Health Care Management Science

, Volume 11, Issue 2, pp 152–166 | Cite as

Simultaneous trend analysis for evaluating outcomes in patient-centred health monitoring services

  • Edward C. Conley
  • David R. Owens
  • Stephen L. Luzio
  • Mahesh Subramanian
  • Ali Shaikh Ali
  • Alex Hardisty
  • Omer Rana


The research aim underpinning the Healthcare@Home (HH) information system described here was to enable ‘near real time’ risk analysis for disease early detection and prevention. To this end, we are implementing a family of prototype web services to ‘push’ or ‘pull’ individual’s health-related data via an system of clinical hubs, mobile communication devices and/or dedicated home-based network computers. We are examining more efficient methods for ethical use of such data in timeline-based (i.e. ‘longitudinal’) data analysis systems. A consistent data collation infrastructure is being created for use along the ‘patient path’—accessible wherever patients happen to be. This ‘patient-centred’ infrastructure can be applied in the evaluation of disease progression risk (in the light of clinical understanding of disease processes). In this paper we describe the requirements for making multi-data trend management ‘scale-up’, together with some requirements of an ‘end-to-end’ functioning data collection system. A Service-Oriented Architecture (SOA) approach is used to maximise benefits from (1) clinical evidence and (2) computational models of disease progression that can be made available elsewhere on the SOA. We discuss the implications of this so-called ‘closed loop’ approach for improving healthcare intervention outcomes, patient safety, decision support, objective measurement of service quality and in providing inputs for quantitative healthcare (predictive) modelling.


Web services Time series analysis routines Scalability Chronic disease management Portal technologies Risk monitoring Service-oriented architecture 



This project has been funded in the Inter Enterprise Computing Theme of the UK Department of Trade and Industry (DTI)-led Technology Programme, for which we are grateful. We also acknowledge gratefully the contribution each of our industrial partners has made to this project: IBM, Zarlink Semiconductor and Smart Holograms. ECC and DRO are grateful for support by the Wales Office of Research and Development for Health and Social Care, Wales Assembly Government.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Edward C. Conley
    • 1
    • 2
  • David R. Owens
    • 1
  • Stephen L. Luzio
    • 1
  • Mahesh Subramanian
    • 2
  • Ali Shaikh Ali
    • 2
  • Alex Hardisty
    • 2
  • Omer Rana
    • 2
  1. 1.Diabetes Research Unit, School of MedicineCardiff UniversityCardiffUK
  2. 2.The Welsh e-Science CentreCardiff University School of Computer ScienceCardiffUK

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