Healthcare is an industry that is constantly evolving. New technologies and treatments are being developed all the time, which can make it difficult for healthcare professionals to keep up. In recent years, machine learning in healthcare has become one of the most popular buzzwords. But what is machine learning in healthcare exactly? Why is machine learning so important for patient data? And what are some of the benefits of machine learning in healthcare?
What is Machine Learning?
Machine learning is a specific type of artificial intelligence that allows systems to learn from data and detect patterns without much human intervention. Instead of being told what to do, computers that use machine learning are shown patterns and data which then allows them to reach their own conclusions.
Machine learning algorithms have a variety of functions, like helping to filter email, identify objects in images and analyze large volumes of increasingly complex data sets. Computers use machine learning systems to automatically go through emails and find spam, as well as recognize things in pictures and process big data.
Machine learning in healthcare is a growing field of research in precision medicine with many potential applications. As patient data becomes more readily available, machine learning in healthcare will become increasingly important to healthcare professionals and health systems for extracting meaning from medical information.
Why is Machine Learning Important for Healthcare Organizations?
For the healthcare industry, machine learning algorithms are particularly valuable because they can help us make sense of the massive amounts of healthcare data that is generated every day within electronic health records. Using machine learning in healthcare like machine learning algorithms can help us find patterns and insights in medical data that would be impossible to find manually.
As machine learning in healthcare gains widespread adoption, healthcare providers have an opportunity to take a more predictive approach to precision medicine that creates a more unified system with improved care delivery, better patient outcomes and more efficient patient-based processes.
The most common use cases for machine learning in healthcare among healthcare professionals are automating medical billing, clinical decision support and the development of clinical practice guidelines within health systems. There are many notable high-level examples of machine learning and healthcare concepts being applied in science and medicine. At MD Anderson, data scientists have developed the first deep learning in healthcare algorithm using machine learning to predict acute toxicities in patients receiving radiation therapy for head and neck cancers. In clinical workflows, the medical data generated by deep learning in healthcare can identify complex patterns automatically, and offer a primary care provider clinical decision support at the point of care within the electronic health record.
Large volumes of unstructured healthcare data for machine learning represent almost 80% of the information held or “locked” in electronic health record systems. These are not data elements but relevant data documents or text files with patient information, which in the past could not be analyzed by healthcare machine learning but required a human to read through the medical records.
Human language, or “natural language,” is very complex, lacking uniformity and incorporates an enormous amount of ambiguity, jargon, and vagueness. In order to convert these documents into more useful and analyzable data, machine learning in healthcare often relies on artificial intelligence like natural language processing programs. Most deep learning in healthcare applications that use natural language processing require some form of healthcare data for machine learning.
What Are the Benefits for Healthcare Providers and Patient Data?
As you can see, there are a wide range of potential uses for machine learning technologies in healthcare from improving patient data, medical research, diagnosis and treatment, to reducing costs and making patient safety more efficient. Here’s a list of just some of the benefits machine learning applications in healthcare can bring healthcare professionals in the healthcare industry:
Improving diagnosis
Machine learning in healthcare can be used by medical professionals to develop better diagnostic tools to analyze medical images. For example, a machine learning algorithm can be used in medical imaging (such as X-rays or MRI scans) using pattern recognition to look for patterns that indicate a particular disease. This type of machine learning algorithm could potentially help doctors make quicker, more accurate diagnoses leading to improved patient outcomes.
Developing new treatments / drug discovery / clinical trials
A deep learning model can also be used by healthcare organizations and pharmaceutical companies to identify relevant information in data that could lead to drug discovery, the development of new drugs by pharmaceutical companies and new treatments for diseases. For example, machine learning in healthcare could be used to analyze data and medical research from clinical trials to find previously unknown side-effects of drugs. This type of healthcare machine learning in clinical trials could help to improve patient care, drug discovery, and the safety and effectiveness of medical procedures.
Reducing costs
Machine learning technologies can be used by healthcare organizations to improve the efficiency of healthcare, which could lead to cost savings. For example, machine learning in healthcare could be used to develop better algorithms for managing patient records or scheduling appointments. This type of machine learning could potentially help to reduce the amount of time and resources that are wasted on repetitive tasks in the healthcare system.
Improving care
Machine learning in healthcare can also be used by medical professionals to improve the quality of patient care. For example, deep learning algorithms could be used by the healthcare industry to develop systems that proactively monitor patients and provide alerts to medical devices or electronic health records when there are changes in their condition. This type of data collection machine learning could help to ensure that patients receive the right care at the right time.
Machine learning applications in healthcare are already having a positive impact, and the potential of machine learning to deliver care is still in the early stages of being realized. In the future, machine learning in healthcare will become increasingly important as we strive to make sense of ever-growing clinical data sets.
At ForeSee Medical, machine learning medical data consists of training our AI-powered risk adjustment software to analyze the speech patterns of our physician end users and determine context (hypothetical, negation) of important medical terms. Our robust negation engine can identify not only key terms, but also all four negation types: hypothetical (could be, differential), negative (denies), history (history of) and family history (mom, wife) are the four important negation types. With over 500 negation terms our machine learning technology is able to achieve accuracy rates that are greater than 97%.
Additionally, our proprietary medical algorithms use machine learning to process and analyze your clinical practice data and notes. This is a dynamic set of machine learned algorithms that play a key role in data collection and are always being reviewed and improved upon by our clinical informatics team. Within our clinical algorithms we’ve developed unique uses of machine learning in healthcare such as proprietary concepts, terms and our own medical dictionary. The ForeSee Medical Disease Detector’s natural language processing engine extracts your clinical data and notes, it’s then analyzed by our clinical rules and machine learning algorithms. Natural language processing performance is constantly improving for better outcomes because we continuously feed our “machine” patient healthcare data for machine learning that makes our natural language processing performance more precise.
But not everything is done by artificial intelligence systems or artificial intelligence technologies like machine learning. The data for machine learning in healthcare has to be prepared in such a way that the computer can more easily find patterns and inferences. This statistical technique is usually done by humans that tag elements of the dataset for data quality which is called an annotation over the input. Our team of clinical experts are performing this function as well as analyzing results, writing new rules and improving machine learning performance. However, in order for the machine learning applications in healthcare to learn efficiently and effectively, the annotation done on the patient data must be accurate, and relevant to our task of extracting key concepts with proper context.
ForeSee Medical and its team of clinicians are using machine learning and healthcare data to power our proprietary rules and language processing intelligence with the ultimate goal of superior disease detection. This is the critical driving force behind precision medicine and properly documenting your patients’ HCC risk adjustment coding at the point of care – getting you the accurate reimbursements you deserve.