Clinical decision support systems CDSS

Clinical decision support systems CDSS

Clinical Decision Support Systems (CDSSs) are revolutionizing patient and chronic disease management and can be deployed across many fields.

Clinical decision support systems (CSDSSs) are being used to revolutionize healthcare in various ways including patient management, chronic disease management, and even in the shift toward value-based health care (VBHC).  

To dive deeper and learn more about the upcoming trends for CDSSs and how to gain the most value out of these systems, we sat down with Vivek Patkar, Chief Medical Officer at Deontics.

Use cases for CDSS and the ways they are revolutionizing patient care delivery

HT: What are the current main use cases for clinical decision support systems (CDSSs) and in which ways are they revolutionizing the delivery of patient care?  

Vivek Patkar: The clinical decision support system market will be $USD 2.2 billion in the next three to five years from $USD 1.3 billion today.1 New AI-based medical decision support systems are driving this.  

There are four areas of current use cases. Let’s look at how they’re going to revolutionize the delivery of patient care in the coming years.    

1. Therapeutic pathways: Standardized pathways for chronic disease management are becoming more common practice in most healthcare organizations where CDSSs are playing major roles.  

Chronic diseases include common diseases, like respiratory disease, or cardiac diseases, and those requiring more specialist care such as cancer. Chronic diseases must be managed throughout the entire patient journey from prevention to diagnosis, diagnosis to survivorship, to palliative care in some cases.  

2. Precision medicine: Rapidly advancing technologies of high-throughput sciences such as genome sequencing, and pharmacogenomics are changing the healthcare industry both in terms of our understanding of diseases and the availability of targeted therapeutics.  

Targeted therapies allow physicians to minimize unwanted harmful effects by precisely targeting diseased cells based on an individual’s genomic makeup. However, keeping up with rapidly expanding knowledge in the era of precision medicine is becoming a humanly impossible task.

Pharmacogenomics can impact the medication choice for a particular patient, from painkillers to cancer medications. For example, the US Food and Drug Administration (FDA) lists over 100 cancer drugs that have pharmacogenomic associations.2-3  Medical decision support systems can help general practitioners or doctors in hospitals to individualize the dosages of these drugs based on individual genetic make-up, thus preventing many common side effects of these drugs.  

All of this information is difficult to put into practice for an unaided physician without clinical decision support technology. Especially when you are managing patients on multiple medications for many different conditions.  

3. Diagnostic imaging: In diagnostic fields, artificial intelligence (AI)-based support systems are advancing image processing. Whether it is radiology or pathology, these clever AI systems can use many different algorithms to help clinicians and specialists potentially make quicker and safer decisions.  

The AI-based systems assist pathologists and radiologists by pointing out abnormal lesions or areas in the image which could be overlooked or could take a longer time to identify for an unaided clinician.  

4. Personalized healthcare: Understanding patient preferences, values, and individual circumstances, like occupation, plays a key role in the shared decision-making process. Combining clinical evidence from randomized clinical trials with patient values and preferences provides more personalized healthcare, which may help result in better patient satisfaction and improved compliance with the treatments.  


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