The Fundamentals of Clinical Decision Support Systems
In the past, clinical decision support systems have been rule-based and mainly used for tasks like ordering medications and defining contraindications. For example, if someone has had a bad response to treatment it needs to be noted in a chart with a reminder that this patient needs further assessment before coming for the next exam so that they don’t have the same bad reaction. More often than not, this process has been problematic because the clinical system support usually wasn’t smart enough to make decisions and differentiations that aren’t rule-based. A traditional clinical support system may group medications that shouldn’t be prescribed together. But physicians still have to come in and make the final decision to differentiate what is necessary for the patient.
With machine learning and artificial intelligence, clinical decision support systems can become a more seamless process, with decision-making based on far more data. Using machine learning, the maximum potential of a patient’s electronic health record can be utilized to account for more than is feasible for a physician. Machine-learning models can be utilized to leverage more data and focus on helping the physician achieve better patient outcomes with more data-driven decisions.
Practical Implementation of AI and Machine Learning in Systems
Clinical decision support tools are already in use behind the scenes in many healthcare institutions. Scheduling optimization has broad application for multiple industries. In healthcare, predicting MRI utilization based on weather and patient data can have a tangible impact on scheduling.
But predicting no-shows is just the tip of the iceberg. A 2017 survey of AI experts from Oxford University and Yale predicted that in as little as 120 years, all human jobs could be automated. Surgeons in particular will have even less time. They were predicted to be out of work by 2053. After that, robots are taking full control of the operating room.
At first blush this may seem counterintuitive. Why would machine learning and automation be on the fast track for complicated and high-risk work like surgery? The answer lies in data processing. While human understanding is limited by experience, machine learning models are limited only by the amount of data that can be fed to them.
There are other use cases of AI in clinical decision support systems that are happening behind the scenes, even in clinical care. One example is urgent medical findings. Lets say a patient comes into an ER experiencing symptoms of a heart attack. The reality is that it is impossible to achieve immediate availability at all times in all hospitals. So even symptoms that resemble a life-threatening condition have to be prioritized and managed. Hospital staff have to determine who is more likely to be experiencing a truly urgent condition and who may be experiencing pain from a less serious ailment. Machine learning models can be trained to identify distinctive symptoms as well as accelerate urgent priorities to the top of the queue when needed.
Integration Issues Faced by AI
Various organizations are developing models themselves; however, sometimes, these models don’t integrate well with other data formats. Most times, these companies can only train on the data they have access to, and it may not be representative of the whole patient population. So a lot of institutions are finding out that they have to retrain their model based on the patient population that they see, and this is one reason why more Institutions are working together to take data from across the country multinational so that more diverse data can be made available.
Integrating these technologies at the point of care so that they help rather than hinder the workflow is another critical aspect that most institutions struggle with. To mitigate the effects of these challenges, education is key. Further training of physicians, medical students, and residents is necessary to combat integration issues.
The Future of AI and Machine Learning in Clinical Decision Support Systems
Further improvement is expected in the future; there are plans to make AI help automate or provide a point of care information to help the physician make a better decision for their patients. Also, AI will be used to detect conditions that may be early indicators of a disease and prescribe treatment before the patient gets sick, facilitating better patient outcomes. It may also increase our knowledge of disease, reducing the cost of treatment in general.
AI in clinical system support will help provide high-quality care in underprivileged areas and developing nations where they don’t have the expertise and tools to cater to everyone adequately.
A global survey from Philips showed that 79 percent of healthcare professionals under 40 are confident that digital health technologies can achieve better patient outcomes, while 74 percent believe these tools will improve the patient experience
Unfortunately, when it comes to AI implementation in health care, the Healthcare industry still has a lot further to go. However, maybe it’s fair that healthcare is a little slower to adopt these technologies, and caution to adopt them too quickly has a rational case. But in science and health care, physicians need to make the most data-driven decisions they can. Some people will adopt it faster than others, and thankfully a lot of the academic Health Care Centers have the privilege of being able to do that.
Machine learning has only scratched the surface of the future of healthcare workflows and clinical decision applications.
