optimizing healthcare management using deep learning

Initially within the application procedure for searching appropriate combination of care seeker/giver matches it’s observed that user must select a range of options like the type of health care worker (HCW) needed for the job, credentials they require, their estimated rate, distance from the location desired, choosing level of experience HCW possesses, and checking whether or not they have any expertise for each disease that care is needed for which creates a burden for the customer to to be able to manually filter the search criteria according to their needs. And, the same can hold true for the care workers as well, at least after the initial setup they might need to filter the search criteria for jobs of interest every time based on some of the criteria mentioned above and parameters like healthcare setting, care schedule, care services, employment type, etc. Thus, an efficient optimization algorithm based on ensemble of models can eliminate the need to do manual repetitive tasks, find better combinations based on the need, increase user satisfaction. This approach takes into account that not all the parameters in finding a match have same level of importance. And as a result better care planning leads to less user frustration for both parties involved helping the platform to grow faster.

Faculty Supervisor:

Russell Butler

Student:

Partner:

WeBookCare

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

Bishop's University

Program: