Predictive modelling methodology for longitudinal data in long-term care****

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This proposal identifies a new collaboration between Dr. Daniel J. Lizotte and PointClickCare. PointClickCare (PCC) is an established Canadian company that provides cloud-based electronic health record (EHR) centred products and services primarily for senior and long-term care facilities. Unlike most other sources of health informatics data, PCC's databases contain highly longitudinal resident and patient records with frequent observation points. With these data, PCC has an opportunity to develop modules for predictive analytics that leverage the full details of residents' histories in long term care to make more effective care and operational decisions. To do this, PCC's data science team requires additional research and development expertise in predictive modelling using temporally-dependent data. To meet these needs, we propose to develop and implement a comprehensive set of predictive models specifically built to leverage the longitudinal nature of resident and care facility data across PCC's databases. We will develop and apply various modelling methods ranging from simple regression analyses and classifiers to complex clustering methods, as needed. New methods for temporally-dependent feature construction will also be developed as needed with the goal of identifying important patterns that can be used to improve care and operational decisions. The resulting models and analysis from this project will enable PCC to offer new and effective tools to its customers, and more broadly will benefit the Canadian public by providing new methods for using longitudinal healthcare data in predictive models.****