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Systematically Dealing Practical Issues Associated to Healthcare Data Analytics

  • Zeeshan AhmedEmail author
  • Bruce T. Liang
Conference paper
First Online:
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

Abstract

Healthcare data includes information about patients’ life style, medical history, claims data, metabolomics and genomics profiles. Adequate and analytic access to healthcare data has potential to revolutionize the field of medicine by detecting diseases at earlier stages and modelling complex biological interactions by integrating and analyzing knowledge in a holistic manner. To improve the quality and transition of healthcare at reduced cost, innovative platforms are necessary to analyze heterogeneous clinical data of huge volume, velocity, variety and veracity. Healthcare data analytic process implementation is not straightforward and to effectively implement it, various big data challenges have to be overcome, which requires significant efforts from the experts in various disciplines. In response to these challenges and promoting significant medical transformation in public health, we showcase a new HIPAA compliant and high performance computing based large scaled big data system: Management, Analysis and Visualization of Clinical Data (MAV-clic), developed at UConn Health. MAV-clic successfully implements healthcare data analytic process and major analytic functionalities dealing with healthcare data consisting of three different modules: Cohort building, Data analysis, and Customized Measurement analysis. MAV-clic contains medical history of over 800,000 patients, and information about over all providers, medications and diagnosis codes. To fulfill the growing interests in implementing the health information system, MAV-clic fulfills the requirements of data owners as well as data users in the healthcare system. To give better understanding of the MAV-clic, we highlight background, and discuss its system designs, methods, development details and analysis capabilities in this manuscript.

Keywords

Analysis Big data Healthcare HIPAA Management Visualization 
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Notes

Acknowledgment

We are grateful to the Ahmed lab and all its collaborators, who have contributed in anyways to the progress of the MAV-clic. We thank Department of Genetics and Genome Sciences; Institute for Systems Genomics; The Pat and Jim Calhoun Cardiology Center; Cardiovascular Biology and Medicine; School of Medicine, University of Connecticut Health Center (UConn Health), and CMS (SNE-PTN project) for their support. We appreciate all colleagues, who have provided insight and expertise that greatly assisted the research and development.

Contributorship Statement

ZA perceived the idea and did all work on the software and infrastructure design and implementation and related aspects of MAV-clic. BL guided study and both authors participated in writing and review.

Funding Statement

This work was supported by Ahmed lab, Genetics and Genome Sciences, School of Medicine, University of Connecticut Health Center, fund number 208025.

Competing Interests Statement

Authors have no competing interests to declare.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Genetics and Genome Science, School of MedicineUniversity of Connecticut Health Center (UConn Health)FarmingtonUSA
  2. 2.Institute for Systems Genomics, School of MedicineUniversity of Connecticut Health Center (UConn Health)FarmingtonUSA
  3. 3.The Pat and Jim Calhoun Cardiology Center, School of MedicineUniversity of Connecticut Health Center (UConn Health)FarmingtonUSA

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