Predicting patient wait-times in ambulatory health care - QC-322

Desired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences, Mathematics, Operations research
Company: TAP Medical
Project Length: 4 to 6 months
Preferred start date: 07/01/2020
Language requirement: Flexible
Location(s): Montreal, QC, Canada
No. of positions: 4
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About the company: 

TAP Medical simplifies the access to healthcare by creating Canada’s first end-to-end digital healthcare management platform to fulfill the unmet needs of both patients and providers. You are a clinic and do not have a good way to manage no-shows? You are a patient and would like the fastest appointment with an MD? TAP Medical bridges that divide. We streamline the discovery and booking of medical appointments while simultaneously optimizing patient-experience and doctors’ schedules

Please describe the project.: 

No one likes showing up for their medical appointment and waiting for an unpredictable amount of time! This is particularly an issue during the COVID-19 pandemic where grouping people in a waiting room goes against the requirement for social distancing. Why can Uber predict the arrival of your driver within minutes, while medical clinics continue to struggle with telling you when your physician will be ready. A Fraser Report in published in 2014 estimated the societal costs of waiting for healthcare appointments in Canada at $19.2B per year.  The purpose of this internship will be to collect all relevant variables that impact on the duration of medical acts and use Machine Learning (ML) to build a predictive model for the arrival of an MD at a given appointment. On the pateint side, the AI model will enable our software to send an alert that will let the patient know when to leave their home or office and arrive at the clinic for an ideal waiting time of less than 15 minutes. 

Required expertise/skills: 

We are seeking 4 interns with expertise in AI, machine learning or Operations Research.