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Clinical Decision Support Systems (CDSS) Part 4: Looking into the Future
As technology continues to advance, the future outlook for Clinical Decision Support Systems (CDSS) in the healthcare domain holds immense promise and potential. These systems, leveraging cutting-edge technologies such as AI, ML, Big Data, and Real-Time Analytics, are poised to continue to solidify their role in supporting healthcare professionals’ decision-making process.
The Clinical Decision Support System market is projected to grow at a CAGR of 11.2% from 2024 to 2030 (1).Concurrently, as more hospitals and healthcare providers begin to adopt and implementCDSS into their everyday workflow expectations surrounding patient outcomes, diagnostic accuracy, and healthcare efficiency are all projected to improve. As governments and health ministries continue to endorse the utilization of CDSS, there is a growing consensus of the need to construct a formalized self-learning knowledge base that is accessible for all key stakeholders (2). This ensures that recommendations are aligned between different systems, and that fundamentally there are minimal discrepancies between what the latest and most accurate medical practices and treatment plans are.
However, predicting trends, future directions, and the evolution of any market is like attempting to navigate without a map, as uncertainties and unforeseen variables can greatly influence the final outcome. Therefore, it is worthwhile to take a look at how CDSS and its underlying purpose can extend its benefits into emerging industries and other applications. To help with this, let’s look deeper into the conversation Vivek Patkar had with HealthcareTransformers.3 Vivek Patkar is the Chief Medical Officer and Co-Founder of Deontics Ltd, a medtech company in the field of AI and Clinical Decision Support.
During Vivek’s discussion with Healthcare Transformers, five emerging trends in healthcare were identified where CDSS will play a huge role moving forward: data-driven patient management, learning healthcare systems, value-based healthcare, remote or virtual monitoring, and sensor technology.
Data-Driven Patient Management
The rise in Big Data in today’s day and age has simultaneously created a rising tide in real world medical data. Coupled with advancements in AI and Machine Learning to create innovative healthcare products and services, this shift towards data-driven care is needed more than ever. At a high-level, CDSS may not seem like the obvious catalyst to such development and evolution; however, an important component of CDSS holds the answer. Fundamentally, every CDSS contains a knowledge base from where medical knowledge and patient-related information are extracted before processed. It is the compilation of data in this knowledge base that will help yield healthcare from evidence-based medicine to data-driven care.
Learning Healthcare Systems
The rise of generative AI has the potential to allow for CDSS to review and monitor its own recommendations for future refinement. The ability of AI to reflect on its own outputs has the capability to construct and refine treatment pathways and clinical guidelines to ensure maximum effectiveness and efficiency is reached.
Value-based Healthcare
The rise of value-based healthcare care models will need to rely on a set of guidelines that yields the greatest care effectiveness in the most efficient and cost-effective manner. CDSS has the potential to provide recommendations that would not only streamline healthcare services but would also improve patient outcomes whilst minimizing costs.
Remote or Virtual Monitoring
The rise of remote and virtual monitoring of patients plays an important role in relieving the burden placed on healthcare systems. By managing less complex patients through a virtual medium, hospital resources are reserved for those of higher priorities. CDSS can play an important role here in determining the severity of patients, provide clinical guidelines for virtual or remote care, and to a certain extent automate the process with some degree of oversight.
Sensor Technology
Stemming from the rising trend of remote and virtual monitoring, medical devices specifically designed to monitor patients and collect data is simultaneously gaining traction. CDSS will benefit greatly from the amount of data collected by these devices and turning them into recommendable action items to help patients and providers.
In Conclusion
While the future of CDSS is promising and its theoretical application is sensical, we believe that the key to the future success of CDSS and its potential importance in a variety of different healthcare mediums lies within the software’s user interface and user experience, and integration into the current clinical and administrative workflow. Convincing providers and key healthcare stakeholders to meaningfully utilize CDSS and leverage its capabilities to its fullest extent will truly unlock the potential of CDSS, as opposed to seeing it as another administrative barrier that is required by laws and regulations.
This post wraps up our CDSS series. Be sure to check out the first three posts if you missed them!
Sources
Clinical Decision Support System (CDSS) Market Size & Forecast (verifiedmarketresearch.com)
Clinical decision support systems CDSS: Future Trends (healthcaretransformers.com)