Clinical decision support system


A clinical decision support system is a form of health information technology that provides clinicians, staff, patients, or other individuals with knowledge and person-specific information to enhance decision-making in clinical workflows. CDSS tools include alerts and reminders, clinical guidelines, condition-specific order sets, patient data summaries, diagnostic support, and context-aware reference information. They often leverage artificial intelligence to analyze clinical data and help improve care quality and safety. CDSSs constitute a major topic in artificial intelligence in medicine.

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 focused on using knowledge management.

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 analyze and reach a diagnosis based on patient data for different diseases.
In the early days, CDSSs were conceived to make decisions for the clinician in a literal manner. 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 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 review, 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 clinician might use a clinical decision support system is a diagnosis decision support system. 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 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 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 in England, is to use a DDSS to triage medical conditions out of hours by suggesting a suitable next step to the patient. 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

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 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 or CQL 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 the HL7 CDS WG is to build on the Clinical Quality Language. The U.S. Centers for Medicare & Medicaid Services has announced that it plans to use CQL for the specification of Electronic Clinical Quality Measures.

Non-knowledge-based

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 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 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 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

History, United States

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. The Institute of Medicine promoted the usage of health information technology, including clinical decision support systems, to advance the quality of patient care.
With the enactment of the American Recovery and Reinvestment Act of 2009, there was a push for widespread adoption of health information technology through the Health Information Technology for Economic and Clinical Health Act. Through these initiatives, more hospitals and clinics were integrating electronic medical records and computerized physician order entry within their health information processing and storage.
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 the enactment of the HITECH Act included in the ARRA, encouraging the adoption of health IT, more detailed case laws for CDSS and EMRs were still being defined by the Office of National Coordinator for Health Information Technology and approved by Department of Health and Human Services. A definition of "Meaningful use" has yet to be published.

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. 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 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 eHealth technologies... their cost-effectiveness has yet to be demonstrated".
A five-year evaluation of the effectiveness of a CDSS in implementing rational treatment of bacterial infections for antimicrobial stewardship was published in 2014; according to the authors, it was the first long-term study of a CDSS.