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.
Analysis Big data Healthcare HIPAA Management Visualization
This is a preview of subscription content, log in to check access.
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.
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.
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.
Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014)CrossRefGoogle Scholar
Alyass, A., Turcotte, M., Meyre, D.: From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med. Gen. 8, 33 (2015)CrossRefGoogle Scholar
McShane, L.M., et al.: Criteria for the use of omics based predictors in clinical trials: explanation and elaboration predictors in clinical trials: explanation and elaboration. BMC Med. 11(1), 220 (2013)CrossRefGoogle Scholar
Kim, M.O., Coiera, E., Magrabi, F.: Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. J. Am. Med. Inform. Assoc. 24, 246–260 (2017)Google Scholar
Sligo, J., Gauld, R., Roberts, V., Villa, L.: A literature review for large scale health information system project planning, implementation and evaluation. Int. J. Med. Inf. 97, 86–97 (2017)CrossRefGoogle Scholar
Lu, Z., Su, J.: Clinical data management: current status, challenges, and future directions from industry perspectives. Open Access J. Clin. Trials 2, 93–105 (2010)CrossRefGoogle Scholar
Haux, R., Knaup, P., Leiner, F.: Fdata management the other side of the electronic health record. Methods Inf. Med. 46, 74–79 (2007)CrossRefGoogle Scholar
Rumsfeld, J.S., Joynt, K.E., Maddox, T.M.: Big data analytics to improve cardiovascular care: promise and challenges. Nat. Rev. Cardiol. 13, 350–359 (2016)CrossRefGoogle Scholar
van Panhuis, W.G., et al.: A systematic review of barriers to data sharing in public health. BMC Public Health 14, 1144 (2014)CrossRefGoogle Scholar
Fegan, G.W., Lang, T.A.: Could an open-source clinical trial data-management system be what we have all been looking for? PLoS Med. 5, e6 (2008)CrossRefGoogle Scholar
Walker, J.G., et al.: The CRISP colorectal cancer risk prediction tool: an exploratory study using simulated consultations in Australian primary care. BMC Med. Inform. Decis. Mak. 17, 13 (2017)CrossRefGoogle Scholar
Liu, L., Liu, L., Fu, X., Huang, Q., Zhang, X., Zhang, Y.: A cloud-based framework for large-scale traditional Chinese medical record retrieval. J. Biomed. Inform. 77, 21–33 (2017)CrossRefGoogle Scholar
Krishnankutty, B., Bellary, S., Kumar, N.B., Moodahadu, L.S.: Data management in clinical research: an over-view. Indian J. Pharmacol. 44, 168–172 (2012)CrossRefGoogle Scholar
Turner, S., Foong, S.: Navigating the road to implementation of the health insurance portability and accountability act. Am. J. Public Health 93, 1806–1808 (2003)CrossRefGoogle Scholar
Miller, J.D.: Sharing clinical research data in the United States under the health insurance portability and accountability act and the privacy rule. Trials 11, 112 (2010)CrossRefGoogle Scholar
Goldstein, M.M.: Health information privacy and health information technology in the US correctional setting. Am. J. Public Health 104, 803–809 (2014)CrossRefGoogle Scholar
Bradford, W., Hurdle, J.F., LaSalle, B., Facelli, J.C.: Development of a HIPAA-compliant environment for translational research data and analytics. J. Am. Med. Inform. Assoc. 21, 185–189 (2014)CrossRefGoogle Scholar
Ahmed, Z., Zeeshan, S., Dandekar, T.: Developing sustainable software solutions for bioinformatics by the “Butterfly” paradigm. F1000Res. 7, 54–66 (2014)Google Scholar
Ahmed, Z., Zeeshan, S.: Cultivating Software Solutions Development in the Scientific Academia. Recent Pat. Comput. Sci. 7, 54–66 (2011)CrossRefGoogle Scholar
Ahmed, Z.: Designing flexible GUI to increase the acceptance rate of product data management systems in industry. Int. J. Comput. Sci. Emerg. Technol. 2, 100–109 (2011)Google Scholar