The Clinical Decision Support System (CDSS) is a piece of software which takes a set of input information about a patient’s clinical situation and produces an output that can help practitioners to make decisions. A CDSS is employed to reduce human error, to
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automate routine tasks, to address information overload, and to make the clinical guidelines available and accessible to a wide range of medical staff (Bemmel and Musen, 1997). CDSSs can provide practitioners, patients, and other individuals with information that is filtered and processed as it is needed (Zheng, 2010).
The use of CDSSs has increased steadily over the last 10 years. In the existing literature, there are over 5000 articles relating to CDSSs, and they suggest that all CDSSs can be classified into one of the following categories: (1) systems of general diagnosis, which suggest differential diagnoses and work-up protocols, (2) systems for a limited number of clinical diagnoses, which produce a specific diagnosis among them, (3) specific systems which are designed to interpret a certain category of images, such as digitised Xray images or pathology slides (Miller, 2009).
There are a wide range of applications for CDSSs within health care. Around 100 CDSSs have been reviewed and classified in this context by Garg et al. (2005), who defined the following categories:
1. Systems for disease management (40 %)
2. Systems for drug dosing and prescribing (29 %)
3. Reminder systems for prevention (21 %)
4. Systems for diagnosis (10 %)
The focus of the vast majority of the proposed CDSSs is on managing already diagnosed cases, and managing drug dosing or drug prescribing, rather than assisting with diagnoses.
Metzger et al. (2002) also classify CDSSs according to the timing at which the CDSS provides the support (before, during, or after the practitioners make the decision).
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Based on internet technology, CDSSs can be classified as stand-alone or web-based systems. Based on the target clinical domain, CDSSs can also be classified into different clinical areas (Kong, 2011).
With regard to the computer algorithms, CDSSs can be categorised into two types: knowledge-based systems, and non-knowledge-based systems (Berner and Lande, 2007; Prasath et al., 2013; Musen and Middleton, 2014). The knowledge-based systems consist of three parts: the knowledge base; the inference engine; and the mechanism for communication with the user.
The knowledge-based systems are the most common type of CDSSs. They rely on medical information compiled in the form of IF-THEN rules. The following is an example of a system providing support to laboratory test ordering: IF new specific test is ordered AND IF the same test was previously ordered within the last two days, THEN alert the practitioner. In this situation the rules are prepared to avoid duplicating the same test. Another example is the diagnostic CDSS, which provides suggestions about the diagnosis to the physicians. The knowledge base contains information about the symptoms of various diseases. The inference engine employs the necessary formulae to combine the knowledge base with the patient’s data. Finally, the communication part of the system is used to interface with the user, to show results and to receive input data (Berner and Lande, 2007).
Many different types of CDSS employ rule-based techniques such as: alerts and reminders, diagnostic assistance, therapy critiquing and planning, prescribing DSS, information retrieval, and image recognition and interpretation (Coria, 2003).
One of the earliest CDSS that is classified as knowledge-based is the MYCIN system (Shortliffe, 1976). In this system the knowledge of infectious diseases is represented as 600 rules, which are formed based on consultation with medical experts.
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Unlike knowledge-based systems, the non-knowledge-based variety does not contain a knowledge base, and do not rely on the medical literature or expert physicians’ knowledge. They also do not need IF-THEN rules; instead they employ data mining algorithms such as neural networks, classifiers, or genetic algorithms. This type of CDSS can learn from the past data of known diagnoses. It does not need any prior knowledge from medical literature or from experts in the medical area. The system makes decisions by studying the patterns within the data to find the relationships between the input features (signs and symptoms) and the diagnoses. After the system is trained it can be used to diagnose the new cases based on their input features (Berner and Lande, 2007). Non-knowledge-based systems are the preferred choice when relevant prior medical knowledge is limited or does not exist (Hardin and Chieng, 2007).
The main advantage of using non-knowledge-based systems is that they do not need to employ IF-THEN rules and do not rely on any medical information from expert clinicians. However, this type of CDSS cannot explain or justify the chosen decision (Berner and Lande, 2007).
The knowledge-based systems are called evidence-adaptive CDSSs when they are designed to use a clinical knowledge base which is derived from, and continually reflects, the most up-to-date evidence from the research literature and practice-based sources (Sim et al., 2001).
With the development of computational power and medical technology, large medical datasets and classification algorithms have become available. Consequently, data mining has gained considerable interest. It has begun to be used in many CDSSs for various applications such as: medical imaging recognition and interpretation systems, gene and protein expression analysis, education systems, laboratory systems, acute care systems, and other miscellaneous systems.