How Machine Learning is Transforming Clinical Decision Support Tools


In today’s world, healthcare and medicine are becoming more and more sophisticated. Currently, taking timely medical decisions continues to be one of the most crucial goals to be achieved by the doctors and healthcare providers alike. This is the reason why there are decision support tools being used in the medical world these days. The real reason for such growth and development is to provide good care and medical help to the patients by many healthcare organizations.

Clinical decision support (CDS) systems
A CDS system is a decision-making tool that supports healthcare providers in understanding an issue and provides proper and timely treatment. It analyzes big datasets and large volumes of medical data and analyzes them. This procedure allows the doctors to identify the next proper step to proceed in a treatment. Along with suggesting the next treatment steps, the CDS system also helps in flagging and alerting the clinicians of various problems in a certain procedure or treatment pathway.

High-end decision support tools and software have come about as a result of proper usage of data available and then gleaning useful insights from it.  Almost everything today is supported by data that is available, and for that machine learning and analytics are used by a large portion of the healthcare profession. Digital innovation, algorithms, big data, automation, and deep learning are some of the tools used to create decision support systems. A well-balanced CDS system will not only help in increasing efficiency, but also help in reducing the overall costs. 



Issues with CDS
Clinical decision support systems (CDS) are not an entirely new idea, as for years, it has been in existence in the medical world. Although it is a value increasing tool, there are certain drawbacks to it. The systems which are not properly implemented and designed can pose challenges that can cause loss of money, medical feasibility and even can endanger the safety of the patients. 
The CDS systems before today were highly integrated with all the information bulking sources and points. This fact made them stand alone without much data that was coherent. Lack of integration and automation led to problems like unnecessary alerts.  In return, this led to clinician burnout and alarm fatigue, which can affect patient treatment and safety.

Machine learning in building improved CDS systems
As the clinical decision support system remains a very integral part of the healthcare industry along with the constant worry of the systems not performing to their fullest, the medical industry is now using better ways to update the tools. Researchers, vendors and various developers are inclined to use machine learning and artificial intelligence for their benefits.

Using mathematic algorithms and programs, the researchers are first trying to build good quality data, which will provide better insights and output. Better data will fit the high-end algorithms in a better way, thus increasing the efficiency. Also, artificial intelligence is leveraged, to bring new and highly sophisticated workflow tools.
Clinical decision support systems are important and non-avoidable tools in the healthcare industry these days. With proper data collection, machine learning algorithms, trained personnel, and integration methods,  healthcare professionals are assisted in providing quality care to patients, as well as and also add usefulness and value to organizations.

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