Marija Radic, Claudia Vienken, Laurin Nikschat, Thore Dietrich, Holger König, Lorenz Laderick and Dubravko Radic. AI-based Business Models in Healthcare: An Empirical Study of Clinical Decision Support Systems. (ICDEc 2022)

1. © Fraunhofer IMW
Page 1
AI-BASED BUSINESS MODELS IN HEALTHCARE:
AN EMPIRICAL STUDY OF CLINICAL DECISION SUPPORT SYSTEMS
Bucharest, May 9th - 11th, 2022
7TH INTERNATIONAL CONFERENCE ON DIGITAL ECONOMY
This work was supported as a Fraunhofer Flagship Project.
Dr. Marija Radić, Claudia Vienken, Laurin Nikschat, Dr. Thore
Dietrich, Holger Koenig, Lorenz Laderick, Prof. Dr. Dubravko Radić

2. © Fraunhofer IMW
THE FRAUNHOFER-GESELLSCHAFT
EUROPE‘S LARGEST ORGANIZATION FOR APPLIED RESEARCH
◼ 75 institutes and facilities
◼ 29.000 employees
◼ 2.8 billion Euro research budget;
thereof 2.4 billion Euro in
contractual research
Fraunhofer Center for
International Management and
Knowledge Economy IMW
Main locations
Secondary locations
Source: Fraunhofer IMW, as of: 5.7.2021
Page 2

3. © Fraunhofer IMW
INNOVATION IS MORE THAN TECHNICAL INVENTION
THE FRAUNHOFER WAY: A MULTI-PERSPECTIVE APPROACH TO INNOVATION
Engineering
Natural sciences
Life sciences
Economics
Social sciences
Political sciences
Market
Technology
People
INNOVATION
ECO-SYSTEM
Page 3

4. © Fraunhofer IMW
OUTLINE
1. Methodology
Foundation
Business Model Approach
2. Business Model Analysis – General Statistics
3. Results – CDSS Business Model Framework
4. Conclusion
Page 4

5. © Fraunhofer IMW
Methodology: Foundation
◼ Base of the study: comprehensive market analysis for artificial intelligence (AI) solutions in the healthcare
domain
◼ Internal database containing ~1400 structured profiles of established companies, research
institutions and startups, who state the use of AI on their website
◼ Releveant providers were identified by desk research based on articles, blogs, newsletters and further
sources
◼ Three criterias for the selection of clinical decision support systems (CDSS) from the database:
1. Systems that follow the definition, which refers “CDSS as digital systems that provide the decision maker
with the right information at the right time to support healthcare professionals in making clinical
decisions” [1]
2. Focus on excisting business models (research projects where excluded)
3. Systems that target physicians
◼ As a result 33 companies and startups with 36 different CDSS where selected
Page 5

6. © Fraunhofer IMW
Methodology: Business Model Analysis Approach
◼ The concept of a „Business Model Triangle“ by [2] was
followed for analyzing the business models of the 36 selected
CDSS
◼ This approach describes a business model in four
dimensions:
1. Target customer segment
2. Value proposition
3. Value creation
4. Value capture
◼ A morphological analysis based on information available
in the internal database and respective publicly accessible
websites was conducted
◼ Relevant attributes where matched with the dimensions
of the Business Model Triangle
Page 6
The Business Model Triangle by [2]

7. © Fraunhofer IMW
Business Model Analysis: General Statistics
Company size
◼ About half of the companies are small
companies with 10 to 50 employees (49%)
◼ One third are large companies with more than
250 employees (33%)
◼ Followed by microenterprises with a share of
12% and medium-sized companies with 6%
Founding year
◼ About half of the companies (49%) were
founded less than 10 years ago
◼ The remaining companies are predominantly
established companies
Page 7

8. © Fraunhofer IMW
Results: CDSS Business Model Framework
Page 8

9. © Fraunhofer IMW
CDSS Business Model Framework – Value Proposition
◼ Level of care: Most of the CDSS (n=23) operate
on at least two levels of care simultaneously
while diagnosis and therapy recommendation
are the most frequent ones (58% each)
◼ Indication: 18 different indications were
identified, while almost half of the CDSS cover
multiple indications; oncology (n=14) is
predominant followed by cardiology and
pharmacology
◼ Impact: 83% of all CDSS state efficiency im-
provements or cost savings on their website;
almost 60% state an improvement of patient
engagement
Page 9
The value proposition of CDSS is mainly
composed of three different attributes

10. © Fraunhofer IMW
CDSS Business Model Framework – Value Creation
◼ Databases: most of the CDSS consider clinical
trials; other sources are curated expert
knowledge, monitored vital data, clinical
guidelines and knowledge database
◼ Methods: machine learning (52%), data
analytics (50%) or statistical methods (50%) for
decision support where mostly stated; in addi-
tion, almost half of the systems refer to clinical
pathways (n=18)
◼ Interfaces: 58% use electronic health records,
27% clinical information systems and 25%
mobile applications for physicians or patients
Page 10
The value proposition can be stratified along
the lines of databases, methods and interfaces

11. © Fraunhofer IMW
CDSS Business Model Framework – Value Capture
◼ Pricing model: software licensing model is
used in most of the cases (64%); 6 systems offer
single purchase options for e.g., additional
hardware
◼ Pricing models differ in their designs e.g.,
subscription licenses (monthly or annual)
and feature licenses
◼ 39% of the systems offer a modular license
◼ Additional services: some CDSS also offer
additional services such as a laboratory services,
e.g., for blood sample analysis
Page 11
Two attributes were found for the value
capture of CDSS

12. © Fraunhofer IMW
Conclusion
◼ The shown framework provides an overview of possible business model characteristics in the field
of AI-based CDSS.
◼ The analysis shows that CDSS currently focus mainly on the leading causes of death i.e., heart
diseases and cancer [3].
◼ The support of these systems focuses on varying levels of care such as prevention, diagnosis,
therapy recommendation using machine learning and statistical methods.
◼ The majority of analyzed CDSS use licensing as a pricing model.
◼ As AI-based innovations take off, they will allow providers to diagnose disease earlier with greater
accuracy – and ultimately manage it more effectively.
◼ The importance of AI-based CDSS in patient care is undisputed. Still CDSS are rarely used in
everyday clinical practice. Several ethical and legal issues remain to be resolved [4].
Page 12

13. © Fraunhofer IMW
Holger König
Scientific employee of the Price and Service Management
Group
Fraunhofer Center for International Management and
Knowledge Economics IMW
Neumarkt 9-19
04109 Leipzig
Phone +49-341 231039-244
holger.koenig@imw.fraunhofer.de
THANK YOU FOR YOUR ATTENTION!

14. © Fraunhofer IMW
REFERENCES
[1] Steinwendner J. Entscheidungsunterstützungssysteme: von der Datenrepräsentation zur künstlichen Intelligenz.
In: Pfannstiel Mario A, Kassel Kristin, Rasche Christoph. Innovati-onen und Innovationsmanagement im
Gesundheitswesen. Wiesbaden: Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature (2020).
[2] Gassmann O, Frankenberger K, Csik M. The business model navigator: 55 models that will revolutionise your
business. Upper Saddle River, NJ: FT Press (2014).
[3] Centers for Disease Control and Prevention. Number of deaths for leading causes of death.
https://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm, last accessed 2021/06/28.
[4] Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. In: Bohr A,
Memarzadeh K eds, Artificial Intelligence in Healthcare. Academic Press (2020).
Page 14