Clinical Decision Support System: Fundamentals and Applications

By Fouad Sabry

What Is Clinical Decision Support System

A clinical decision support system, often known as a CDSS, is a type of health information technology that offers physicians, staff members, patients, and other individuals access to knowledge and information that is personal to them in order to improve health and health care. The Clinical Decision Support System (CDSS) is comprised of several different applications that improve clinical workflow decision-making. 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 appropriate reference information, as well as a variety of other tools. A working definition of "health evidence" has been offered by Robert Hayward of the Centre. It reads as follows: "Clinical decision support systems link health observations with health knowledge to influence health choices by clinicians for improved health care." CDSSs comprise a prominent topic in artificial intelligence in medicine.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Clinical decision support system

Chapter 2: Gello Expression Language

Chapter 3: International Health Terminology Standards Development Organisation

Chapter 4: Medical algorithm

Chapter 5: Health informatics

Chapter 6: Personal Health Information Protection Act

Chapter 7: Treatment decision support

Chapter 8: Artificial intelligence in healthcare

Chapter 9: Health information technology

Chapter 10: Applications of artificial intelligence

(II) Answering the public top questions about clinical decision support system.

(III) Real world examples for the usage of clinical decision support system in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of clinical decision support system' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of clinical decision support system.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 6, 2023

Book preview

Chapter 1: Clinical decision support system

The term clinical decision support system (CDSS) refers to a type of health information technology that aids in health and medical care by providing knowledge and information tailored to a specific person. In order to improve clinical decision-making, CDSS incorporates a wide range of methods and resources. Computerized alerts and reminders to care providers and patients are just one example of these tools. Others include clinical guidelines, condition-specific order sets, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually relevant reference information. Clinical decision support systems link health observations with health knowledge to influence health choices by clinicians for improved health care; this is the working definition of Health Evidence proposed by Centre member Robert Hayward. The use of CDSSs is a major focus of medical AI research.

A clinical decision support system is an interactive database that draws on patient data to generate clinical recommendations. This definition suggests that a CDSS is merely a DSI with an emphasis on knowledge management.

Helping doctors and nurses out in the field is what modern CDSS is all about. In other words, clinicians use a CDSS to analyze patient data and arrive at a diagnosis for a variety of diseases.

Initially, clinical decision support systems (CDSSs) were designed to actively take on the role of the clinician. After inputting relevant data, the clinician would simply follow the CDSS's recommendation for the next best course of action. The modern approach to CDSS use, on the other hand, involves collaboration between the clinician and the CDSS, with the latter making use of the former's knowledge to produce a more accurate analysis of the patient's data than either could achieve independently. The clinician is responsible for sifting through the CDSS's recommendations and extracting relevant data, while also ignoring the CDSS's incorrect recommendations. and, if necessary, further testing is ordered to help narrow the diagnosis.

A case-based reasoning (CBR) system is another type of case-based decision support system (CDSS).

The National Health Service in England employs a DDSS to triage medical conditions after hours and recommends the best course of action for the patient to take (e.g. call an ambulance, or see a general practitioner on the next working day). The suggestion is based on the available 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 may be incorrect and is not based on the opinion of a medically-trained person - it is only used for initial triage purposes and can be disregarded if common sense or caution suggest otherwise.

Knowledge bases, inference engines, and communication mechanisms are the three main components of most CDSSs.

Knowledge artifacts need to be expressed in a computably meaningful way, and this can only be done through the use of an expression language like GELLO or CQL (Clinical Quality Language). If a patient has had diabetes mellitus for more than six months and their last haemoglobin A1c result was less than 7 percent, then they should get retested if it has been more than three months since their last test.

The HL7 CDS WG is currently concentrating on expanding upon the Clinical Quality Language (CQL).

CDSSs that rely on machine learning instead of a preexisting knowledge base can be helpful in the post-diagnosis stage by highlighting patterns that doctors should further investigate.

As of the year 2012, support-vector machines, artificial neural networks, and genetic algorithms are the three most common forms of non-knowledge-based systems.

Analyzing patient data patterns, artificial neural networks use nodes and weighted connections to infer relationships between symptoms and diagnoses.

To get the best CDSS results possible, genetic algorithms are based on streamlined evolutionary processes that employ directed selection. The random sets of solutions to a problem are evaluated for their individual parts by the selection algorithms. The best solutions are recycled through the process again after being combined and mutated. This process is repeated until the right answer is found. Similar to neural networks, they are black boxes that attempt to infer insights from patient records.

In contrast to the knowledge-based approach, which encompasses the diagnosis of many diseases, non-knowledge-based networks tend to focus on a limited set of symptoms, such as those for a single disease.

As part of the American Recovery and Reinvestment Act of 2009 (ARRA), the Health Information Technology for Economic and Clinical Health Act was passed to encourage the widespread adoption of health information technology (HITECH). As a result of these efforts, more facilities are incorporating EMRs and CPOE into their health information processing and storage infrastructure. As a result, the IOM has advocated for the increased use of health IT, such as clinical decision support systems, to better the standard of care provided to patients. To Err is Human, a 1999 report by the Institute of Medicine, highlighted the staggering number of preventable patient deaths in the United States. There has been a lot of interest in patient care quality since this number was released.

More specific case laws for CDSS and EMRs are still being defined by the Office of National Coordinator for Health Information Technology (ONC) and approved by the Department of Health and Human Services following the passage of the HITECH Act as part of the ARRA, encouraging the adoption of health IT (HHS). There has not yet been a published definition of meaningful use..

Even if no laws were in place, CDSS providers would almost certainly be held legally responsible to patients who could be harmed by their use and to clinicians who employ the technology. However, legal regulations regarding duties of care have not been established.

CDSS are becoming more appealing as a result of recently enacted laws relating to performance shift payment incentives.

CDSS's efficacy has been contested by the available data. Certain diseases seem to respond better to CDSS than others. Patients with blood glucose control, blood transfusion management, prevention of physiologic deterioration, prevention of pressure ulcers, prevention of acute kidney injury, and prophylaxis against venous thromboembolism were found to have better outcomes with the use of CDSS, according to a 2018 systematic review. When the CDSS was integrated with the EHR, researchers in 2014 found no reduction in mortality risk. CDSSs improved practitioner performance in 64% of the studies and patient outcomes in 13% of the studies, according to a 2005 systematic review. Automatic electronic prompts, as opposed to prompts that must be activated by the user, were one CDSS feature linked to improved practitioner performance.

Many hospitals and software developers have put in a lot of time and energy to develop CDSSs that can help with every facet of clinical work. The institution