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2022/2023  KAN-CIHCO1602U  Frontiers of Digital Healthcare

English Title
Frontiers of Digital Healthcare

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 25
Study board
Study Board for MSc in Business Administration and Innovation in Health Care
Course coordinator
  • Chee-Wee Tan - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Innovation
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 13-02-2022

Relevant links
Learning objectives
  • describe technological trends at the frontier of digital healthcare innovation, including big data, cognitive computing, machine learning, wearables, as well as mobile and social platforms
  • relate trends in digital healthcare innovation to key concepts from academic literature and draw connections in-between relevant theoretical concepts
  • explain and apply methods and techniques of data analysis and machine learning in scenarios of digital healthcare
  • reflect on their individual learning progress and identify touchpoints, intersections, and gaps in comparison to previous knowledge
  • identify a research opportunity at the digital healthcare frontier and conduct independent desk-based research involving secondary data to investigate this issue
Course prerequisites
This elective course is open to master-level students of all study programmes inside and outside CBS. There are no formal prerequisites to participate. Prior knowledge of healthcare or digital technologies field is helpful, but not a must.

This is a mandatory elective course for the MSc in Business Administration and Innovation in Health Care.

To sign up send a 1-page motivational letter and a grade transcript to ily.stu@cbs.dk before the registration deadline for elective courses. You may find the registration deadlines on my.cbs.dk ( https:/​/​studentcbs.sharepoint.com/​graduate/​pages/​registration-for-electives.aspx )

Please also remember to sign up through the online registration.
Examination
Frontiers of Digital Healthcare:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-3
Size of written product Max. 15 pages
Assignment type Project
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
If a student is ill during the regular oral exam, he/she will be able to re-use the project for the make-up exam. If a student is ill during the writing of the project and did not contribute to the project, the make-up exam can be written individually or in groups (provided that other students are taking the make-up/re-exam). If the student did not pass the regular exam or did not show up at the oral exam, he/she must make a new revised project/business plan (confer advice from the examiner) and hand it in on a new deadline specified by the secretariat.
Course content, structure and pedagogical approach

This course aims to deliver participants from various backgrounds with insights into the latest developments, technologies, and trends at the frontier of digital healthcare innovation.

 

Digital technology innovations are poised to disrupt the healthcare landscape, a sector which has often been thought of as one of the least digitized industries. Omnipresent trends in the likes of online social communities, ubiquitous mobile devices, and the surge of smart sensor technologies, have culminated in the exponential growth of available data that bear significant implications for healthcare provision. Additionally, advances in artificial intelligence, cloud computing, machine learning, and other technologies have created unprecedented opportunities for harvesting, storing, processing, and utilizing these massive data repositories, for the benefit of patients, healthcare providers, and other stakeholders in the healthcare industry.

 

A burgeoning number of healthcare startups are currently aiming to disrupt the traditional model in which healthcare services are provided by exploiting advances in digital technologies to assist patients and healthcare professionals at the point of care. At the same time, incumbent players in the healthcare industry (e.g., healthcare providers, pharmaceutical firms, and insurers) as well as new entrants (e.g., information technology leaders such as IBM, Google, and Microsoft) are pushing forward to foster digital innovation under the roof of their own established business models. From a societal standpoint, digital innovations deliver enormous opportunities to bolster the chances of achieving the triple mission of higher quality, lower cost, and better access to healthcare systems worldwide.

 

With digital innovations holding the promise of resolving focal challenges in healthcare, healthcare is transforming into a vibrant space. Governmental institutions, incumbent players, and techological startups are currently seeking much-needed talent that comprehends digitalization trends and is able to turn health data into information pertinent to tackling contemporary problems in the field of healthcare.

 

This course is designed to acquaint participants with the latest technological developments and trends at the frontier of digital healthcare innovation, providing participants with hands-on experience on analyzing and exploiting health data in a real-world setting. Over the duration of this course, we will cover modern trends in digital healthcare innovation, including: big data, cognitive computing, machine learning, wearables, as well as mobile and social platforms. For each of these trends, we will not only drill into its underlying conceptual, theoretical, and methodological foundations, but we will also touch on concrete application scenarios and business cases across different healthcare system contexts. In parallel to these conceptual elements, course participants will acquire and apply basic data mining and machine learning techniques to tackle contemporary problems in healthcare.

 

As the course progresses, course participants will work in groups to compile a learning diary with their summary of, and reflections about, each of the innovation trends reviewed in the lecture including its drivers and inhibitors, enabling technologies, and potential application scenarios. This learning diary will form the basis for the first part of the written product (i.e., project report). Towards the end of the course, students will then identify a specific application scenario (i.e., case) and independently acquire secondary data to analyze this data based on quantitative methods acquired in this course. This analysis should inform their case study research, which then forms the second part of the written product.
Description of the teaching methods
This course embraces a blended learning format, combining classroom teaching with online media and resources.

Classroom teaching blends classic lectures with in-class discussions and small interactive exercises. The classroom sessions will primarily focus on the introduction of technological trends and their applications to the field of healthcare.

From session to session, participants will work through an open online course on machine learning. These self-paced online modules revolves around the acquisition of basic machine learning techniques and are intentionally designed to not limit their application to the healthcare field.

In order to link classroom activities with online pedagogical materials as well as
reflect on the methodological and technical foundations of the course, practical demonstrations will take place in class to showcase how machine learning techniques covered online can be applied.

The course also aims to include industry experts as guest speakers as and when necessary.
Feedback during the teaching period
Students will receive feedback amongst others
- in class during and after discussion and exercise modes
- online in quizzes of the self-paced study modules
- from their peers they work with in groups
- during office hours, on request
- at the oral examination
Student workload
teaching 30 hours
preparation of class 60 hours
project 90 hours
exam preparation 35 hours
Expected literature

Relevant literature: 
(subject to change)

 
Last updated on 13-02-2022