The rise in data, processing power, and algorithms have made AI, or more specifically machine learning, useful for more than theory. In the US, Electronic Health Record (EHR) adoption is over 80%, and Europe is catching up. This adoption, in combination with increased standardization, has made it easier to take advantage of gathered data. We can speed up the time it takes to develop a new drug and help diagnose patients. With this data, we can even assist recovering addicts to avoid overdosing.
From start to finish, the cost of developing a new drug is over a billion dollars, and on average, it takes ten or more years. This process includes many stages where testing determines if a particular solution advances to the next stage or not. Luckily, automating some of these tests decreases development times and can potentially saves lives. First, we can hypothesize more potential compounds that have a higher likelihood of working. Furthermore, tests can be automated, and anomalies can be detected. We can discover particular patterns when looking at sample groups and help fight biases in on-going research. Drug discovery will still be a difficult and expensive process, but technological advancement continues to provide more efficient solutions.
Experiments and new projects are not the results of an individual effort but of a team effort. Teams need to share information with each other. Data, experiments, and algorithms are not innately interpreted in an intuitive manner. Visualizations are the most effective way to communicate, as they allow team members to share their findings adequately. Furthermore, dashboards allow you to manipulate inputs, zoom into particular graphs or charts, and have greater control , which leads to deeper insights. At Appsilon, we have put in a lot of effort into open-source Shiny packages for better UI and UX within Shiny dashboards. We have also scaled Shiny applications, without Shiny servers, for hundreds of users. If your team is sharing results but feels that there has to be a better way, take a look at our technology page where you will find a few of our Shiny packages.
Medical imaging is one of the most useful fields of healthcare. It provides a noninvasive view into a patient and has become a necessity for diagnosis. Medical imaging includes CT scan, X-rays, ultrasound, PET, and more. These methods rely on a manual review. Doctors, nurses, and technicians are trained to know what to look for in these scans. But, their time is valuable and can be better spent elsewhere, and deep learning has achieved better-than-human levels of image recognition.
Misdiagnosing cancer is a common issue. That’s where AI comes in. With a second opinion, AI can reduce misdiagnosis by up to 85% with augmented intelligence, according to Andrew Beck at Harvard University.
These models and algorithms can be further applied to medical imaging for classification, anomaly detection, and segmentation. We are not looking to replace doctors but support them in their work, increase the quality of patient care, and keep costs down.
Patient notes, prescription orders, or diaries are a few of the many types of unstructured, handwritten, or hand typed data points in healthcare. You may be surprised to find out that handwriting recognition has been used since the late 80’s when Yann Lecun developed a solution for ZIP code detection. We have come a long way since then and machine learning allows us to recognize even a doctor’s handwriting. Furthermore, we can make sense of the text that has been entered. Patient files can become much more insightful as we begin combining structured and unstructured data. Below, is a diagram of data points that can be associated with a patient’s health records and potentially lead to a diagnosis and treatment.
We are all animals, creatures of habit. Addicts are aware of their predisposition to relapse all too well. The most common cause of an overdose is when a recovering addict relapses and does not account for their decreased tolerance. With such a high potential for loss of life, we must do everything in our power to help those who make mistakes during their recovery process, and solace lies in our habits. Certain behaviors or patterns are indicative of high-risk behavior. With the help of machine learning, we can model historical data and behavior to help us predict which patients need more support with greater accuracy than human intuition.
The influx of data means that doctors are more informed about their patients than ever before. A general practitioner, for example, can prepare for their day by going through their patients’ records. This can be used in telemedicine, a growing aspect of care. Patients can also be diagnosed before even arriving at the hospital or office. A few converging questions can provide an idea of what to expect and which further tests may be needed. Furthermore, the diagnostic predictions we receive become more precise over time, as doctors’ feedback can be used to further train and fine-tune the algorithm.
Healthcare is one of the most data-intensive realms of our society. Wherever we find data, even in the form of a medical notepad, we can apply data science to improve its accuracy and alleviate repetitive workloads. Note that this list is not comprehensive, as there are indefinite ways to apply data science in health care. Medical applications of machine learning can even include cancer detection.
For more information on how data science can help you and your patients, get in touch with us below.