Clinical Decision Support System

A Clinical Decision Support System (CDSS) is a health information technology system that is designed to assist physicians and other health professionals 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".

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 (active voice) recommendations for care, not just assessments.

Purpose

The main purpose of modern CDSS is to assist clinicians at the point of care.^[6] This means that a clinician would interact with a CDSS to help determine diagnosis, analysis, etc. of patient data. Typically the CDSS would make suggestions of outputs or a set of outputs for the clinician to look through and the clinician officially picks useful information and removes erroneous CDSS suggestions.^[5]

There are two main types of CDSS:

·         Knowledge-Based

·         NonKnowledge-Based

Knowledge-based CDSS

Most CDSS consist of three parts, the knowledge base, inference engine, and 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 will allow the system to show the results to the user as well as have input into the system.

Non-knowledge-based CDSS

CDSS’s 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. Two types of non-knowledge-based systems are artificial neural networks and genetic algorithms.

Artificial neural networks or more generally neural networks use nodes and weighted connections between them to analyze the patterns found in the patient data to derive the associations between the symptoms and a diagnosis. This eliminates the need for writing rules and for expert input. However since the system cannot explain the reason it uses the data the way it does, most clinicians don’t use them for reliability and accountability reasons.

Clinical Challenges

Much effort has been put forth by medical institutions and software companies to produce viable CDSSs to cover 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 workflow. To this end CDSSs have met with varying amounts of success, while others suffer from common problems preventing or reducing successful adoption and acceptance.

Electronic Health Records and CDSS

Implementing Electronic Health Records (EHR) was always going to be an inevitable challenge. The reasons behind this challenge is that it is a relatively uncharted area as it is something that has never been done before, thus there is; and will be many issues and complications during the implementation phase of an EHR. This can be seen throughout the numerous studies that have been undertaken. Challenges in implementing electronic health records (EHRs) have received some attention, but less is known about the process of transitioning from legacy EHRs to newer systems.With all of this said, electronic health records are the way of the future for healthcare industry. It is a way to capture and utilise real-time data to provide high-quality patient care, ensuring efficiency and effective use of time and resources. By incorporating EHR and CDSS it has the potential to change the way medicine has been taught and practiced.As it is said that, “the highest level of the EHR is a CDSS”.

 

Benefits of CDSS and EHR

There has always been errors that occur within the healthcare industry, thus trying to minimise them as much as possible in order to provide quality patient care. Four areas that can be addressed with the implementation of CDSS and Electronic Health Records (EHRs), are:

CDSS will be most beneficial once the healthcare facility is 100% electronic thus simplifying the number of modifications that have to occur to ensure that all the systems are up to date. However, the measurable benefits of clinical decision support systems on physician performance and patient outcomes remain the subject of ongoing research. Systematic reviews of the literature have yielded differing correlations to date.

Barriers to CDSS and EHR

Service oriented architecture has been proposed as a way to address some of the barriers.^[18] The main barriers associated with CDSS and EHRs consist of feasibility (cost), poor usability/ integration, uniformity, clinician non-acceptance, alert desensitisation, as well as the key fields of data entry that need to be addressed when implementing a CDSS to avoid potential adverse events from occurring. These include:

→ Correct data is being used
→ All the data has been implemented
→ Current best practice
→ Evidence based

The main areas of concern with moving into a fully integrated EHR system are:

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