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Journal of Healthcare Engineering
Volume 5, Issue 4, Pages 457-478
Research Article

Information Analytics for Healthcare Service Discovery

Lily Sun,1 Mohammad Yamin,2 Cleopa Mushi,1 Kecheng Liu,3,4 Mohammed Alsaigh,2 and Fabian Chen5

1School of Systems Engineering, University of Reading, UK
2Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia
3Henley Business School, University of Reading, UK
4School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China
5Royal Berkshire NHS Foundation Trust, Reading, UK

Received 1 February 2014; Accepted 1 August 2014

Copyright © 2014 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. NHS, “2013/14 Choice Framework. Department of Health,” accessed 15 September 2013
  2. R. T. Cenfetelli, I. Benbasat, and S. Al-Natour, “Addressing the what and how of online services: positioning supporting-services functionality and service quality for business-to-consumer success,” Information Systems Research, vol. 19, no. 2, pp. 161–181, 2008. View at Google Scholar
  3. H. Landrum, V. R. Prybutok, X. Zhang, and D. Peak, “Measuring IS systems service quality with SERVQUAL: users' perceptions of relative importance of the five SERVPERF dimensions,” Informing Science: the international journal of an emerging trans-discipline, vol. 12, pp. 17–35, 2009. View at Google Scholar
  4. A. Parasuraman, V. A. Zeithaml, and L. L. Berry, “SERVQUAL: A multiple-item scale for measuring customer perceptions of service quality,” Journal of Retailing, vol. 64, no. 1, pp. 12–40, 1998. View at Google Scholar
  5. CQC, Raising standards, putting people first - our strategy for 2013 to 2016, Care Quality Commission, UK, accessed 01 October 2013,
  6. A. Jøsang, R. Ismail, and B. Colin, “A survey of trust and reputation systems for online service provision,” Decision Support Systems, vol. 43, no. 2, pp. 618–644, 2007. View at Google Scholar
  7. D. J. Kim, D. L. Ferrin, and H. R. Rao, “Trust and satisfaction, two stepping stones for successful e-commerce relationships: a longitudinal exploration,” Information Systems Research, vol. 20, no. 2, pp. 237–257, 2009. View at Google Scholar
  8. V. Mantzana, M. Themistocleous, Z. Irani, and V. Morabito, “Identifying healthcare actors involved in the adoption of information systems,” European Journal of Information Systems, vol. 16, no. 1, pp. 91–102, 2007. View at Google Scholar
  9. A. Boonstra, D. Boddy, and S. Bell, “Stakeholder management in IOS projects: analysis of an attempt to implement an electronic patient file,” European Journal of Information Systems, vol. 17, no. 2, pp. 100–111, 2008. View at Google Scholar
  10. G. Büyüközkan, G. Çifçi, and S. Güleryüz, “Strategic analysis of healthcare service quality using fuzzy AHP methodology,” Expert Systems with Applications, vol. 38, pp. 9407–9424, 2011. View at Google Scholar
  11. R. Scheepers, H. Scheepers, and O. K. Ngwenyama, “Contextual influences on user satisfaction with mobile computing: findings from two healthcare organisations,” European Journal of Information Systems, vol. 15, no. 3, pp. 261–268, 2006. View at Google Scholar
  12. K. Liu, L. Sun, and Y. Fu, “Ontological modelling of content management and provision,” Journal of Information and Software Technology Elsevier, vol. 50, no. 11, pp. 1155–1164, 2008. View at Google Scholar
  13. L. Sun and C. J. Mushi, “Case-based analysis in user requirements modelling for knowledge construction,” Information and Software Technology, vol. 52, no. 7, pp. 770–777, 2010. View at Google Scholar
  14. P. Soffer and I. Hadar, “Applying ontology-based rules to conceptual modelling: a reflection on modelling decision-making,” European Journal of Information Systems, vol. 16, no. 5, pp. 599–611, 2007. View at Google Scholar
  15. K. Liu, Semiotics in information systems engineering, Cambridge University Press, Cambridge, 2000.
  16. P. Mika, “Ontologies are us: a unified model of social networks and semantics,” Journal of web Semantics, vol. 5, no. 1, pp. 5–15, 2007. View at Google Scholar
  17. D. Fensel, Ontologies: silver bullet for knowledge management and electronic commerce, Springer-Verlag, Berlin, 2001.
  18. R. K. Stamper, M. Hafkamp, and Y. Ades, “Understanding the roles of signs and norms in organisations - a semiotic approach to information systems design and behaviour,” Information Technology, Elsevier, vol. 19, no. 1, pp. 67–116, 2000. View at Google Scholar
  19. L. Sun, K. Ousmanou, and M. Cross, “An ontological modelling of user requirements for personalised information provision,” Information Systems Frontier, vol. 12, no. 3, pp. 337–356, 2010. View at Google Scholar
  20. J. J. Gibson, The Ecological Approach to Visual Perception, Houghton Mifflin Company, Boston, 1968.
  21. IEEE, P1484.2/D8 Draft Standard For Learning Technology - Public and Private inFormation (PAPI) For Learners (PAPI Learner) - Core Features, IEEE Computer Society, Institute of Electrical and Electronics Engineers, Inc, 2001.
  22. IMS, “IMS Learner information package (LIP) specification, IMS Global learning consortium, Inc,” accessed 04 October 2013
  23. G. Fenza, D. Furno, and V. Loia, “Hybrid approach for context-ware service discovery in healthcare domain,” Journal of Computer and System Sciences, Elsevier, vol. 78, pp. 1232–1247, 2012. View at Google Scholar
  24. A. Holt, I. Bichindaritz, and R. Schmidt, “Medical applications in case-based reasoning,” The Knowledge Engineering Review, vol. 20, no. 3, pp. 289–292, 2005. View at Google Scholar
  25. G. C. Niemeijer, R. J. Does, J. Mast, A. Trip, and J. Heuvel, “Generic project definitions for improvement of health care delivery: a case-based approach,” Quality Management in Health Care, vol. 20, no. 2, pp. 152–164, 2011. View at Google Scholar
  26. T. L. Saaty, “Relative measurement and its generalization in decision making: why pairwise comparisons are central in mathematics for the measurement of intangible factors - the analytic hierarchy/network process,” Review of the Royal Spanish Academy of Sciences, Series A, Mathematics, vol. 102, no. 2, pp. 251–318, 2008. View at Google Scholar
  27. L. Kuntz and A. Vera, “Modular organization and hospital performance,” Health Services Management Research, vol. 20, no. 1, pp. 48–58, 2007. View at Google Scholar
  28. L. Pecchia, U. Bracale, P. Melillo, M. Sansone, and M. Bracale, “AHP for health technology assessment,” in Proceedings of the International Symposium on the Analytic Hierarchy Process, pp. 1–8, University of Pittsburgh, PA, 2009.
  29. A. Charnes, W. Cooper, A. Y. Lewis, and L. M. Seiford, Data envelopment analysis: theory, methodology and application, Kluwer Academic Publishers, MA, 1997.
  30. Y. Juan, “A hybrid approach using data envelopment analysis and case-based reasoning for housing refurbishment contractors' selection and performance improvement,” Expert Systems with Applications, vol. 36, no. 3, pp. 5702–5710, 2009. View at Google Scholar
  31. A. Ishizaka and A. Labib, “Review of the main developments in the analytic hierarchy process,” Expert Systems with Applications, vol. 38, no. 11, pp. 14336–14345.
  32. O. Dura'n and J. Aguilo, “Computer-aided machine-tool selection based on a fuzzy-AHP approach,” Expert Systems with Applications, vol. 34, pp. 1787–1794, 2008. View at Google Scholar
  33. R. Ramanathan, “Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process,” Computer & Operations Research, vol. 33, pp. 1289–1307, 2006. View at Google Scholar
  34. C. C. Chow and P. Luk, “A strategic service quality approach using analytic hierarchy process,” Managing Service Quality, vol. 15, no. 3, pp. 78–289, 2005. View at Google Scholar
  35. R. Parameshwaran, P. S. S. Srinivasan, M. Punniyamoorthy, S. T. Charunyanath, and C. Ashwin, “Integrating fuzzy analytical hierarchy process and data envelopment analysis for performance management for automobile repair shops,” European Journal of Industrial Engineering, vol. 3, no. 4, pp. 450–467, 2009. View at Google Scholar
  36. P. Baughan, B. O'Neill, and E. Fletcher, “Auditing the diagnosis of cancer in primary care: the experience in Scotland,” British Journal of Cancer, vol. 101, pp. 87–91, 2009. View at Google Scholar
  37. M. O'Reilly and N. Parker, “Unsatisfactory saturation: a critical exploration of the notion of saturated sample sizes in qualitative research,” Qualitative Research, vol. 13, no. 2, pp. 190–197, 2013. View at Google Scholar
  38. G. Guest, A. Bunce, and L. Johnson, “How many interviews are enough? An experiment with data saturation and variability,” Field Methods, vol. 18, no. 1, pp. 59–82, 2006. View at Google Scholar
  39. M. Mason, “Sample Size and Saturation in PhD Studies Using Qualitative Interviews, Forum Qualitative Sozialforschung,” Qualitative Social Research, vol. 11, no. 3, 2010. View at Google Scholar
  40. P. C. Zikopoulos, C. Eaton, D. deRoos, T. Deutsch, and G. Lapis, Understanding big data: analytics for enterprise class Hadoop and streaming data, ISBN: 9780071790536, McGraw Hill, New York, 2012.
  41. C. White, Using big data for smarter decision making, IBM BI Research, All Rights Reserved, 2011.