Customer:

The Northstar Clinical Network is coordinated by UCL and Great Ormond Street Hospital. The network brings together clinicians, hospitals, academics, biotechs, healthcare authorities and charities with interests in the care of patients with Duchenne Muscular Dystrophy (DMD).

Key Challenges:

  • Collect longitudinal patient data in clinical settings over an extended period
  • Support the analysis of data for research
  • Drive the adoption of a unified database across the UK
  • Provide support and work in close partnership with the network
  • Deliver, in partnership, data as required by NICE
  • Evolve the software services as the network’s focus changes
  • Satisfy information governance requirements across multiple centres

The Solution:

  • The development of clinical forms for use in data collection in clinics throughout the UK
  • A Clarinet Technology database system for DMD patient records
  • Secure online data entry services 
  • Automated paper form scanning services
  • Data analytic services
  • A robust approach to information security management

The Result:

  • A valuable shared database asset establishing the network within the clinical community
  • A national system providing a vehicle to drive best practice
  • A 15 year longitudinal dataset of DMD patient data
  • Support for research leading to substantial publication and changes to clinical practice
  • Recognised by NICE for monitoring patients on new DMD treatments

Customer:

iSMAC is an international consortium bringing together national clinical networks engaged in the care of patients with Spinal Muscular Atrophy (SMA).

Key Challenges:

  • Combine clinical data from different partner networks
  • Coordinate partners in the common definition of data
  • Work with differing data collection technologies
  • Deliver data to partner organisation according to data sharing agreements
  • Respond to data analysis requests
  • Clean and manage data collected in clinical settings

The Solution:

  • Host and maintain a Clearwater data warehouse
  • Integrate Clearwater and Clarinet
  • Populate a Clearwater Metadata Repository
  • Publish an online annotatable Data Dictionary
  • Host regular technical meetings
  • Construct data cleaning and analysis pipelines
  • Work closely with network data managers
  • Establish a formal data import, review and release process

The Result:

  • An operational data warehouse populated from heterogeneous source data
  • Automated support for data import, cleaning, analysis and export 
  • Data sharing facilities made available to partner organisations
  • Accelerated resolution of data definition problems
  • Routine availability of a coherent data set derived from multiple clinical networks