Report: Clinical Decision Support Applications and Tools Analysis

This report provides a comprehensive analysis of Clinical Decision Support Systems (CDSS), exploring their conception, types, components, benefits, and applications within the healthcare industry. The report delves into knowledge-based and non-knowledge-based support systems, detailing components like data repositories, rules engines, and interfaces. It highlights the benefits of CDSS, including improved patient safety and reduced medication errors, and discusses relevant standards and regulations. Furthermore, the report examines various applications of CDSS, such as treatment decisions and ontology-driven support systems for chronic diseases, concluding with the importance of CDSS in enhancing healthcare decision-making and patient care. The report references several research papers and articles to support its findings.

CLINICAL DECISION SUPPORT APPLICATIONS AND TOOLS
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Abstract
The report is useful for analyzing the clinical decision support system conception. It is very
much important for detecting different types of support systems, which are important for
boosting the development. The report pays attention in evaluating the clinical decision support
system conceptions. Moreover, it properly detects a number of elements of the system and
different applications of the system.
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Table of Contents
Introduction......................................................................................................................................4
Support system conceptions for improving clinical decisions........................................................4
Types of systems for supporting clinical decisions.........................................................................5
Clinical decision support system components.................................................................................5
Clinical decision supporting tool benefits.......................................................................................6
Standards and regulations................................................................................................................6
Applications.....................................................................................................................................7
Conclusion.......................................................................................................................................7
References........................................................................................................................................8
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Introduction
Clinical decision support system is regarded as one of the effective applications, which helps in
analyzing data. In addition, it is very much important for aiding the healthcare service providers
in taking wise decisions, which are necessary for developing the care of the patients. It is
considered as one of the important variations that is very much important for promoting the
process of supporting the management of business. The report pays attention in evaluating the
clinical decision support system conceptions. Moreover, it has aimed at identifying all the
benefits, which are important for developing the tools, which are necessary for improving the
clinical support system. Moreover, it is important for improving necessary regulations and
implementation standards.
Support system conceptions for improving clinical decisions
Clinical decision support system is an important process, which is important for providing care
that is person centered (Yauet al. 2019, p. e001093). In addition, it is required for providing
important information and knowledge on different staffs, nurses, professionals and patients. This
decision support system is widely used by the clinicians in order to detect the diseases and
provide proper care. This system is important for developing the patient safety. In addition, it is
necessary for getting rid of unnecessary complications. It has specifically paid attention in
treating their complications caused by different diseases. This particular support system for
making decision has developed over the course of last few years. Health Information Technology
for Economic and Clinical Health Act has been enacted for engaging the service providers
properly by applying a number of technologies. Accurate information and time are provided by
CDSS tool. All these information are user or client specific. It is responsible for improving the
decision making process.
All the tools of clinical decision support system have been designed properly in order for sifting
a large portion of information, which are quite difficult to access. These tools (Whitehead et al.
2019, p. 33) aid all the providers of services. These are responsible for detecting potential
interaction problems of medication.
Clinical decision support system tools are used in keeping health records electronically. It is
useful for workflow streamlining and development of the available data sets.
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Types of systems for supporting clinical decisions
Clinical decision support system can be categorized into two types. These are support systems,
which are non-knowledge based, and the support systems, which are based on knowledge.
Non-knowledge based and knowledge based support system
Specific regulations and rules are used in knowledge-based support system, which are useful for
evaluating the patient information. Inference engine helps in applying them. It is useful for
displaying all the obtained results effectively (Kamssuet al. 2018, p. 20). CDSSes, which are
based on knowledge, contain information regarding respiratory, inference engine and mechanism
for communication. These are necessary for developing the operations properly.
An example can be used for illustrating it. CDSS that is based on knowledge is applied properly
in order to evaluate the interactions of prospective drugs. In such cases, alarms are also issued. In
this case drug B is applied. Machine learning process is used for comprehending clinical decision
support system that is not based on knowledge. The example that can be stated here is the neutral
network which is useful for performing particular tasks by applying a number of examples.
Patient dashboards and data reports
It totally depends upon particular rules, which are important for improving the dashboard
management. In addition, it is useful for developing the data sets of the patients by using all the
information, whichuse the rules properly.
Reminders and alerts
Reminders and alerts are important for raising alarms, which are necessary for patient centered
information. Detailed messages, emails and sms can be used as alerts (Amland and Hahn-Cover,
2016, p. 107).
Clinical guidelines
All the clinical guidelines depend entirely on particular rules, which have the power of offering
effective patient care.
Clinical decision support system components
It is very much important to give the necessary system for support, which is important for
making decisions. It helps greatly in storing effective knowledge and information by collecting
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important information properly (Liang et al. 2017, p. 33). These are regarded as effective
resources that helps in processing a huge amount of information.
There are three important components of a clinical decision support system. These consist of
data repository, rules engine and interface.
Data repository
Data repository is very much necessary for storing the information properly. The structured data
is important for providing drug related information.
Rules engine
CDSS’s important component is rules engine. It helps in developing and managing information
properly. It is very much useful for getting a deep insight of different clinical contexts. The
testing evidences consist of pregnancy knowledge tests, reference materials and additional tests.
Figure 1: Core components of clinical decision support system
(Source: Liang et al. 2017, p. 33)
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Clinical decision supporting tool benefits
The clinical decision-supporting tool has a number of benefits. It is very much important for
providing the recommendations, which are based on evidences and reviewed by peers
(Hillebrandet al. 2015, p. S17). These are useful for analyzing patient related EHR information.
Clinical decision supporting tool has different benefits. These include
● Improving patient safety
● Decreasing risks associated with medication error
● Getting rid of misdiagnosis
● Assessment of all important information of a particular area
Standards and regulations
Standards
● Standards of mapping and representing data
● Improving knowledge resources which are important for delivering effective clinical
decision support system
● Standards of representing knowledge properly
Regulations
There are different regulations that are related to implementing a number of supporting tools for
making important clinical decisions. Different safety issues are identified properly by applying
Food and drug safety act, 2012 (Ali et al. 2016, p. 94). It is useful for replacing them properly.
Different technologies related to health information are adopted by American recovery and
reinvestment act, 2009. This act is very much important for helping all the hospitals in applying a
physician order entry that is computerized. It is also important for keeping all the medical
records properly and helps to enhance the quality.
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Applications
Treatments for decision
The clinical decision support system is important for developing the decisions for using
treatments for particular patients. It is responsible for decreasing the usage of unnecessary tests
(Velez et al. 2019, p. 33).
Ontology driven decision support system
This is very much important for evaluating different risks associated with some diseases, which
are chronic. It is useful for inventing new processes of treatment and using them in improving
the health of the patients.
Conclusion
By analyzing different clinical decision support system types and components, it can be
concluded that it is very much important to apply specific information and technologies, which
are important for improving the clinical decision making system. It is important for improving
the process of making clinical decisions. Different medical records and information related to
healthcare are used effectively in making important recommendations. These recommendations
are important for developing the management of different professional of healthcare. It is
considered as the computer application that is useful for helping the healthcare professionals in
developing the process of decision-making. Clinical decision support can be used effectively in
improving the delivery system of healthcare.
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References
Ali, S.M., Giordano, R., Lakhani, S. and Walker, D.M., 2016. A review of randomized
controlled trials of medical record powered clinical decision support system to improve quality
of diabetes care. International journal of medical informatics, 87, pp.91-100.
Amland, R.C. and Hahn-Cover, K.E., 2016.Clinical decision support for early recognition of
sepsis. American Journal of Medical Quality, 31(2), pp.103-110.
Hillebrand, K., Leinum, C.J., Desai, S., Pettit, N.N. and Fuller, P.D., 2015. Residency
application screening tools: a survey of academic medical centers. American Journal of Health-
System Pharmacy, 72(Supplement_1), pp.S16-S19.
Kamssu, A.J., Siekpe, J.S. and Adeyemo, M.O., 2018. DECISION SUPPORT TOOLS: A
COMPARATIVE ANALYSIS BETWEEN TWO HOSPITALS. Journal of Research in Business
Information Systems, p.20.
Liang, L., Safi, J.A. and Gagliardi, A., 2017. Number and type of guideline implementation tools
varies by guideline, clinical condition.
Velez, C., Tschampel, T., Apostolova, E. and Boris, A., Computer Technology Associates Inc,
2019. Disease specific ontology-guided rule engine and machine learning for enhanced critical
care decision support. U.S. Patent Application 15/998,436.
Whitehead, N.S., Williams, L., Meleth, S., Kennedy, S., Ubaka-Blackmoore, N., Kanter, M.,
O'Leary, K.J., Classen, D., Jackson, B., Murphy, D.R. and Nichols, J., 2019. The Effect of
Laboratory Test–Based Clinical Decision Support Tools on Medication Errors and Adverse Drug
Events: A Laboratory Medicine Best Practices Systematic Review. The Journal of Applied
Laboratory Medicine, pp.jalm-2018.
Yau, M., Timmerman, V., Zwarenstein, M., Mayers, P., Cornick, R.V., Bateman, E. and Fairall,
L., 2019.e-PC101: an electronic clinical decision support tool developed in South Africa for
primary care in low-income and middle-income countries. BMJ Global Health, 3(Suppl 5),
p.e001093.
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