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Annotated Bibliography
Barriers to Widespread Adoption of Clinical Decision Support Systems
Devaraj, S., Sharma, S. K., Fausto, D. J., Viernes, S., & Kharrazi, H. (2014). Barriers and
facilitators to clinical decision support systems adoption: A systematic review.*Journal of
Business Administration Research,*3(2), 36.
https://www.researchgate.net/publication/274656251_Barriers_and_Facilitators_to_Clini
cal_Decision_Support_Systems_Adoption_A_Systematic_Review#:~:text=Some%20of
%20the%20important%20barriers,of%20information%2C%20lack%20of%20agreement
This research sought to identify possible barriers to using a computer-based Clinical
Decision Support System to enhance clinical practice. The study is a meta-analysis. The
authors cite user or physician attitudes toward the system, disagreement with the design,
less reliability or authenticity of the information, murky workflow issues, reluctance to
use the system before patients, lack of content or system knowledge, and economic and
time limitations as just a few of the significant barriers to CDSS use. The article was
chosen because it outlines the most common barriers to implementing CDSS.
The author's strong academic and research background adds to the resource's credibility.
Srikant Devaraj, Ph.D., is a research economist and assistant professor at Ball State
University's Center for Business and Economic Research (CBER). In 2016, Devaraj
graduated with a Ph.D. in economics from Indiana University, specializing in health and
health informatics economics. Devaraj has written for several scholarly publications,
including Decision Making and managerial publications, Medicine and social science
journals. Sushil Sharma is a professor of information systems (IS) and an associate dean
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at Ball State University's Miller College of Business. The fact that Dr. Sharma has two
Ph.D. degrees gives him a special distinction. He has more than ten years of experience in
administrative leadership and more than 25 years of experience in higher education. I
would cite this quote: "Despite compelling data supporting CDSSs' efficacy in medicine,
they are seldom ever employed in actual clinical settings. Finding instruments to aid its
wider distribution may be made easier by comprehending the causes of this
phenomenon."
Roshanov, P. S., Fernandes, N., Wilczynski, J. M., Hemens, B. J., You, J. J., Handler, S. M., ...
& Haynes, R. B. (2013). Features of effective computerised clinical decision support
systems: meta-regression of 162 randomised trials. Bmj, 346.
https://www.bmj.com/content/346/bmj.f657.short
The objective of this article was to determine what makes computerized clinical decision
support systems ineffective or beneficial in terms of bettering patient outcomes or
delivery of care. The study uncovered several variables that could help to partly explain
why specific systems don't work while others don't. Compared to alternative methods of
providing guidance, presenting decision support through order entry systems or electronic
charting is related to the failure. According to the authors, systems that required
practitioners to justify when defying guidance had higher success rates than those that did
not. As part of the final paper, the quote to include is "Systems that gave patients and
practitioners guidance simultaneously had superior success rates." Finally, the majority of
systems had their own developers review them, and these evaluations were more likely to
be beneficial than those carried out by a third party. Less effective systems were those
that offered guidance in electronic charting or order entry system interfaces. These results
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held up well when tested using several statistical techniques, internal validation, and after
adjusting for other potentially significant variables. This material was selected because it
provides insights into what an effective CDSS should have.
This journal article has authors with extensive knowledge as it pertains to health care and
information systems. Robert Brian Haynes is an internist and a clinical epidemiologist,
and his areas of expertise are health services research in general, clinical informatics, and
clinical care of diabetics. His research involves the application, distribution, synthesis,
summary, evaluation, and retrieval of the evidence, and it is situated at the intersection of
health care research and clinical practice. General internist Dr. John You works at
Hamilton Health Sciences hospital. At McMaster University, he holds the position of
Joint Member in the Department of Health Research Methods, Impact, and evidence. He
is an Associate Professor in the Department of Medicine. His primary academic and
clinical interest is how to provide critically sick hospitalized patients and their families
with better end-of-life care, decision making, and communication. "The robust and
significant size and correlation with failure for advice given in electronic charting or
order entry systems were maintained in sensitivity studies and internal validation.
Although this conclusion may appear counterintuitive, it is conceivable that when several
warnings are offered at once, a single notification loses its power to alter provider
behavior." This is an insightful quote to add to the final research paper.
Yang, Q., Steinfeld, A., & Zimmerman, J. (2019, May). Unremarkable ai: Fitting intelligent
decision support into critical, clinical decision-making processes. In*Proceedings of the
2019 CHI Conference on Human Factors in Computing Systems*(pp. 1-11).
https://arxiv.org/pdf/1904.09612.pdf
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Clinical decision support technologies (DST) promise to enhance healthcare outcomes by
providing data-driven insights. The authors divulge that most DSTs have failed in
practice while being successful in lab conditions. The poor contextual fit was identified
as the root problem through empirical investigation. An entirely new kind of DST is
designed and field-tested in this article. It automatically creates presentations with subtle
machine prognostics included for clinician decision meetings. The article hypothesized
that for doctors to evaluate the DST information architecture and understand what an
actual prediction would look like, they would need to examine one of their patients' data.
The statement "Early DST prototypes would need real patient data to evaluate the context
of their interactions and their effects on healthcare. Due to hospital policies, ethics and
rules, this is not feasible in urgent clinical situations" would be included in the final draft.
The researchers involved in coming up with this article add to the credibility in that Qian
Yang is an associate professor of Information systems specializing in Human-computer
interactions and designs at Cornell University. Aaron Steinfeld is a Carnegie Mellon
University professor specializing in advanced transportation and human-robot interaction.
Lastly, John Zimmerman is a professor at the same university, specializing in Human-
computer interaction. From their academic background and experience, publications from
these authors are reliable in that the topic is about decision support software and the
authors have extensive knowledge in software design.
Liberati, E. G., Ruggiero, F., Galuppo, L., Gorli, M., González-Lorenzo, M., Maraldi, M., ... &
Moja, L. (2017). What hinders the uptake of computerized decision support systems in
hospitals? A qualitative study and framework for implementation.*Implementation
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Science,*12(1), 1-13.
https://implementationscience.biomedcentral.com/articles/10.1186/s13012-017-0644-2
This research looks at how various health professionals at hospitals at various levels of
CDSS adoption view the hurdles and enablers to adopting an evidence-based CDSS. The
authors state that, when determining whether a hospital is ready to adopt CDSSs, factors
like the organizational culture that values transparency and accountability, the
effectiveness of inter-disciplinary collaborations, and clinicians' attitudes toward
scientific guidelines and evidence must all be taken into account. The lack of integration
of CDSSs into routine practice may be the source of the remaining adoption hurdles,
indicating that efforts should be focused on enhancing the system's flexibility and
flexibility as well as its capacity to adapt to the requirements of various workflows and
users. This article is relevant in that it will help in drafting recommendations.
One of the authors of the journal article is Elisa Liberati. Hospital ethnography is Elisa
Liberati's area of expertise as a postdoctoral research associate. Her study examines the
human, social and organizational elements of quality improvement in healthcare. She
received a Ph.D. in April 2016 from the Università Cattolica di Milano in Italy. She has
an MA in Organizational and Work Psychology.
Additionally, she works as a research associate at Sidney Sussex College. Francesca
Ruggiero is an Associate Professor of Anatomic Pathology. He also serves as the director
of the Gastrointestinal Pathology Unit at Penn State University. The two have published
many peer-reviewed journal articles and continue to do research work in their fields.
They are relevant because they have a background in healthcare and, therefore, can
research on barriers to implementing CDSSs.
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Laka, M., Milazzo, A., & Merlin, T. (2021). Factors that impact the adoption of clinical decision
support systems (cdss) for antibiotic management.*International journal of environmental
research and public health,*18(4), 1901. file:///C:/Users/user/Downloads/ijerph-18-
01901-v2%20(1).pdf
This research assessed the environment-specific and personal variables influencing
physicians' perception of using clinical decision support systems (CDSS) for antibiotic
treatment. The study (a cross-sectional online poll) examined how doctors felt about
using CDSS to monitor antibiotic use. Clinicians' perceptions of the adoption of CDSS
were significantly influenced by care context, CDSS usage, and clinical experience.
Users of CDSS were less likely to lack trust in the system and see it as a threat to their
professional autonomy than nonusers. Contrarily, experienced doctors were more likely
to be so out of concern about jeopardizing their clinical judgment. Patient preferences and
time restraints were more often seen by primary care providers as impediments to CDSS
use for antibiotic treatment. The material is relevant because it provides a distinct insight
into the CDSS deployment landscape by highlighting the many individuals,
organizational, and system-level elements that affect user acceptance. It also provides
information in a unique context which is antibiotic usage. The statement to quote is
"Understanding the variations in CDSS adoption for antibiotic treatment across various
doctors may be aided by an understanding of context variables and the person."
Dr. Mah Laka is one of the authors of the material. Dr. Mah Laka is a public health
researcher in his early career. She is a clinical epidemiologist interested in evaluating
health technologies and digital health. Mah Laka has a background in health science
(bioinformatics) with first-class honors and most recently completed a Ph.D. at the
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University of Adelaide's School of Public Health. In her Ph.D. research, she evaluated the
sustainability and viability of electronic decision support systems for evidence-based
decision-making. She also conducts health technology assessments. Infectious disease
epidemiologist Adriana Milazzo is a senior professor and part of the School of Public
Health's research group on infectious disease epidemiology, ecosystem health, and
climate change. After spending more than 20 years as a clinical epidemiologist and
methodologist in this still young discipline, Professor Merlin became the first Professor
of Health Technology Assessment in Australia. She has produced over 150 health
technology evaluation reports, clinical practice recommendations, systematic literature
reviews, and published methodological work. She is a University of Adelaide's Health
Technology Assessment co-founder and director.
Shi, Y., Amill-Rosario, A., Rudin, R. S., Fischer, S. H., Shekelle, P., Scanlon, D. P., & Damberg,
C. L. (2021). Barriers to using clinical decision support in ambulatory care: Do clinics in
health systems fare better?.*Journal of the American Medical Informatics
Association,*28(8), 1667-1675. https://europepmc.org/article/MED/33895828
This research provides a benchmark for future policies and empirical research on this
subject by quantifying the use of clinical decision support (CDS) and the specific barriers
mentioned by ambulatory clinics. It also examines whether CDS barriers and utilization
varied depending on the clinics' affiliation with health systems. Clinics inside health
systems used more CDS tools than clinics outside of systems but also reported higher
user acceptance and resource hurdles. Clinics connected to health systems saw an
increase in obstacles to process reform. Clinics in rural areas were more likely to mention
training obstacles. While health systems are successful in promoting CDS technologies,
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their connected ambulatory clinics may still need further help to get over obstacles,
particularly the need to reorganize workflow. Rural clinics could need extra funding for
training. This material is relevant in that it outlines barriers and insights on the usage of
CDSS in a unique healthcare context, ambulatory services.
Shira Fischer is among the several authors of the material. She is a professor at Harvard
medical school. He specializes in medical bioinformatics. He has published over ten other
journal articles related to medical bioinformatics. Dr. Rudin's primary areas of interest
have been medical imaging research and education. He has assisted in the professional
development of many people who have had successful careers in industrial,
governmental, academic, and clinical medical physics. Cheryl Damberg, on the other
hand, serves as the head of the RAND Center of Excellence on Health System
Performance and RAND Corporation's lead senior economist. Her research examines
how providers reinvent healthcare delivery in response to new payment structures and
more responsibility, as well as the effect of efforts to generate quality and cost
improvements in healthcare. She has worked to improve the design of value-based
payment models, emphasizing reducing inequalities via better assessment that considers
social risk factors and develops health equality indicators.
Klarenbeek, S. E., Schuurbiers-Siebers, O. C., van den Heuvel, M. M., Prokop, M., & Tummers,
M. (2020). Barriers and facilitators for implementation of a computerized clinical
decision support system in lung cancer multidisciplinary team meetings—a qualitative
assessment.*Biology,*10(1), 9. file:///C:/Users/user/Downloads/biology-10-00009-v2.pdf
The development of oncological computerized clinical decision support systems
(CCDSSs) will streamline the workflow of multidisciplinary team meetings (MDTMs).
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The objective of this qualitative evaluation was to identify implementation facilitators
and challenges and give actionable results for an implementation plan to deploy these
systems in MDTMs effectively. The system's output was incomplete or unreliable and
wasn't sufficiently adaptable to local and contextual demands, which were the main
adoption hurdles. The quote to carry to the final draft will be "Understanding the enablers
and obstacles to effective adoption of CCDSSs in cancer treatment appears very
important given the increased interest in these systems." The article is relevant because it
discusses barriers to implementing CDSSs in cancer of the lung multidisciplinary team
meetings.
Dr. Marcia Tummers, one of the authors, is employed with the Radboud University
Medical Center's Department for Health Evidence. Dr. Schuurbiers-Siebers also works in
the same institution. Most of the other authors have at least a Ph.D. in medical-related
areas. They have, in one way or another, worked or researched oncology or clinical
decision support systems related to oncology.
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I.
(2020). An overview of clinical decision support systems: benefits, risks, and strategies
for success.*NPJ digital medicine,*3(1), 1-10. https://www.nature.com/articles/s41746-
020-0221-y.pdf?origin=ppub
A clinical decision support system (CDSS) aims to enhance healthcare delivery by
incorporating specific patient data, clinical knowledge, and other health information into
medical choices. The authors say that in a standard CDSS, patient-specific
recommendations or evaluations are offered to the clinician for an alternative after the
features of each patient are matched to a computerized clinical knowledge base. This
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software is intended to be direct assistance to clinical decision-making. Today's CDSSs
are typically employed at the point of care, allowing the physician to combine their
expertise with data or recommendations from the CDSS. "People must be on the lookout
for CDSS's possible drawbacks, which might vary from the system squandering money to
the system lowering the standard of patient care and wearing out its users." That would
be the part to quote in the final draft. Maintenance, implementation, and construction of
CDSS must be done with thoughtful planning and extra care. This material would help
write a good introduction. It consists of the definition of a clinical decision support
system and its application.
Reed Sutton, one of the authors, is a researcher at the University of Alberta's Division of
Gastroenterology. His area of Ph.D. study was medical artificial intelligence. His study
used machine learning (ML) to improve the interpretation of abdominal CTE images and
healthcare usage. General dentistry is Dr. Pincock's area of expertise. He is based in
Orem, Utah. At the University of Alberta. Dr. Karen I. Kroeker teaches medical students
as an associate professor. She worked in a medical research laboratory for two years after
completing her BSc (Zoology, Microbial, Molecular, and Cellular Biology) at the
University of Calgary. Dr. Kroeker joined the University of Alberta's Faculty of
Medicine in 2010. At the University of Alberta Hospital, she is a member of the Active
Medical Staff. She manages a pediatric IBD transition clinic and has a sizable practice
with inflammatory bowel disease patients. These authors have extensive medical
knowledge. Their academic and research excellence makes their work credible.
Alighanbari, Z. A. H. R. A., Alizadeh, A., & Khorrami, F. A. R. I. D. (2017). Barriers and
strategies in Implementing Clinical Decision Support System in Hospitals: A Case Study
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in Iran.*Hormozgan Medical Journal,*21(2), 137-145.
file:///C:/Users/user/Downloads/90920170208.pdf
According to the participants, the biggest obstacles to adopting CDSS were divided into
six areas. These include human-resource-related and physical constraints and
environmental, legal, technological, and monetary constraints. The authors say that since
people posed the biggest challenge to deploying CDSS in this hospital, the hospital may
use the system better by using the obstacles presented in the five categories. The
statements "These obstacles include educating human resources before they start
working, including them in the CDSS implementation process, and using evidence-based
scientific databases in CDSS while addressing systemic impediments." Would be quoted
in the final draft. This material is relevant in that it will be able to compare barriers to
implementing CDSS in a different country, Iran.
Farid Khorrami holds a Ph.D. in health information management. He currently works at
Hormozgan University teaching related subjects. He has published tens of credible
journal articles related to health and technology. The other two authors possess a Ph.D. in
nursing. They have extensive knowledge in health care and research.
Wasylewicz, A. T. M., & Scheepers-Hoeks, A. M. J. W. (2019). Clinical decision support
systems.*Fundamentals of clinical data science, 153-169.
https://www.ncbi.nlm.nih.gov/books/NBK543516/
The CDSS must be adjusted to meet end-user preferences to achieve high adoption and
efficient usage. Then and only then can alert weariness be reduced. The authors agree that
the second pillar of the roadmap is the best information that can be found at the time.
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Only CDSS has a promise since it offers up-to-date and supplementary information. The
CDSS being utilized significantly impacts how well clinical rules adapt and work. The
quote in the final draft of this article is, "A thorough user requirement documentation and
user requirement specification (URS) is necessary for implementing, selecting, or
tendering a new CDSS. Structured evaluation and evaluation of clinical rules is essential
for reducing alert fatigue in active CDSS." Many articles have been written on validating
these clinical criteria to make the CDSS's recommendations as clinically relevant as
possible. This material will aid in drafting recommendations.
The authors of the material are reliable in that, Wasylewicz works at Catharina Hospital
in Eindhoven, the Netherlands, in the Department of Clinical Pharmacy. Scheepers-
Hoeks is employed at the Maastricht University Medical Center's Department of Clinical
Pharmacy and Toxicology in the Netherlands. They both possess PhDs. Their years of
experience, supplemented with many peer-reviewed articles published, make their work
credible.
