Healthcare is one of the leading industries that has embraced advanced technologies such as machine learning. The industry is reaping the benefits of tools and algorithms in all aspects, from operations to treatment, medicines, and management of the patient’s data.
In healthcare facilities such as a hospital or testing lab, efficient coordination between the healthcare expert and data management is essential to provide effective treatment and service to the patients. The clinical decision support system (CDSS) is one such tool that bridges the gap between the available patient data and healthcare experts, which aids them in making the appropriate decision in real time.
CDSS is a part of the new healthcare paradigm that intends to enhance a clinician’s ability to make decisions by aiding them with augmented information, usually at the point of care. It helps clinicians to make an informed decision about a patient's treatment procedure.
CDSS systems also help medical teams by handling some routine tasks, warning of potential problems, or providing suggestions for the medical team to consider. Initially, clinical support systems were launched in a basic form that comprised software that aided in clinical decision making by matching the characteristics of a patient with a knowledge database saved on computers.
The modern clinical decision support systems are equipped with machine learning algorithms that can analyze data from more than 1.5 million patients and predict the probability of a patient’s deteriorating health condition in real time.
Clinicians can use the CDSS system recommendations and combine their expertise to make the right decision. Moving forward, the clinical decision support systems are expected to leverage data and make observations that cannot be easily interpreted by humans.
The advanced decision support system in healthcare will help in ensuring patient safety and avoiding complications that may affect the patient’s health. Harnessing the hidden insights of big data is essential for enhancing the efficiency of the healthcare system.
Major factors such as growing emphasis on reducing medical errors, reducing hospital readmission rates to curb healthcare costs, increasing number of chronic diseases, technological advancements, and partnerships between CDSS vendors and cloud-based service providers are influencing the growth of the global clinical decision support systems (CDSS) market.
According to the BIS Research analysis, the global clinical decision support systems (CDSS) market registered a market value of $1.8 billion in 2020 and is expected to reach $3.2 billion by 2030.
Integration of Machine Learning into Clinical Decision Support System
While the CDSS systems add significant value to the healthcare industry, it also comes with substantial challenges. The decision support system in healthcare has been around for many years, but most of them have been standalone solutions and have not been integrated well into the clinical database. Therefore, the poorly implemented CDSS tool might have led to unnecessary alerts and alarm fatigue among healthcare workers. It can also threaten patient safety and lead to inefficient outcomes.
The researchers and developers are working on this issue and aim to develop tools that are intuitive, informative, and efficient. With the onset of the digital revolution in healthcare, technologies such as advanced machine learning algorithms have been at the core of these improved CDSS tools.
However, several foundational challenges such as limited data access, lack of education and training in operating CDSS tools, and poor technology integration are a few roadblocks the healthcare industry has yet to overcome.
The proper application of machine learning and other analytics tools to clinical support systems will require stakeholders to address these challenges with more innovative solutions that could lead to more informed decision making and better patient care.
Machine learning and CDSS tools are most effective when the input data is accurate and complete. However, due to the lack of an efficient data management system, the medical database is usually filled with errors.
Hence, to efficiently integrate clinical decision support systems with machine learning, organizations must develop efficient data-driven systems which are derived from large and accurate datasets rather than specified by human programmers.
With the solution to these challenges, machine learning can be implemented for the decision support system in healthcare while maintaining high performance and remaining equitable.
For instance, in a recent research study conducted by the University of Minnesota Medical School, researchers evaluated the real-time performance of a machine learning tool that supported clinical decision making for emergency department discharges.
In this study, a team of researchers, doctors, and informaticians analyzed the real-time performance of a machine learning-enabled COVID-19 prognostic tool. The study included 1,469 patients that were infected with COVID-19 within 14 days of acute care, hospital-based visit, observation, and inpatient encounters between March 4?and August 21, 2020.
The research team successfully developed and implemented the COVID-19 prediction model at 12 sites. The machine learning-enabled CDSS tool could provide clinical decision support for emergency department providers to conduct shared decision making with patients regarding emergency department discharge. The tool also performed well across all genders, races, and ethnicities, according to researchers.
The integration of a clinical decision support system with machine learning is still at a nascent stage, but it has strong potential for growth and adoption.
Currently, a significant fraction of the CDSS installed do not provide features beyond general alerts, reminders, summary dashboards, and automated information retrieval systems. However, one of the strong points of this technology is that it improves with time.
Continuous input from clinicians, patients, and other stakeholders will be essential for correctly implementing the technology to enhance meaningful uses, reduce costs, and improve treatment outcomes.