Addressing the Complexities of Big Data Analytics in Healthcare: The Diabetes Screening Case

The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context.

[1]  M. Lynne Markus,et al.  Participation in Development and Implementation - Updating An Old, Tired Concept for Today's IS Contexts , 2004, J. Assoc. Inf. Syst..

[2]  A. Hanauer,et al.  Opportunities and Challenges in Association and Episode Discovery from Electronic Health Records , 2022 .

[3]  Christopher G. Chute,et al.  The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data , 2010, J. Am. Medical Informatics Assoc..

[4]  Stavri G. Nikolov,et al.  Image fusion: Advances in the state of the art , 2007, Inf. Fusion.

[5]  Gareth Herschel,et al.  Gartner ' s Business Analytics Framework , 2012 .

[6]  Carolyn McGregor,et al.  Temporal abstraction in intelligent clinical data analysis: A survey , 2007, Artif. Intell. Medicine.

[7]  S. F. Roberts,et al.  Perspective: Transforming Science Into Medicine How Clinician–Scientists Can Build Bridges Across Research's “Valley of Death” , 2012, Academic medicine : journal of the Association of American Medical Colleges.

[8]  V. Torra.  On some aggregation operators for numerical information , 2003 .

[9]  Herbert F. Jelinek,et al.  An innovative Multi-disciplinary Diabetes Complications Screening Program in a Rural Community: A Description and Preliminary Results of the Screening , 2006 .

[10]  Andrew Stranieri,et al.  A participatory information management framework for patient centred care of autism spectrum disorder , 2013 .

[11]  D. Butler.  Translational research: Crossing the valley of death , 2008, Nature.

[12]  Jun Gao,et al.  DW4TR: A Data Warehouse for Translational Research , 2011, J. Biomed. Informatics.

[13]  Steven Miller,et al.  Big Data Analytics , 2013 .

[14]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[15]  Julie Fisher,et al.  Improving service of online health information provision: A case of usage-driven design for health information portals , 2014, Information Systems Frontiers.

[16]  Michael C. Schatz,et al.  CloudBurst: highly sensitive read mapping with MapReduce , 2009, Bioinform..

[17]  Marie-Christine Chambrin,et al.  A New Approach to the Abstraction of Monitoring Data in Intensive Care , 2005, AIME.

[18]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[19]  Riccardo Bellazzi,et al.  Intelligent analysis of clinical time series: an application in the diabetes mellitus domain , 2000, Artif. Intell. Medicine.

[20]  Robert A. Weinstein,et al.  Application of Information Technology: Development of a Clinical Data Warehouse for Hospital Infection Control , 2003, J. Am. Medical Informatics Assoc..

[21]  Elpida T. Keravnou.  Temporal Abstraction of Medical Data: Deriving Periodicity , 1997 .

[22]  Shein-Chung Chow,et al.  On Big-Data Analytics in Biomedical Research , 2015 .

[23]  Adam Wright,et al.  A four-phase model of the evolution of clinical decision support architectures , 2008, Int. J. Medical Informatics.

[24]  Yuval Shahar,et al.  A knowledge-based method for temporal abstraction of clinical data , 1995 .

[25]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[26]  Anna Ståhlbröst,et al.  Participation in Living Lab: Designing Systems with Users , 2010, Human Benefit through the Diffusion of Information Systems Design Science Research.

[27]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[28]  Susan C. Weber,et al.  STRIDE - An Integrated Standards-Based Translational Research Informatics Platform , 2009, AMIA.

[29]  Michael H. Coen,et al.  Cross-Modal Clustering , 2005, AAAI.

[30]  B. Bloom.  Crossing the Quality Chasm: A New Health System for the 21st Century , 2002 .

[31]  Daswin De Silva,et al.  Development of User Warrant Ontology for Improving Online Health Information Provision , 2013, ACIS.

[32]  T. Davenport.  Competing on analytics. , 2006, Harvard business review.

[33]  Vijay V. Raghavan,et al.  Web information fusion: A review of the state of the art , 2008, Inf. Fusion.

[34]  Andrew Stranieri,et al.  Multivariate Data-Driven Decision Guidance for clinical scientists , 2013, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW).

[35]  Amarnath Banerjee,et al.  Clinical decision support: Converging toward an integrated architecture , 2012, J. Biomed. Informatics.

[36]  Darcy A. Davis,et al.  Predicting individual disease risk based on medical history , 2008, CIKM '08.

[37]  Tom Ziemke,et al.  On the Definition of Information Fusion as a Field of Research , 2007 .