A clinical decision support system (CDSS) is a
health information technology
Health information technology (HIT) is health technology, particularly information technology, applied to health and health care. It supports health information management across computerized systems and the health information exchange, secure ex ...
, provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, to help health and health care. CDSS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients, clinical guidelines, condition-specific order sets;´, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually relevant reference information, among other tools. Robert Hayward of the Centre has proposed a working definition 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
Artificial intelligence (AI) has been used in applications to alleviate certain problems throughout industry and academia. Artificial intelligence, AI, like electricity or computers, is a general purpose technology that has a multitude of applica ...
.
Characteristics
A clinical decision support system is an active knowledge system that uses variables of patient data to produce advice regarding health care. This implies that a CDSS is simply a
decision support system
A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and h ...
that is focused on using
knowledge management
Knowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organisational objectives by making ...
.
Purpose
The main purpose of modern CDSS is to assist clinicians at the point of care.
[Berner, Eta S., ed. Clinical Decision Support Systems. New York, NY: Springer, 2007.] This means that clinicians interact with a CDSS to help to analyse and reach a
diagnosis
Diagnosis is the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines, with variations in the use of logic, analytics, and experience, to determine " cause and effect". In systems engin ...
based on patient data for different diseases.
In the early days, CDSSs were conceived to make decisions for the clinician literally. 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, utilising both their knowledge and the CDSS's, better to analyse 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.
["Decision support systems ." 26 July 2005. 17 Feb. 2009]
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 (DDSS). 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
In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems.
In everyday life, an auto mechanic who fixes an engine by recalli ...
(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
Radiation therapy or radiotherapy, often abbreviated RT, RTx, or XRT, is a therapy using ionizing radiation, generally provided as part of cancer treatment to control or kill malignant cells and normally delivered by a linear accelerator. Radia ...
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 the 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 help the physician prepare the diagnoses. CDSSs help review and filter the physician's preliminary diagnostic choices to improve outcomes. 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
The National Health Service (NHS) is the umbrella term for the publicly funded healthcare systems of the United Kingdom (UK). Since 1948, they have been funded out of general taxation. There are three systems which are referred to using the " ...
in England, is to use a DDSS to
triage
In medicine, triage () is a practice invoked when acute care cannot be provided for lack of resources. The process rations care towards those who are most in need of immediate care, and who benefit most from it. More generally it refers to prio ...
medical conditions out of hours by suggesting a suitable next step to the patient (e.g. call an
ambulance
An ambulance is a medically equipped vehicle which transports patients to treatment facilities, such as hospitals. Typically, out-of-hospital medical care is provided to the patient during the transport.
Ambulances are used to respond to medi ...
, 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
In the field of artificial intelligence, an inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert ...
, 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
Drug interactions occur when a drug's mechanism of action is disturbed by the concomitant administration of substances such as foods, beverages, or other drugs. The cause is often the inhibition of the specific receptors available to the drug, ...
, then a rule might be that IF drug X is taken AND drug Y is taken THEN alert the 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
Gello ( grc, Γελλώ), in Greek mythology, is a female demon or revenant who threatens the reproductive cycle by causing infertility, miscarriage, and infant mortality. By the Byzantine era, the () were considered a class of beings. Women bel ...
or CQL (Clinical Quality Language) is needed for expressing knowledge artefacts in a computable manner. For example: if a patient has diabetes mellitus, and if the last haemoglobin A1c test result was less than 7%, recommend re-testing if it has been over six months, but if the last test result was greater than or equal to 7%, then recommend re-testing if it has been over three months.
The current focus of th
HL7CDS WG is to build on the Clinical Quality Language (CQL). The U.S. Centers for Medicare & Medicaid Services (CMS) has announced that it plans to use CQL for the specification of Electronic Clinical Quality Measures (eCQMs).
Non-knowledge-based CDSS
CDSSs which do not use a knowledge base use a form of
artificial intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...
called
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
, which allow computers to learn from past experiences and/or find patterns in clinical data. This eliminates the need for writing rules and 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, 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 machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratorie ...
s,
artificial neural network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
An ANN is based on a collection of connected unit ...
s and
genetic algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to gene ...
s.
#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 covers the diagnosis of many diseases.
An example of a non-knowledge-based CDSS is a web server developed using a support vector machine for the prediction of gestational diabetes in Ireland.
Regulations
United States
With the enactment of the
American Recovery and Reinvestment Act of 2009
The American Recovery and Reinvestment Act of 2009 (ARRA) (), nicknamed the Recovery Act, was a stimulus package enacted by the 111th U.S. Congress and signed into law by President Barack Obama in February 2009. Developed in response to the Gr ...
(ARRA), there is a push for widespread adoption of
health information technology
Health information technology (HIT) is health technology, particularly information technology, applied to health and health care. It supports health information management across computerized systems and the health information exchange, secure ex ...
through the Health Information Technology for Economic and Clinical Health Act (HITECH). Through these initiatives, more hospitals and clinics are integrating
electronic medical record
An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared throu ...
s (EMRs) and
computerized physician order entry
Computerized physician order entry (CPOE), sometimes referred to as computerized provider order entry or computerized provider order management (CPOM), is a process of electronic entry of medical practitioner instructions for the treatment of pat ...
(CPOE) within their health information processing and storage. Consequently, the
Institute of Medicine
The National Academy of Medicine (NAM), formerly called the Institute of Medicine (IoM) until 2015, is an American nonprofit, non-governmental organization. The National Academy of Medicine is a part of the National Academies of Sciences, E ...
(IOM) promoted the usage of health information technology, including clinical decision support systems, to advance the 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
The United States Department of Health and Human Services (HHS) is a cabinet-level executive branch department of the U.S. federal government created to protect the health of all Americans and providing essential human services. Its motto is " ...
(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 diseases 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
An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared throu ...
.
There may be some benefits, however, in terms of other outcomes.
A 2005
systematic review
A systematic review is a Literature review, scholarly synthesis of the evidence on a clearly presented topic using critical methods to identify, define and assess research on the topic. A systematic review extracts and interprets data from publ ...
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 care recommendations.
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
DSS and other
DSS may refer to:
Science and technology Biology and medicine
*Dejerine Sottas syndrome, genetic disorder a.k.a. Charcot-Marie-Tooth disease, type 3
*Dengue shock syndrome
*Disease-specific survival
*Dextran, a chemical used experimentally to in ...
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 an 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. Commonly used pharmacy and prescription-ordering systems now perform batch-based checking 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 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. It 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 have 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 how the clinician will use the product in situ. 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 utilise an enormous range of potentially relevant data. For example, an electronic
evidence-based medicine
Evidence-based medicine (EBM) is "the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients". The aim of EBM is to integrate the experience of the clinician, the values of t ...
system may potentially consider a patient's symptoms, medical history,
family history
Genealogy () is the study of families, family history, and the tracing of their Lineage (anthropology), lineages. Genealogists use oral interviews, historical records, genetic analysis, and other records to obtain information about a family a ...
and
genetics
Genetics is the study of genes, genetic variation, and heredity in organisms.Hartl D, Jones E (2005) It is an important branch in biology because heredity is vital to organisms' evolution. Gregor Mendel, a Moravian Augustinian friar wor ...
, as well as historical and geographical trends of disease occurrence, and published clinical data on therapeutic effectiveness when recommending a patient's course of treatment.
Clinically, a large deterrent to CDSS acceptance is workflow integration.
While it has been shown that clinicians require explanations of Machine Learning-Based CDSS, in order to able to understand and trust their suggestions, there is an overall distinct lack of application of
explainable Artificial Intelligence
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine lear ...
in the context of CDSS, thus adding another barrier to the adoption of these systems.
Another source of contention with many medical support systems is that they produce a massive number of alerts. When systems produce a high volume of warnings (especially those that do not require escalation), besides the annoyance, clinicians may pay less attention to warnings, causing potentially critical alerts to be missed. This phenomenon is called alert fatigue.
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 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 (see
meta-analysis
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting me ...
), 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 quantifies its value to improve a system's quality and measure its effectiveness. Because different CDSSs serve different purposes, no generic metric applies to all such systems; however, attributes such as consistency (with 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. This challenge is because 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. Still, less is known about transitioning from
legacy
In law, a legacy is something held and transferred to someone as their inheritance, as by will and testament. Personal effects, family property, marriage property or collective property gained by will of real property.
Legacy or legacies may refer ...
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, fully implementing a CDSS integrated with an EHR has historically required significant planning by the healthcare facility/organisation for the CDSS to be successful and effective.
The success and effectiveness can be measured by the increased patient care being delivered and reduced
adverse events
An adverse event (AE) is any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have a causal relationship with this treatment. An adverse event can ther ...
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 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
In software engineering, service-oriented architecture (SOA) is an architectural style that focuses on discrete services instead of a monolithic design. By consequence, it is also applied in the field of software design where services are provided ...
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. Most healthcare facilities are still running completely paper-based systems; some are in a transition phase of scanned EHRs or moving towards such a transition phase.
Victoria has attempted to implement EHR across the state with its HealthSMART program, but it has cancelled the project due to unexpectedly high costs.
South Australia (SA) however is slightly more successful than Victoria in the implementation of an EHR. This may be because all public healthcare organisations in SA are centrally run.
(However, on the other hand, the UK's
National Health Service
The National Health Service (NHS) is the umbrella term for the publicly funded healthcare systems of the United Kingdom (UK). Since 1948, they have been funded out of general taxation. There are three systems which are referred to using the " ...
is also centrally administered, and its
National Programme for IT
The NHS Connecting for Health (CFH) agency was part of the UK Department of Health and was formed on 1 April 2005, having replaced the former NHS Information Authority. It was part of the Department of Health Informatics Directorate, with the role ...
in the 2000s, which included EHRs in its remit, was an expensive disaster.)
SA is in the process of implementing "Enterprise
patient administration system
Patient Administration Systems (often abbreviated to PAS) developed out of the automation of administrative paperwork in healthcare organisations, particularly hospitals, and are one of the core components of a hospital's IT infrastructure. The P ...
(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 a 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
The EBMEDS Clinical Decision Support service provided by Duodecim Medical Publications Ltd is used by more than 60% of Finnish public health care doctors.
Research
Prescription errors
A study in the UK tested the Salford Medication Safety Dashboard (SMASH), a web-based CDSS application to help GPs and pharmacists find people in their electronic health records who might face safety hazards due to prescription errors. The dashboard was successfully used in identifying and helping patients with already registered unsafe prescriptions and later it helped monitoring new cases as they appeared.
See also
*
Gello Expression Language The GELLO Expression Language was started in 2001 and introduced in 2002; in 2005, GELLO was adopted as an international standard by Health Level Seven International and ANSI for a decision support language. GELLO Release 2 was completed and approve ...
*
*
Medical algorithm
A medical algorithm is any computation, formula, statistical survey, nomogram, or look-up table, useful in healthcare. Medical algorithms include decision tree approaches to healthcare treatment (e.g., if symptoms A, B, and C are evident, then ...
*
Medical informatics
Health informatics is the field of science and engineering that aims at developing methods and technologies for the acquisition, processing, and study of patient data, which can come from different sources and modalities, such as electronic hea ...
*
Personal Health Information Protection Act
The ''Personal Health Information Protection Act'', (the Act) also known as PHIPA ('pee-hip-ah'), is Ontario legislation established in November 2004. PHIPA is one of two components of the Health Information Protection Act 2004. The ''Health Info ...
(a law in force in Ontario)
*
Treatment decision support
Treatment decision support consists of the tools and processes used to enhance medical patients’ healthcare decision-making. The term differs from ''clinical'' ''decision support'', in that clinical decision support tools are aimed at medical pr ...
(decision support tools for patients)
*
Artificial intelligence in healthcare
Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of compl ...
*
A Prospective Medical System of the Future /A complete health care system
References
External links
Duodecim EBMEDS Clinical Decision SupportDecision support chapter from Coiera's Guide to Health Informatics maintains an extensive archive of Artificial Intelligence systems in routine clinical use.
* Robert Trowbridge/ Scott Weingarten
{{DEFAULTSORT:Clinical Decision Support System
Information systems
Health informatics
Medical software
Medical expert systems
Applications of artificial intelligence
Decision support systems