Benefits of Machine Learning in Healthcare

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. 

Will Machine Learning Replace Doctors?

The question of whether machine learning will replace doctors is both complex and nuanced, touching on the evolving intersection of technology and healthcare. Machine learning for healthcare has seen exponential growth, offering groundbreaking capabilities that range from improving diagnostic accuracy to personalizing patient treatment plans. However, to fully understand the impact of machine learning in medicine, it is essential to explore the roles it plays and the potential it holds.

Machine learning in medicine, sometimes referred to as “ML” is not a new concept; it has been a field of research and application for decades. However, recent advancements in computational power and data availability have accelerated its growth. ML in healthcare is now seen as a critical tool that can analyze vast amounts of data far beyond human capability, identifying patterns and predicting outcomes with remarkable accuracy. This ability has led to the development of medical machine learning applications that can diagnose diseases from imaging scans, predict patient outcomes, and even suggest treatment options.

How is machine learning used in healthcare? The applications are as diverse as they are impactful. For example, algorithms can analyze retinal images to detect diabetic retinopathy, predict cardiovascular risks from electronic health records, or assist in the early detection of cancerous tumors through imaging. These machine learning in healthcare examples highlight the technology's potential to augment the capabilities of medical professionals, rather than replace them.

The integration of machine learning and medicine is primarily aimed at enhancing the efficiency, accuracy, and personalization of healthcare. By handling data-intensive tasks, medical machine learning allows doctors to focus more on their irreplaceable roles—patient care, decision-making based on clinical judgment, and empathy. The nuanced nature of medical practice, which involves understanding patient history, interpreting complex clinical signs, and considering the socio-emotional aspects of patient care, remains beyond the reach of current ML models.

While the advancement of machine learning in healthcare offers exciting possibilities, it is unlikely to replace doctors entirely. Instead, healthcare machine learning is set to become an invaluable ally in the medical field, enhancing diagnostic and treatment capabilities, improving patient outcomes, and allowing doctors to concentrate on the aspects of care that require human insight and empathy. The future of healthcare lies not in choosing between machine learning and medical professionals but in leveraging the strengths of both to create a more efficient, accurate, and compassionate healthcare system.

What is the Difference Between Machine Learning and Deep Learning in Healthcare?

Deep learning, a subset of machine learning, employs neural networks with multiple layers (hence "deep") to model complex patterns in data. In the realm of healthcare, deep learning has shown remarkable success in interpreting medical images, such as X-rays, MRI scans, and pathology slides, often achieving accuracy comparable to or surpassing that of human experts. The ability of deep learning models to automatically learn feature representations from data, without the need for manual feature extraction, makes them particularly suited for tasks where the relevant features are difficult for humans to specify.

The difference between machine learning and deep learning in healthcare is not just technical but also practical. ML in healthcare often requires domain experts to identify relevant features in the data before training models, making it somewhat dependent on human expertise. In contrast, deep learning can autonomously learn from raw data, making it more powerful for tasks involving complex data such as medical imaging or genomics.

Machine learning in healthcare examples include diagnostic support systems, risk assessment tools, and patient monitoring applications. These systems can help clinicians make better decisions by providing them with insights derived from vast datasets. For instance, a machine learning model might analyze electronic health records (EHRs) to predict which patients are at risk of developing a particular condition, allowing for early intervention.

In contrast, deep learning has enabled the development of more advanced applications, such as automatic detection of cancerous lesions in mammograms or predicting cardiovascular risks from retinal images. These applications illustrate the potential of deep learning to perform tasks that were previously thought to be the exclusive domain of human experts.

While machine learning and deep learning both play crucial roles in advancing healthcare, they serve different purposes and are suited to different types of problems. Machine learning in medicine provides a broad set of tools for analyzing and making predictions from data, requiring some degree of human guidance to identify relevant features. Deep learning, with its ability to autonomously learn from complex data, offers a more powerful approach for tasks involving intricate patterns or high-dimensional data, setting a new standard for what is possible in medical machine learning.

What Are the Benefits of Machine Learning 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.

Data Security and Privacy

With the increasing digitization of health records, securing patient data is paramount. Machine learning can enhance data security by detecting and responding to cybersecurity threats in real-time. ML algorithms can identify unusual patterns that may indicate a data breach, ensuring patient data remains protected.

Improving care

Machine learning in healthcare can also be used by medical professionals to improve the quality of patient care. For example, deep learning medical 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.

Blog by Dr. Seth Flam
CEO, ForeSee Medical