A Clinical Decision Support System (CDSS) is a health information technology system that is designed to assist physicians and other health professionals with clinical decision-making tasks. A working definition has been proposed by Robert Hayward of the Centre for Health Evidence; "Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care".
The CDSS features associated with success include the following:
·
the CDSS is integrated
into the clinical workflow rather than as a separate log-in or screen.
·
the CDSS is electronic
rather than paper-based templates.
·
the CDSS provides
decision support at the time and location of care rather than prior to or after
the patient encounter.
·
the CDSS provides
(active voice) recommendations for care, not just assessments.
Purpose
The main purpose of modern CDSS
is to assist clinicians at the point of care.[6] This means that a clinician would interact with a CDSS to help
determine diagnosis, analysis, etc. of patient data. Typically the CDSS would make
suggestions of outputs or a set of outputs for the clinician to look through
and the clinician officially picks useful information and removes erroneous
CDSS suggestions.[5]
There are two main types of CDSS:
·
Knowledge-Based
·
NonKnowledge-Based
Knowledge-based
CDSS
Most CDSS consist of three
parts, the knowledge base, inference
engine, and mechanism to communicate. The knowledge base contains the rules and
associations of compiled data which most often take the form of IF-THEN rules.
If this was a system for determining drug interactions, then a rule might be
that IF drug X is taken AND drug Y is taken THEN alert user. Using another
interface, an advanced user could edit the knowledge base to keep it up to date
with new drugs. The inference engine combines the rules from the knowledge base
with the patient’s data. The communication mechanism will allow the system to
show the results to the user as well as have input into the system.
Non-knowledge-based
CDSS
CDSS’s that do not use a
knowledge base use a form of artificial
intelligence called machine learning, which allow computers to
learn from past experiences and/or find patterns in clinical data. Two types of
non-knowledge-based systems are artificial neural networks and genetic
algorithms.
Artificial neural networks or more generally neural networks use nodes and weighted connections between
them to analyze the patterns found in the patient data to derive the
associations between the symptoms and a diagnosis. This eliminates the need for
writing rules and for expert input. However since the system cannot explain the
reason it uses the data the way it does, most clinicians don’t use them for
reliability and accountability reasons.
Clinical
Challenges
Much effort has been put forth
by medical institutions and software companies to produce viable CDSSs to cover
all aspects of clinical tasks. However, with the complexity of clinical
workflows and the demands on staff time high, care must be taken by the
institution deploying the support system to ensure that the system becomes a
fluid and integral part of the workflow. To this end CDSSs have met with
varying amounts of success, while others suffer from common problems preventing
or reducing successful adoption and acceptance.
Electronic
Health Records and CDSS
Implementing Electronic Health
Records (EHR) was always going to be an inevitable challenge. The reasons
behind this challenge is that it is a relatively uncharted area as it is
something that has never been done before, thus there is; and will be many
issues and complications during the implementation phase of an EHR. This can be
seen throughout the numerous studies that have been undertaken. Challenges in implementing
electronic health records (EHRs) have received some attention, but less is
known about the process of transitioning from legacy EHRs to newer systems.With
all of this said, electronic health records are the way of the future for
healthcare industry. It is a way to capture and utilise real-time data to
provide high-quality patient care, ensuring efficiency and effective use of
time and resources. By incorporating EHR and CDSS it has the potential to
change the way medicine has been taught and practiced.As it is said that, “the
highest level of the EHR is a CDSS”.
Benefits
of CDSS and EHR
There has always been errors
that occur within the healthcare industry, thus trying to minimise them as much
as possible in order to provide quality patient care. Four areas that can be
addressed with the implementation of CDSS and Electronic Health Records (EHRs),
are:
CDSS will be most beneficial
once the healthcare facility is 100% electronic thus simplifying the number of
modifications that have to occur to ensure that all the systems are up to date.
However, the measurable benefits of clinical decision support systems on
physician performance and patient outcomes remain the subject of ongoing
research. Systematic reviews of the literature have yielded differing
correlations to date.
Barriers
to CDSS and EHR
Service oriented architecture has been proposed as a way to address some
of the barriers.[18] The main barriers associated with CDSS and
EHRs consist of feasibility (cost), poor usability/ integration, uniformity,
clinician non-acceptance, alert desensitisation, as well as the key fields of
data entry that need to be addressed when implementing a CDSS to avoid
potential adverse events from occurring. These include:
→ Correct data is being used
→ All the data has been implemented
→ Current best practice
→ Evidence based
→ All the data has been implemented
→ Current best practice
→ Evidence based
The main areas of concern with
moving into a fully integrated EHR system are:
1. Privacy
2. Confidentiality
3. User-friendliness
4. Document accuracy and completeness
5. Integration
6. Uniformity
7. Acceptance
8. Alert desensitisation
2. Confidentiality
3. User-friendliness
4. Document accuracy and completeness
5. Integration
6. Uniformity
7. Acceptance
8. Alert desensitisation
