AI is revolutionizing healthcare by providing new ways to improve the accuracy, speed, and efficiency of clinical decision-making. With its ability to analyze vast amounts of patient data, AI is helping doctors and healthcare professionals make more informed diagnoses faster.
This article looks at AI-based clinical decision support (CDS) systems and describes some successful real-life examples of where they are already having a positive impact on patient outcomes.
Enhanced clinical decision-making
The NHS defines CDS systems as digital tools supporting clinicians to make more equitable, evidence-based decisions. They can be used across care settings, specialities and pathways to improve healthcare delivery and reduce errors.
There are five core principles for CDS design – providing the right information, to the right person, in the right format, through the right channel, at the right time – and CDS systems must adhere to the six ‘i’s; inclusive, intelligent, interpretable, integrated, impactful, and interoperable.
AI technology can simulate human reasoning thinking, collecting and expressing learned knowledge content by generating suggestions, and reducing the dependence on memory, the decision error rate, and the response time. In this way, AI can help make reasonable and safe healthcare diagnosis and treatment decisions, ensuring clinical safety and quality, and improving treatment efficiency and efficacy.
AI-based CDS systems make use of existing health data to facilitate clinical decisions and improve evidence-based care. They can analyze a patient’s electronic health records and provide healthcare providers with real-time insights into their condition, helping identify potential health risks, such as drug interactions or early signs of disease, that may have otherwise gone unnoticed.
Additionally, AI CDS systems can help clinicians make more accurate diagnoses by analyzing medical imaging, such as X-rays, CT or MRI scans, and comparing them to a vast database of similar images. This helps identify abnormalities or potential issues that may not be immediately apparent to the human eye.
AI-backed CDS systems in action
The examples below demonstrate just some of the many areas where AI is already helping to improve diagnostic accuracy, increase efficiency, and enhance patient outcomes:
Diagnosing lung cancer
This CDS system was designed to analyze low-dose CT scans, using AI algorithms to identify patterns that indicate the presence of cancer. One of the key benefits is that it reduces the number of false positives, therefore avoiding unnecessary biopsies and surgeries that can prove both costly and traumatic for patients. The system has also been shown to improve the sensitivity of lung cancer diagnosis, helping to identify more cases at an early stage when it is more treatable.
Diagnosing acute kidney injury
This AI-backed CDS system was developed by The Royal Free Hospital in London to analyze patient data, such as vital signs, laboratory results and electronic health records, to provide a diagnosis of acute kidney injury within hours of a patient being admitted to the hospital. In clinical trials, it was found to have an accuracy of over 80%, and the time taken to diagnose went from 2.5 days to around 7 hours, which is key to help prevent further kidney damage.
Diagnosing sepsis
This CDS system was developed by the University of California to assist in the faster diagnosis of sepsis, again key to preventing further organ damage. In clinical trials, the diagnosis time was reduced to an average of 6 hours from 1.5 days, with an accuracy of 85%.
Diagnosing skin cancer
This CDS system was developed by researchers at Stanford University to improve the accuracy of skin cancer diagnosis, using AI algorithms to analyze images of skin lesions. The system is trained on a dataset of over 130,000 images of skin lesions, including both benign and malignant cases, and in clinical trials was found to have an accuracy of 86.6%.
Next steps in clinical decision support
AI-backed CDS systems empower healthcare professionals with real-time insights and evidence-based recommendations. By analyzing patient data, relevant research, and medical literature, they assist clinicians in making accurate diagnoses and selecting appropriate treatments.
However, while AI can provide valuable insights and recommendations, it is ultimately up to the healthcare provider to make the final decision about a patient’s care. AI will continue to play a vital but supporting role.
The life sciences industry is increasingly leveraging AI to accelerate research, delivering optimal accuracy and speed, while also freeing up valuable resources. Read more about the Impacts of AI in Life Sciences by clicking the button below.
