Paolo Fraccaro, PhD student
Context-aware computing of actionable information to improve patient safety in primary care
The focus of this PhD focuses on context-aware computing of actionable information in primary care informatics systems to improve patient safety. As population is aging and prevalence of people living with multiple conditions increases, this research investigates how we can move primary care informatics systems from treating the average patient towards a more personalised way of delivering healthcare and guarantee patient safety.
Start: October 2013
End: February 2017
Funded by: Greater Manchester Primary Care Patient Safety Translational Research Centre
Multimorbidity, Chronic Kidney Disease
- Routinely collected primary care data
- Main source of data is the Salford Intergrated Record, which collects data from all GP practices and one secondary care provider in Salford
- Multidisciplinary approach including informatics, statistics, engineering and epidemiology
Benefits and Outcomes
This research is relevant for both clinicians and policy makers. On one side informatics and statistics can analyse data and provide specific information to clinicians about the chances of a patient to develop a disease or an adverse event. On the other side, data modelling allows to identify high risk patients, and policy makers can assess better if an intervention is needed or where to allocate resources.
The aim is to provide examples of how health informatics could be applied on routinely collected data to improve patient safety and healthcare delivery and hope to develop examples that are implementable in primary care informatics systems.
Prof. Iain Buchan
Dr Niels Peek
Dr John Ainsworth
Fraccaro P, Arguello Casteleiro M, Ainsworth J, Buchan I (2015) Adoption of Clinical Decision Support in Multimorbidity: A Systematic Review. JMIR Med Inf 3: e4. Available: http://medinform.jmir.org/2015/1/e4/.
Fraccaro P, Nicolo M, Bonetto M, Giacomini M, Weller P, et al. (2015) Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach. BMC Ophthalmol 15: 10. Available: http://www.biomedcentral.com/1471-2415/15/10.
Fraccaro P, Brown B, Prosperi M, Sperrin M, Buchan I, et al. (2015) Development and preliminary validation of a dynamic, patient-tailored method to detect abnormal laboratory test results. Stud Health Technol Inform 216: 701–705. Available: http://ebooks.iospress.nl/publication/40300.