomedicine and healthcare industry, we are currently facing great challenges in dealing with the generated or gathered massive biomedical data about patients through EMR systems, which have emerging demand to deal with it towards the essential need of biomedicine at bedside. We can only get the benefit of this available big biomedical data when we have adequate computational analytics tools to build future decision systems
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