Clinical Decision Support Systems (CDSS) were explicitly introduced in the 90’s with the aim of providing knowledge to clinicians in order to influence its decisions and, therefore, improve patients’ health care. There are different architectural approaches for implementing CDSS. Some of these approaches are based on cloud computing, which provides on-demand computing resources over the internet. The goal of this paper is to determine and discuss key issues and approaches involving architectural designs in implementing a CDSS using cloud computing. To this end, we performed a standard Systematic Literature Review (SLR) of primary studies showing the intervention of cloud computing on CDSS implementations. Twenty-one primary studies were reviewed. We found that CDSS architectural components are similar in most of the studies. Cloud-based CDSS are most used in Home Healthcare and Emergency Medical Systems. Alerts/Reminders and Knowledge Service are the most common implementations. Major challenges are around security, performance, and compatibility. We concluded on the benefits of implementing a cloud-based CDSS since it allows cost-efficient, ubiquitous and elastic computing resources. We highlight that some studies show weaknesses regarding the conceptualization of a cloud-based computing approach and lack of a formal methodology in the architectural design process.

1. Architectural approaches for implementing
Clinical Decision Support Systems in Cloud: A
Systematic Review
Luis Tabares, Jhonatan Hernandez and Ivan Cabezas
imcabezas@usbcali.edu.co
June 27, 2016
International Workshop on Cloud Connected Health, CCH 2016, Washington D.C .
1

2. 2
IvanLuisLIDIS Jhonatan

3. Content
Clinical Decision Support Systems
Cloud Computing
Systematic Review Protocol
Systematic Review Results
Final Remarks
3

4. Clinical Decision Support Systems
CDSS:
Systems providing clinicians, staff and
patients with intelligently filtered knowledge
and person-specific information, presented at
appropiate time, to enhance health and
health care outcomes
Types:
• Alerts and Reminders
• Knowledge service
• Diagnostic, treatment and prescription
support
• Information Recovery
• Image recognition and interpretation
4
Administrative
Managing clinical complexity
Cost control
Decision support
(Wyatt & Spiegelhalter, 1991; Berner,
2009; Goertzel, 1969; Coiera, 2005)
Knowledge
based
Non-
knowledge
based

5. Cloud Computing
5
Model for enabling ubiquitous, convenient, on-demand network
access to a shared pool of configurable computing resources.
On-demand self-service
Broad network access
Resource pooling
Rapid elasticity
Measured service
(Mell & Grance, 2009)

6. Systematic Literature Review
6
A systematic literature review (SLR) is a
means of identifying, evaluating and
interpreting all available research relevant
to a particular research question, or topic
area, or phenomenon of interest
(Kitchenham, 2004; “Exploring Systematic
Reviews,” n.d.)

7. SLR Protocol
7
(Kitchenham, 2004)

8. SLR Planning
8
Identified need:
Determine and discuss key issues and approaches involving
architectural designs in implementing a CDSS using Cloud
Computing.
CDSS
Cloud
Computing
Intervention of Cloud
Computing in CDSS
implementations
Identification
of the need
for a review

9. SLR Planning (ii)
9
Research Questions:
ID Research Question (RQ)
RQ1 What evidence is there about implementing CDSS in the cloud since 2010?
What are the major architectural approaches, contributions, limitations and
concerns about implementing Cloud CDSS?
RQ2 Among health areas, which have more CDSS implementations?
RQ3 What types of CDSS are being built?
RQ4 What are the quality attributes that are typically driven in CDSS
architectural designs?
RQ5 What are the main data sources used in cloud-based CDSS?
RQ6 What evidence is there that cloud computing is an adequate approach for
implementing CDSS?
Specifying
the research
question(s)

10. SLR Conducting
10
Search Process
Identification
of research

11. SLR Conducting (ii)
11
Selection of Primary Studies
Inclusion Criteria (IC)
ID Criteria
IC1 Primary studies published between 2010 and 2016
IC2 Journals and conference proceedings
IC3 Articles describing the use or intervention of cloud
computing on CDSS
Exclusion Criteria (EC)
ID Criteria
EC1 Articles not showing the intervention of cloud computing
on CDSS
EC2 Duplicated reports of the same study
Selection of
primary
studies

12. SLR Conducting (iii)
12
Study Quality Assessment
ID Assessment Question (AQ) Score
AQ1 Was the method process properly
described?
12,5%
AQ2 Were the results clearly described? 12,5%
AQ3 Was the architectural approach
described?
25%
AQ4 It is possible to identify key quality
attributes or driving design scenarios?
25%
AQ5 The article guides a future architectural
design to conduct a CDSS
implementation?
25%
Study
quality
assessment

13. SLR Conducting (iv)
13
Data Extraction & Monitoring
Data extraction
and monitoring

14. SLR Conducting (v)
14
Data Extraction Template
Extracted Data
General Data: data extractor, extraction date, data checker, checking date, study identifier,
title, authors, year of publication, full reference, name of database, type of source, name of
source and quality assessment score
Summary of the proposed architectural approach
Contributions of cloud computing on CDSS
Gaps on intervention of cloud computing on CDSS
Challenges of computing on CDSS
Application area within the domain of health
Types of proposed clinical decision support systems
List of quality attributes addressed
Data sources proposed for the implementation of the CDSS
Data extraction
and monitoring

15. SLR Conducting (vi)
15
Data Analysis
ID Synthesis or Tabulations (T) RQ
T1 For each study, the proposed architectural approach, its main
contributions, gaps and challenges
RQ1
T2 Number of studies per outcome about intervention of cloud
computing on CDSS
RQ6
T3 Number of studies per application area RQ2
T4 Number of studies per type of CDSS RQ3
T5 Number of studies per quality attributes RQ4
T6 Number of studies per data source RQ5
T7 Discussion about intervention of cloud computing on CDSS
implementations in terms of main outcomes detected in the
literature
RQ6
Data
synthesis

16. SLR Results
16
12
8
4 4 4
1 1 1

17. SLR Results (ii)
17

18. SLR Results (iii)
18

19. SLR Results (iv)
19

20. SLR Results (v)
20
Cloud-based CDSS Architectural Drivers
Security
CompatibilityPerformance

21. SLR Results (vi)
21

22. SLR Results (vii)
22

23. SLR Results (viii)
23

24. SLR Results (ix)
24
Intervention of Cloud Computing on CDSS
Cost-efficiency
Better patient outcomes
“Unlimited resources”
Clinical data quality
Researching knowledge

25. Final Remarks
• Healthcare organizations are adopting cloud-based CDSS
to provide enhanced patient care outcomes.
• On-premise environments could allow similar advantages
but the effort to achieve that in these environments is
larger.
• Main challenges in cloud-based CDSS: Performance,
Compatibility and Reliability.
• Main concerns in cloud-based CDSS: Security and
Privacy. These concerns may not be being well validated
in practice.
• There is a lack of formalism regarding to software
engineering practice.
25

26. Final Remarks (ii)
• There is a lack of rigor using the term “Cloud”
26
On-demand self-service
Broad network access
Resource pooling
Rapid elasticity
Measured service
• Not all primary
characteristics of Cloud
Computing are being strictly
implemented
• Web-based, SOA-based or
ROA-based proposals are
presented as cloud-based.

27. Final Remarks (iii)
27
Common Cloud-based CDSS Architectural Approach
(Oh et al., 2015)
3 Common
Components:
• Knowledge database
• Inference Engine
• Interface Server

28. Future Work
CDSS Triage as a Service
28
Common CDSS
Components

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36. Architectural approaches for implementing
Clinical Decision Support Systems in Cloud: A
Systematic Review
Iván Cabezas, Luis Tabares and Jhonatan Hernández
imcabezas@usbcali.edu.co
June 27, 2016
International Workshop on Cloud Connected Health, CCH 2016, Washington D.C .
36