A clinical decision support system (CDSS) is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support (CDS), that is, assistance 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". CDSSs constitute a major topic in artificial intelligence in medicine.
Characteristics
A clinical decision support system has been defined as an "active knowledge systems, which use two or more items of patient data to generate case-specific advice." This implies that a CDSS is simply a decision support system that is focused on using knowledge management in such a way so as to achieve clinical advice for patient care based on multiple items of patient data.
Purpose
The main purpose of modern CDSS is to assist clinicians at the point of care. This means that clinicians interact with a CDSS to help to analyse, and reach a diagnosis based on, patient data.
In the early days, CDSSs were conceived of as being used to
literally make decisions for the clinician. The clinician would input
the information and wait for the CDSS to output the "right" choice and
the clinician would simply act on that output. However, the modern
methodology of using CDSSs to assist means that the clinician interacts
with the CDSS, utilizing both their own knowledge and the CDSS, to make a
better analysis of the patient's data than either human or CDSS could
make on their own. Typically, a CDSS makes suggestions for the clinician
to look through, and the clinician is expected to pick out useful
information from the presented results and discount erroneous CDSS
suggestions.
The two main types of CDSS are knowledge-based and non-knowledge-based :
An example of how a clinical decision support system might be
used by a clinician is a diagnosis decision support system. A DDSS
requests some of the patients data and in response, proposes a set of
appropriate diagnoses. The physician then takes the output of the DDSS
and determines which diagnoses might be relevant and which are not, and if necessary orders further tests to narrow down the diagnosis.
Another example of a CDSS would be a case-based reasoning (CBR) system.
A CBR system might use previous case data to help determine the
appropriate amount of beams and the optimal beam angles for use in radiotherapy
for brain cancer patients; medical physicists and oncologists would
then review the recommended treatment plan to determine its viability.
Another important classification of a CDSS is based on the timing
of its use. Physicians use these systems at point of care to help them
as they are dealing with a patient, with the timing of use being either
pre-diagnosis, during diagnosis, or post diagnosis.
Pre-diagnosis CDSS systems are used to help the physician prepare the
diagnoses. CDSS used during diagnosis help review and filter the
physician's preliminary diagnostic choices to improve their final
results. Post-diagnosis CDSS systems are used to mine data to derive
connections between patients and their past medical history and clinical
research to predict future events. As of 2012 it has been claimed that decision support will begin to replace clinicians in common tasks in the future.
Another approach, used by the National Health Service
in England, is to use a DDSS (either, in the past, operated by the
patient, or, today, by a phone operative who is not medically-trained)
to triage medical conditions out of hours by suggesting a suitable next step to the patient (e.g. call an ambulance, or see a general practitioner
on the next working day). The suggestion, which may be disregarded by
either the patient or the phone operative if common sense or caution
suggests otherwise, is based on the known information and an implicit
conclusion about what the worst-case diagnosis is likely to be;
it is not always revealed to the patient, because it might well be
incorrect and is not based on a medically-trained person's opinion - it
is only used for initial triage purposes.
Knowledge-based CDSS
Most CDSSs consist of three parts: the knowledge base, an inference engine, and a 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 allows the system to show the results
to the user as well as have input into the system.
An expression language such as GELLO or CQL (Clinical Quality
Language) is needed for expressing knowledge artifacts in a computable
manner. For example: if a patient has diabetes mellitus, and if the last
hemoglobin A1c test result was less than 7%, recommend re-testing if it
has been over 6 months, but if the last test result was greater than or
equal to 7%, then recommend re-testing if it has been over 3 months.
The current focus of the HL7 CDS WG is to build on the Clinical Quality Language (CQL). CMS has announced that it plans to use CQL for the specification of eCQMs (https://ecqi.healthit.gov/cql).
Non-knowledge-based CDSS
CDSSs which 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. This eliminates the need for writing rules
and for expert input. However, since systems based on machine learning
cannot explain the reasons for their conclusions, most clinicians
do not use them directly for diagnoses, for reliability and
accountability reasons. Nevertheless, they can be useful as post-diagnostic systems, for suggesting patterns for clinicians to look into in more depth.
As of 2012, three types of non-knowledge-based systems are support-vector machines, artificial neural networks and genetic algorithms.
- Artificial neural networks use nodes and weighted connections between them to analyse the patterns found in patient data to derive associations between symptoms and a diagnosis.
- Genetic algorithms are based on simplified evolutionary processes using directed selection to achieve optimal CDSS results. The selection algorithms evaluate components of random sets of solutions to a problem. The solutions that come out on top are then recombined and mutated and run through the process again. This happens over and over until the proper solution is discovered. They are functionally similar to neural networks in that they are also "black boxes" that attempt to derive knowledge from patient data.
- Non-knowledge-based networks often focus on a narrow list of symptoms, such as symptoms for a single disease, as opposed to the knowledge based approach which cover the diagnosis of many different diseases.
Regulations
United States
With the enactment of the American Recovery and Reinvestment Act of 2009 (ARRA), there is a push for widespread adoption of health information technology
through the Health Information Technology for Economic and Clinical
Health Act (HITECH). Through these initiatives, more hospitals and
clinics are integrating electronic medical records (EMRs) and computerized physician order entry (CPOE) within their health information processing and storage. Consequently, the Institute of Medicine
(IOM) promoted usage of health information technology including
clinical decision support systems to advance quality of patient care. The IOM had published a report in 1999, To Err is Human,
which focused on the patient safety crisis in the United States,
pointing to the incredibly high number of deaths. This statistic
attracted great attention to the quality of patient care.
With the enactment of the HITECH Act included in the ARRA,
encouraging the adoption of health IT, more detailed case laws for CDSS
and EMRs are still being defined by the Office of National Coordinator for Health Information Technology (ONC) and approved by Department of Health and Human Services (HHS). A definition of "Meaningful use" is yet to be published.
Despite the absence of laws, the CDSS vendors would almost
certainly be viewed as having a legal duty of care to both the patients
who may adversely be affected due to CDSS usage and the clinicians who
may use the technology for patient care. However, duties of care legal regulations are not explicitly defined yet.
With recent effective legislations related to performance shift payment incentives, CDSS are becoming more attractive.
Effectiveness
The
evidence of the effectiveness of CDSS is mixed. There are certain
disease entities, which benefit more from CDSS than other disease
entities. A 2018 systematic review identified six medical conditions, in
which CDSS
improved patient outcomes in hospital settings, including: blood glucose
management, blood transfusion management, physiologic deterioration
prevention, pressure ulcer prevention, acute kidney injury prevention,
and venous thromboembolism prophylaxis.
A 2014 systematic review did not find a benefit in terms of risk of death when the CDSS was combined with the electronic health record. There may be some benefits, however, in terms of other outcomes.
A 2005 systematic review
had concluded that CDSSs improved practitioner performance in 64% of
the studies and patient outcomes in 13% of the studies. CDSSs features
associated with improved practitioner performance included automatic
electronic prompts rather than requiring user activation of the system.
A 2005 systematic review found... "Decision support systems significantly improved clinical practice in 68% of trials."
The CDSS features associated with success included integration into the
clinical workflow rather than as a separate log-in or screen.,
electronic rather than paper-based templates, providing decision
support at the time and location of care rather than prior and providing
recommendations for care.
However, later systematic reviews were less optimistic about the effects of CDS, with one from 2011 stating "There
is a large gap between the postulated and empirically demonstrated
benefits of [CDSS and other] eHealth technologies ... their
cost-effectiveness has yet to be demonstrated".
A 5-year evaluation of the effectiveness of a CDSS in
implementing rational treatment of bacterial infections was published in
2014; according to the authors, it was the first long term study of a
CDSS.
Challenges to adoption
Clinical challenges
Much
effort has been put forth by many medical institutions and software
companies to produce viable CDSSs to support 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 clinical workflow. Some CDSSs have met with
varying amounts of success, while others have suffered from common
problems preventing or reducing successful adoption and acceptance.
Two sectors of the healthcare domain in which CDSSs have had a
large impact are the pharmacy and billing sectors. There are commonly
used pharmacy and prescription ordering systems that now perform
batch-based checking of orders for negative drug interactions and report
warnings to the ordering professional. Another sector of success for
CDSS is in billing and claims filing. Since many hospitals rely on Medicare
reimbursements to stay in operation, systems have been created to help
examine both a proposed treatment plan and the current rules of Medicare
in order to suggest a plan that attempts to address both the care of
the patient and the financial needs of the institution.
Other CDSSs that are aimed at diagnostic tasks have found
success, but are often very limited in deployment and scope. The Leeds
Abdominal Pain System went operational in 1971 for the University of
Leeds hospital, and was reported to have produced a correct diagnosis in
91.8% of cases, compared to the clinicians' success rate of 79.6%.
Despite the wide range of efforts by institutions to produce and
use these systems, widespread adoption and acceptance has still not yet
been achieved for most offerings. One large roadblock to acceptance has
historically been workflow integration. A tendency to focus only on the
functional decision making core of the CDSS existed, causing a
deficiency in planning for how the clinician will actually use the
product in situ. Often CDSSs were stand-alone applications, requiring
the clinician to cease working on their current system, switch to the
CDSS, input the necessary data (even if it had already been inputted
into another system), and examine the results produced. The additional
steps break the flow from the clinician's perspective and cost precious
time.
Technical challenges and barriers to implementation
Clinical
decision support systems face steep technical challenges in a number of
areas. Biological systems are profoundly complicated, and a clinical
decision may utilize an enormous range of potentially relevant data. For
example, an electronic evidence-based medicine system may potentially consider a patient's symptoms, medical history, family history and genetics,
as well as historical and geographical trends of disease occurrence,
and published clinical data on medicinal effectiveness when recommending
a patient's course of treatment.
Clinically, a large deterrent to CDSS acceptance is workflow integration.
Another source of contention with many medical support systems is
that they produce a massive number of alerts. When systems produce high
volume of warnings (especially those that do not require escalation),
aside from the annoyance, clinicians may pay less attention to warnings,
causing potentially critical alerts to be missed.
Maintenance
One
of the core challenges facing CDSS is difficulty in incorporating the
extensive quantity of clinical research being published on an ongoing
basis. In a given year, tens of thousands of clinical trials are
published.
Currently, each one of these studies must be manually read, evaluated
for scientific legitimacy, and incorporated into the CDSS in an accurate
way. In 2004, it was stated that the process of gathering clinical data
and medical knowledge and putting them into a form that computers can
manipulate to assist in clinical decision-support is "still in its
infancy".
Nevertheless, it is more feasible for a business to do this
centrally, even if incompletely, than for each individual doctor to try
to keep up with all the research being published.
In addition to being laborious, integration of new data can
sometimes be difficult to quantify or incorporate into the existing
decision support schema, particularly in instances where different
clinical papers may appear conflicting. Properly resolving these sorts
of discrepancies is often the subject of clinical papers itself, which often take months to complete.
Evaluation
In
order for a CDSS to offer value, it must demonstrably improve clinical
workflow or outcome. Evaluation of CDSS is the process of quantifying
its value to improve a system's quality and measure its effectiveness.
Because different CDSSs serve different purposes, there is no generic
metric which applies to all such systems; however, attributes such as
consistency (with itself, and with experts) often apply across a wide
spectrum of systems.
The evaluation benchmark for a CDSS depends on the system's goal:
for example, a diagnostic decision support system may be rated based
upon the consistency and accuracy of its classification of disease (as
compared to physicians or other decision support systems). An
evidence-based medicine system might be rated based upon a high
incidence of patient improvement, or higher financial reimbursement for
care providers.
Combining with electronic health records
Implementing
EHRs was an inevitable challenge. The reasons behind this challenge are
that it is a relatively uncharted area, and there are many issues and
complications during the implementation phase of an EHR. This can be
seen in the numerous studies that have been undertaken.
However, 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.
EHRs are a way to capture and utilise real-time data to provide
high-quality patient care, ensuring efficiency and effective use of time
and resources. Incorporating EHR and CDSS together into the process of
medicine has the potential to change the way medicine has been taught
and practiced. It has been said that "the highest level of EHR is a CDSS".
Since "clinical decision support systems (CDSS) are computer
systems designed to impact clinician decision making about individual
patients at the point in time that these decisions are made", it is clear that it would be beneficial to have a fully integrated CDSS and EHR.
Even though the benefits can be seen, to fully implement a CDSS
that is integrated with an EHR has historically required significant
planning by the healthcare facility/organisation, in order for the
purpose of the CDSS to be successful and effective.
The success and effectiveness can be measured by the increase in patient
care being delivered and reduced adverse events
occurring. In addition, there would be a saving of time and resources,
and benefits in terms of autonomy and financial benefits to the
healthcare facility/organisation.
Benefits of CDSS combined with EHR
A
successful CDSS/EHR integration will allow the provision of best
practice, high quality care to the patient, which is the ultimate goal
of healthcare.
Errors have always occurred in healthcare, so trying to minimise
them as much as possible is important in order to provide quality
patient care. Three areas that can be addressed with the implementation
of CDSS and Electronic Health Records (EHRs), are:
- Medication prescription errors
- Adverse drug events
- Other medical errors
CDSSs will be most beneficial in the future when healthcare
facilities are "100% electronic" in terms of real-time patient
information, thus simplifying the number of modifications that have to
occur to ensure that all the systems are up to date with each other.
The measurable benefits of clinical decision support systems on
physician performance and patient outcomes remain the subject of ongoing
research.
Barriers
Implementing
electronic health records (EHR) in healthcare settings incurs
challenges; none more important than maintaining efficiency and safety
during rollout,
but in order for the implementation process to be effective, an
understanding of the EHR users' perspectives is key to the success of
EHR implementation projects. In addition to this, adoption needs to be actively fostered through a bottom-up, clinical-needs-first approach. The same can be said for CDSS.
As of 2007, the main areas of concern with moving into a fully integrated EHR/CDSS system have been:
- Privacy
- Confidentiality
- User-friendliness
- Document accuracy and completeness
- Integration
- Uniformity
- Acceptance
- Alert desensitisation
as well as the key aspects of data entry that need to be addressed
when implementing a CDSS to avoid potential adverse events from
occurring. These aspects include whether:
- correct data is being used
- all the data has been entered into the system
- current best practice is being followed
- the data is evidence-based
A service oriented architecture has been proposed as a technical means to address some of these barriers.
Status in Australia
As
of July 2015, the planned transition to EHRs in Australia is facing
difficulties. The majority of healthcare facilities are still running
completely paper-based systems, and some are in a transition phase of
scanned EHRs, or are moving towards such a transition phase.
Victoria has attempted to implement EHR across the state with its
HealthSMART program, but due to unexpectedly high costs it has
cancelled the project.
South Australia (SA) however is slightly more successful than
Victoria in the implementation of an EHR. This may be due to all public
healthcare organisations in SA being centrally run.
(However, on the other hand, the UK's National Health Service is also centrally administered, and its National Programme for IT in the 2000s, which included EHRs in its remit, was an expensive disaster.)
SA is in the process of implementing "Enterprise patient administration system
(EPAS)". This system is the foundation for all public hospitals and
health care sites for an EHR within SA and it was expected that by the
end of 2014 all facilities in SA will be connected to it. This would
allow for successful integration of CDSS into SA and increase the
benefits of the EHR.
By July 2015 it was reported that only 3 out of 75 health care facilities implemented EPAS.
With the largest health system in the country and a federated
rather than centrally administered model, New South Wales is making
consistent progress towards statewide implementation of EHRs. The
current iteration of the state's technology, eMR2, includes CDSS
features such as a sepsis pathway for identifying at-risk patients based
upon data input to the electronic record. As of June 2016, 93 of 194
sites in-scope for the initial roll-out had implemented eMR2.
Status in Finland
Duodecim EBMEDS Clinical Decision Support service is used by more than 60% of Finnish public health care doctors.
