Care Anywhere Program Structure

Care Anywhere is a multidisciplinary program open to students from a wide range of backgrounds. Students enrol in a “home program” appropriate to their background (e.g., in engineering, computer science, physical therapy, kinesiology, etc.) and satisfy the general requirements of their home program.

Students in the natural sciences and engineering (NSE) take the following 9 credits of Care Anywhere courses, typically as part of their home program (in some cases, students may have to take a slight overload to fulfill their home program requirements; non-NSE students may be eligible to take a reduced number of Care Anywhere courses and still participate in the program – please check with your home program):

Term 1:Term 2:
RHSC 506D: Care Space Placement (1 cr; Prof. Janice Eng)BMEG 591K: Fundamentals of Biosensing (3 cr; Prof. Calvin Kuo)
APSC 598P: Care Anywhere Project ( 3 cr; Prof. Antony Hodgson)
APSC 598S: Care Anywhere Seminar (1 cr; Profs. Antony Hodgson and Babak Shadgan)APSC 598S (continued)
APSC 598L: Care Anywhere Professional Skills and Mentorship Program (1 cr; Prof. Lyndia Wu)APSC 598L (continued)

In addition, students will take:

  • Care Anywhere Focus Courses – 3 credits from focus list (see below)
  • Electives and/or additional courses as needed to satisfy program requirements of home departments (typically 6 credits for NSE programs). We have identified below a number of related courses available at UBC that may be of particular interest to Care Anywhere students.

See additional details on these courses below:

RHSC 506D – Care Space Placement (T1, 1cr)

The Care Space Placement is a 1-credit graduate course in which we will:

1) Orient trainees to the clinical environment, focusing on models of care and roles of various stakeholders (e.g., patients, family, different health care providers), as well as models of change management focused on integrating technology into care settings.

2) Enable trainees to be embedded in a care-space setting (e.g., long-term care facility, rehabilitation clinic, fall-prevention clinic) relevant to their proposed thesis research. This will enable trainees to gain insight into healthcare problems, change opportunities and generate potential solutions.

Download the RHSC 506D Course Outline

Required for NSE students, optional for non-NSE students.

BMEG 591K – Fundamentals of Biosensing Technologies and Systems (T2, 3cr)

This 3-credit graduate course will present an integrated framework on aspects of biosensing design, deployment, and integration into healthcare delivery that will enable both technical and health-focused trainees to understand each other’s knowledge bases.  

Required for all Care Anywhere students (both NSE and non-NSE). 

APSC 598P – Care Anywhere Project (T2, 3cr)

This course will span 1.5 terms with a project that relates specifically to Care Anywhere’s focus on developing biosensing technologies, deploying to a care environment, integrating data streams into health information networks, or using data analytics to make actionable decisions.

Trainees will work in teams of 4 to 5, comprised of students with diverse academic backgrounds (e.g., 2-3 engineering, 1 computer science, and 1 health trainee). Teams will check in regularly with either the instructor or supervising postdoctoral fellow They will make a final presentation at an annual Care Anywhere Showcase event. 

Required for all Care Anywhere students (both NSE and non-NSE).

APSC 598S – Care Anywhere Seminar (T1&2, 1cr)

To build a shared sense of community, all Care Anywhere trainees and faculty supervisors will participate in a biweekly 1 credit seminar series in which graduate students will present their thesis proposals or their ongoing work, modeling their presentations on formats expected in typical conferences in the field.

Required for all Care Anywhere students (both NSE and non-NSE). 

APSC 598L – Professional Skills Development & Peer Mentoring (T1&2, 1cr)

All Care Anywhere graduate students will take a 1-credit Professional Skills Development and Peer Mentoring course. In this course, students will create an Individual Development Plan identifying a selection of 5-10 professional development workshops (minimum 30 contact hours) including a mandatory EDI workshop and entrepreneurial workshop. The peer mentoring component is required for NSE trainees and optional for non-NSE trainees. It will connect a first-year Care Anywhere trainee, a more senior trainee (typically a doctoral student), and an industry or healthcare mentor for informal meetings once per term.

Required for NSE students, optional for non-NSE students.

Care Anywhere Focus and Elective Courses

Natural Sciences and Engineering Master students will build disciplinary expertise by taking at least one Focus Area Core Course from their choice of three key areas: 

  • biosensor design (including fabrication and evaluation)
  • clinical systems and applications (opportunity identification, data acquisition in use environments, integration into medical networks and health information systems); or 
  • data analytics (use of machine learning and other data analytic techniques to extract meaningful information from large data records and inform clinical care).

NSE Master’s students will take an additional 6 credits of free electives to build expertise for their thesis. Courses could range from statistics, engineering, health and physiology. If desired, students could take further electives directed related to Care Anywhere.  

Courses particularly relevant to Care Anywhere are listed here (focus courses in bold):

Biosensor Design:

  • BMEG 599 Implantable Biosensors (new 2023) – Prof. Babak Shadgan
  • ELEC 521 Biomedical Microdevices
  • ELEC 546 Micro and Nano Fabrication Technologies
  • ELEC 472 Biomechatronics
  • ELEC 462 Sensors and Actuators in Microsystems
  • MECH 420 Sensors and Actuators
  • MECH 521 Fundamentals of Microelectromechanical Systems

Clinical Systems and Applications:

  • NURS 586I Health Informatics
  • BMEG 523 Clinical Informatics – Prof. Matthias Görges
  • CPSC 533R Visual AI – Deep Learning for Computer Graphics
  • BMEG 511 Fundamentals of Applied Pathophysiology in Biomedical Engineering – Prof. Babak Shadgan
  • CPSC 544 Human Computer Interaction
  • EECE 565 Communication Networks
  • EECE 569 Mobile Communications Networks

Data Analytics:

  • ELEC 500M Machine Learning Fundamentals for Engineers (new 2022) – Prof. Xiaoxiao Li
  • EECE 568 From Exploring to Building Machine Learning Models
  • EECE 570 Visual Computing
  • CPSC 547 Information Visualization