BACKGROUND: Managed clinical networks have been used to coordinate chronic disease management across geographical regions in the United Kingdom. Our objective was to review how clinical networks and multidisciplinary team-working can be supported by Web-based information technology while clinical requirements continually change. METHODS: A Web-based population information system was developed and implemented in November 2000. The system incorporates local guidelines and shared clinical information based upon a national dataset for multispecialty use. Automated data linkages were developed to link to the master index database, biochemistry, eye screening, and general practice systems and hospital diabetes clinics. Web-based data collection forms were developed where computer systems did not exist. The experience over the first 10 years (to October 2010) was reviewed. RESULTS: The number of people with diabetes in Tayside increased from 9694 (2.5% prevalence) in 2001 to 18,355 (4.6%) in 2010. The user base remained stable (~400 users), showing a high level of clinical utility was maintained. Automated processes support a single point of data entry with 10,350 clinical messages containing 40,463 data items sent to external systems during year 10. The system supported quality improvement of diabetes care; for example, foot risk recording increased from 36% in 2007 to 73.3% in 2010. CONCLUSIONS: Shared-care datasets can improve communication between health care service providers. Web-based technology can support clinical networks in providing comprehensive, seamless care across a geographical region for people with diabetes. While health care requirements evolve, technology can adapt, remain usable, and contribute significantly to quality improvement and working practice.
BACKGROUND: Managed clinical networks have been used to coordinate chronic disease management across geographical regions in the United Kingdom. Our objective was to review how clinical networks and multidisciplinary team-working can be supported by Web-based information technology while clinical requirements continually change. METHODS: A Web-based population information system was developed and implemented in November 2000. The system incorporates local guidelines and shared clinical information based upon a national dataset for multispecialty use. Automated data linkages were developed to link to the master index database, biochemistry, eye screening, and general practice systems and hospital diabetes clinics. Web-based data collection forms were developed where computer systems did not exist. The experience over the first 10 years (to October 2010) was reviewed. RESULTS: The number of people with diabetes in Tayside increased from 9694 (2.5% prevalence) in 2001 to 18,355 (4.6%) in 2010. The user base remained stable (~400 users), showing a high level of clinical utility was maintained. Automated processes support a single point of data entry with 10,350 clinical messages containing 40,463 data items sent to external systems during year 10. The system supported quality improvement of diabetes care; for example, foot risk recording increased from 36% in 2007 to 73.3% in 2010. CONCLUSIONS: Shared-care datasets can improve communication between health care service providers. Web-based technology can support clinical networks in providing comprehensive, seamless care across a geographical region for people with diabetes. While health care requirements evolve, technology can adapt, remain usable, and contribute significantly to quality improvement and working practice.
Authors: Bernardo Meza-Torres; Scott G Cunningham; Christian Heiss; Mark Joy; Michael Feher; Graham P Leese; Simon de Lusignan; Fabrizio Carinci Journal: J Diabetes Res Date: 2022-06-14 Impact factor: 4.061
Authors: Nicholas Conway; Karen A Adamson; Scott G Cunningham; Alistair Emslie Smith; Peter Nyberg; Blair H Smith; Ann Wales; Deborah J Wake Journal: J Diabetes Sci Technol Date: 2017-09-14
Authors: Brian Kennon; Graham P Leese; Lynda Cochrane; Helen Colhoun; Sarah Wild; Duncan Stang; Naveed Sattar; Donald Pearson; Robert S Lindsay; Andrew D Morris; Shona Livingstone; Matthew Young; John McKnight; Scott Cunningham Journal: Diabetes Care Date: 2012-09-25 Impact factor: 19.112
Authors: Samantha Alvarez-Madrazo; Stuart McTaggart; Clifford Nangle; Elizabeth Nicholson; Marion Bennie Journal: Int J Epidemiol Date: 2016-05-10 Impact factor: 7.196