This work is a collaboration between the Department of Information Science (Prof Stephen Cranefield, Michael Winikoff and Phd Student Manjula Devananda) and BPAC (the Best Practice Advocacy Centre New Zealand, bpac.org.nz).
The aim of this project is to help primary health care organisations manage their workload, specifically, the workload arising from patients with long-term health conditions. We are developing computational models that will allow us to simulate the progression of patients through a defined care plan, in order to predict the tasks that need to be carried out in a given week, and hence the workload for the medical practice. In order to do this we need (i) an understanding of the (generic) defined care plans for long-term medical conditions, which BPAC is providing (in the form of expert knowledge – note that this information is not specific to individual patients); and (ii) patient data, which is being provided by BPAC, in de-identified form. This project’s methodology follows a typical design science approach: a software solution is developed, and evaluated. There is no medical protocol, or interaction with patients.
Manjula was runner-up of 3MT at BARC (Business Annual Research Conference), 2015.
She has competed at the 3MT, 2015.
- Manjula Devananda, Stephen Cranefield, Hywel Lloyd and Michael Winikoff. "Patient Information Model to Support Population-level Workload Analysis". Australasian Conference on Information Systems (ACIS 2017). https://www.acis2017.org/
- Devananda, Manjula; Cranefield, Stephen; Winikoff, Michael; and Lloyd, Hywel, (2017). "WORKLOAD PREDICTION MODEL OF A PRIMARY HEALTH CENTRE". In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, June 5-10, 2017 (pp. 1192-1204). ISBN 978-989-207655 Research Papers. http://aisel.aisnet.org/ecis2017_rp/77
- Bastin Tony Roy Savarimuthu, Sherlock Licorish, Manjula Devananda, Georgia Greenheld, Virginia Dignum and Frank Dignum. Developers responses to app review feedback A study of communication norms in app development, COIN@AAMAS 2017.