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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.

CDSS aims at improving the quality of care, avoiding errors or adverse drug events, and making the members of the care team more efficient by providing suggestions for the following steps of treatments through the mean of analyzing through a massive amount of digital data. CDS can provide users with useful data insights users may have ignored, or catch potential dangers, such as medication interactions. To streamline workflows and take advantage of existing data sets, CDSS are often incorporated into the electronic health record (EHR).

Usage
The basic principles of CDSS can be applied in many different ways to patient care issues, including early disease detection, to assist in the understanding of highly personalized cancer therapies.

The following are examples showcase CCDSs' usage:


 * Diagnosis decision support systems (DDSS), a specific type of CDSS, proposes a set of appropriate diagnoses after requesting some of the patients data and in response. The doctor 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.


 * Case-based reasoning (CBR) system, another type of CDSS, 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. Doctors 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. It has been claimed that decision support will begin to replace clinicians in common tasks in the future.


 * National Health Service in England uses 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 (which 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).

A list of additional use cases suggested by the FDASIA Health IT Report co-published by FDA, ONC, and the FCC include and not limit to:


 * Calculations of drug dosing;
 * Giving drug formulary guidelines;
 * Providing evidence-base clinician order sets targeted at specific disease or clinician preference requirements;
 * Checking of drug-drug interaction and drug-allergy contraindication to prevent adverse drug events;
 * Reminding of preventative cares;
 * Simplifying the information collection like treatment guidelines;
 * Making estimation for severity of illness and calculating for prediction rules;
 * Duplicate testing alerts, and;
 * Suggestions of appropriate diagnoses based on retrieved patient-specific information;

Effectiveness
The evidence of the effectiveness of CDSS is mixed.

A 2014 systematic review of 28 randomized controlled trials showed an association between a lower risk of morbidity and an active CCDS. However, the systematic review found little evidence to relate the adoption of CCDS can reduce the risk of mortality. In contract, the sepsis mortality rates dropped by fifty-three percent in a hospital in Alabama, after the application of a CCDS with the computerized surveillance algorithm. The algorithm provides real-time analytics about the diagnoses of sepsis and the changes of vital signs to doctors. Doctors also receive reminders about the best practices suggested by the algorithm for treating patients in a severe condition.

Another 2005 systematic review concluded that CDSSs improved practitioner performance in 64% of the studies. The CDSSs improved patient outcomes in 13% of the studies. Sustainable CDSSs features associated with improved practitioner performance include the following:


 * automatic electronic prompts rather than requiring user activation of the system

Both the number and the methodological quality of studies of CDSSs increased from 1973 through 2004.

Another 2005 systematic review found "Decision support systems significantly improved clinical practice in 68% of trials." 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 recommendations for care, not just assessments.

However, other systematic reviews are 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.

Category
There are two main types of CDSS:


 * Knowledge-based
 * Non-knowledge-based

as detailed below.

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 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. 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 (they are so-called "black boxes", because no meaningful information about how they work can be discerned by human inspection), 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.

Three types of non-knowledge-based systems are support vector machines, artificial neural networks and genetic algorithms.


 * 1) 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.
 * 2) 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.
 * 3) 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.

Advantages
“The amount of information we need to understand is getting so untenable that it’s unreasonable to expect the average clinician to integrate all of it into their decision-making effectively and reliably,” said Dr. Joe Kimura, Chief Medical Officer at Atrius Health.

Benefits of CDSS combined with electronic health records
EHRs are a way to capture and utilize 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.

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:


 * 1) Medication prescription errors
 * 2) Adverse drug events
 * 3) Other medical errors

The result from a study conducted by Gartner, a global research and advisory firm, showed the adoption of CDS could prevent 100,000 yearly inpatient adverse drug events, and thus free up 700,000 days of bed yearly, which is the opportunity of saving nearly €300 million in total for the Czech Republic, France, the Netherlands, Sweden, Spain, and the United Kingdom.

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, as noted in the section above.

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, as mentioned above.

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.

Implementation challenges of integrating CCDS with EHR
The main areas of concern with moving into a fully integrated EHR/CDSS system are:


 * 1) Privacy
 * 2) Confidentiality
 * 3) User-friendliness
 * 4) Document accuracy and completeness
 * 5) Integration
 * 6) Uniformity
 * 7) Acceptance
 * 8) 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.

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 (see meta-analysis), 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.

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.