What is Clinical Decision Support?
A complete guide including the systems and tools behind it
Studies referring to clinical decision support go back to the 1970s. However, with the introduction of telehealth and better digital health platforms, there’s been a renewed interest among healthcare executives about its potential to provide better patient outcomes.
So, what is clinical decision support, how does it relate to virtual care and what does that mean for hospitals, health systems, clinics and their patients moving forward? In this blog we’ll dive into what clinical decision support is and the systems and tools used to support it.
What is clinical decision support?
In its simplest definition, clinical decision support (CDS) provides timely information, usually at point-of-care, to help inform decision makers about a patient’s care. Additionally, CDS tools and systems can benefit providers by taking over routine tasks, alerting potential problems and even offer suggestions for patients and providers to consider.
Why is clinical decision support important?
As the healthcare industry becomes increasingly more virtual, we can’t expect our providers to remember every single data set and point out there—it’s just not realistic. Clinical decision support systems and tools assist providers in making quality, patient-centric decisions by analyzing and presenting evidence-based recommendations. With more access to patient data than the industry has ever seen, these tools allow healthcare professionals to get back to their patients faster.
Clinical decision support systems can also be more cost effective by increasing efficiency. The faster providers are able to make care decisions, the quicker patients are presented with a diagnosis and treatment plan. This sets the stage for quicker patient turnaround times without sacrificing the quality of the care given.
In fact, when utilized in partnership with a medical professional, CDS can help create more high-quality patient outcomes by taking into account all available data. This makes it possible to notice changes outside the scope of the professional and patient specific changes within normal limits.
Benefits to clinical decision support systems:
Increased patient safety
A central repository for all information
Lower risks of misdiagnosis
High-quality information delivery to entire care team
Greater efficiency of healthcare practitioners
Improving the quality patient care outcomes
Characterizing clinical decision support systems (CDSS)
So far, we’ve talked a lot about how clinical decision support systems and tools can help health organizations and professionals, but what are these systems? CDSS are categorized into system function, model for giving advice, style of communication, underlying decision making process and human computer interaction.
Let’s dive a little deeper into each.
System function: This characteristic distinguishes two types of functions: What is true? And what to do? This means systems will first determine what is true about a patient and then suggest what to do.
Advice model: This parameter is essentially the approach to giving either passive or active advice. Passive requires the user to do an action to receive advice, like clicking a button or opening a tab. Passive models have been mostly abandoned for their lack of efficacy and dependence on human involvement. Active models typically offer up advice through alerts and warnings. A drawback can be alert fatigue on the part of the user.
Style of communication: This CDSS characteristic follows a consulting and critiquing model. In a consulting model the system is an advisor, asking questions and proposing subsequent actions. For example, advising the right dose for a medication order. A critiquing system lets the user decide the right dose for itself and only afterwards alerts the user that the dose prescribed for this therapy is too low.
Decision making process: These include bayesian models, artificial neural networks, support vector machines and artificial intelligence. These systems are used to improve prediction of outcome, prioritize treatment or help choose the best course of action. Use of such systems in practice, however, have been delayed mainly because of trust issues towards ‘black box’ systems.
Human computer interaction: How the user interacts with the computer. This includes systems like EHR integrations and overlay, keyboard or voice recognition and advice by means of pop-ups, acoustic alarms or messaging systems.
Types of clinical decision support systems:
Digital assistant applications
Computerized physician order entry (CPOE)
Centralized computer application
Disease management systems
Integrated information systems
Challenges and barriers to clinical decision support systems
Reliance on CDSS
It’s important to state that CDSSs are used in addition to, not the replacement of, professional provider knowledge. These systems use imputed data to make recommendations and draw attention to potential problems. However, a CDSS is only as good as the data it’s fed. Taking the time to validate a recommended diagnosis, medication or treatment is still important towards maintaining patient safety.
A pitfall often experienced with CDSS is the occurrence of alert fatigue caused by the overuse of notifications and potential warnings, creating a kind of “boy who cried wolf” scenario. Studies have found up to 95% of CDSS alerts are inconsequential, and oftentimes physicians disagree with or distrust alerts.
Disruptive alerts should be limited to more life-threatening or consequential contraindications, such as serious allergies. As technology continues to improve, CDSSs are becoming smarter and more intentional regarding alerts they give in order to offer providers a clearer patient roadmap.
Lack of interoperability
In reality, this is a problem for most digital health systems in the current digital health environment. Especially in the past, these systems were not made to communicate with each other. As more areas of the healthcare process become digital, investing in interoperability has become a priority for most organizations. For smaller hospitals and clinics, it can be quite a large financial investment and barrier to entry.
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