Examining database persistence of ISO/EN 13606 standardized electronic health record extracts: relational vs. NoSQL approaches

BackgroundThe objective of this research is to compare the relational and non-relational (NoSQL) database systems approaches in order to store, recover, query and persist standardized medical information in the form of ISO/EN 13606 normalized Electronic Health Record XML extracts, both in isolation and concurrently. NoSQL database systems have recently attracted much attention, but few studies in the literature address their direct comparison with relational databases when applied to build the persistence layer of a standardized medical information system.MethodsOne relational and two NoSQL databases (one document-based and one native XML database) of three different sizes have been created in order to evaluate and compare the response times (algorithmic complexity) of six different complexity growing queries, which have been performed on them. Similar appropriate results available in the literature have also been considered.ResultsRelational and non-relational NoSQL database systems show almost linear algorithmic complexity query execution. However, they show very different linear slopes, the former being much steeper than the two latter. Document-based NoSQL databases perform better in concurrency than in isolation, and also better than relational databases in concurrency.ConclusionNon-relational NoSQL databases seem to be more appropriate than standard relational SQL databases when database size is extremely high (secondary use, research applications). Document-based NoSQL databases perform in general better than native XML NoSQL databases. EHR extracts visualization and edition are also document-based tasks more appropriate to NoSQL database systems. However, the appropriate database solution much depends on each particular situation and specific problem.

[1]  Ricardo Sánchez-de-Madariaga,et al.  ccML, a new mark-up language to improve ISO/EN 13606-based electronic health record extracts practical edition , 2013, J. Am. Medical Informatics Assoc..

[2]  Daniel J. Abadi,et al.  Performance tradeoffs in read-optimized databases , 2006, VLDB.

[3]  Rinkle Rani,et al.  Managing Data in Healthcare Information Systems: Many Models, One Solution , 2015, Computer.

[4]  Rinkle Rani,et al.  Modeling and querying data in NoSQL databases , 2013, 2013 IEEE International Conference on Big Data.

[5]  E. F. Codd,et al.  A relational model of data for large shared data banks , 1970, CACM.

[6]  E. F. Codd,et al.  A Relational Model for Large Shared Data Banks , 1970 .

[7]  Yang Jin,et al.  Research on the distributed electronic medical records storage model , 2011, 2011 IEEE International Symposium on IT in Medicine and Education.

[8]  Jesualdo Tomás Fernández-Breis,et al.  LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics , 2009, Int. J. Medical Informatics.

[9]  Tim A. Majchrzak,et al.  Using document-based databases for medical information systems in unreliable environments , 2012, ISCRAM.

[10]  Ying Liu,et al.  Closing the functional and Performance Gap between SQL and NoSQL , 2016, SIGMOD Conference.

[11]  Pranav Bapat,et al.  A Comprehensive Review of Sentiment Analysis of Stocks , 2014 .

[12]  Georg Duftschmid,et al.  Extraction of standardized archetyped data from Electronic Health Record systems based on the Entity-Attribute-Value Model , 2010, Int. J. Medical Informatics.

[13]  Mario Pascual,et al.  PITES: Telemedicine and e-Health Innovation Platform , 2013 .

[14]  Ricardo Sánchez-de-Madariaga,et al.  Service for the Pseudonymization of Electronic Healthcare Records Based on ISO/EN 13606 for the Secondary Use of Information , 2015, IEEE Journal of Biomedical and Health Informatics.

[15]  Wei Xu,et al.  MongoDB Improves Big Data Analysis Performance on Electric Health Record System , 2014 .

[16]  Erik Sundvall,et al.  Comparing the Performance of NoSQL Approaches for Managing Archetype-Based Electronic Health Record Data , 2016, PloS one.

[17]  Erhard Rahm,et al.  XMach-1: A Benchmark for XML Data Management , 2001, BTW.

[18]  Albert Alonso,et al.  PITES: TELEMEDICINE AND E-HEALTH INNOVATION PLATFORM , 2014 .

[19]  Eric C. Pan,et al.  The value of health care information exchange and interoperability. , 2005, Health affairs.

[20]  D Kalra,et al.  ISO 13606 Electronic Health Record Communication Part 1: Reference Model [99 pages] , 2008 .

[21]  Kevin Waugh,et al.  Understanding object-relational mapping: A framework based approach , 2009 .

[22]  Daniel J. Abadi,et al.  Column-stores vs. row-stores: how different are they really? , 2008, SIGMOD Conference.

[23]  Dipak Kalra,et al.  The openEHR Foundation. , 2005, Studies in health technology and informatics.

[24]  Marcin Zukowski,et al.  From x100 to vectorwise: opportunities, challenges and things most researchers do not think about , 2012, SIGMOD Conference.

[25]  Jeffrey D. Uuman Principles of database and knowledge- base systems , 1989 .

[26]  Prakash M. Nadkarni,et al.  Guidelines for the effective use of entity-attribute-value modeling for biomedical databases , 2007, Int. J. Medical Informatics.

[27]  Golajapu Venu Madhava Rao,et al.  Big Data Electronic Health Records Data Management and Analysis on Cloud with MongoDB: A NoSQL Database , 2015 .

[28]  David W. Bates,et al.  A Consensus Action Agenda for Achieving the National Health Information Infrastructure , 2004, Journal of the American Medical Informatics Association.

[29]  Martin L. Kersten,et al.  Breaking the memory wall in MonetDB , 2008, CACM.

[30]  Erik Sundvall,et al.  Performance of XML Databases for Epidemiological Queries in Archetype-Based EHRs , 2012 .

[31]  Huilong Duan,et al.  Archetype relational mapping - a practical openEHR persistence solution , 2015, BMC Medical Informatics and Decision Making.

[32]  Zachary Parker,et al.  Comparing NoSQL MongoDB to an SQL DB , 2013, ACMSE '13.

[33]  Michael Lane,et al.  An evaluation of NoSQL databases for EHR systems , 2014 .

[34]  WhitsonGeorge Health Level Seven , 2009, Definitions.

[35]  Jorge Bernardino,et al.  NoSQL databases: MongoDB vs cassandra , 2013, C3S2E '13.

[36]  Dipak Kalra,et al.  ISO 13606 Electronic Health Record Communication Part 2: Archetype Interchange Specification [146 pages] , 2009 .

[37]  Jyotsna Talreja Wassan Modelling Stack Framework for Accessing Electronic Health Records with Big Data Needs , 2014 .

[38]  Oscar Moreno,et al.  Normalized medical information visualization , 2015, MIE.

[39]  Ibrahim Farag,et al.  A Dynamic Scheduling Algorithm for Spawn Processes in MPI-2 to Improve and Maintain Load Balancing , 2014 .

[40]  Catalina Martínez-Costa,et al.  Using the ResearchEHR platform to facilitate the practical application of the EHR standards , 2012, J. Biomed. Informatics.

[41]  T Beale,et al.  Archetypes: Constraint-based Domain Models for Future-proof Information Systems , 2000, OOPSLA 2000.

[42]  Chen Wang,et al.  Schema Management for Document Stores , 2015, Proc. VLDB Endow..

[43]  C. M. Sperberg-McQueen,et al.  eXtensible Markup Language (XML) 1.0 (Second Edition) , 2000 .