Introduction

Healthcare information processing is a multi-disciplinary field that has become synonymous with the technological advancements with vast amounts of information. With the necessity to cut back prices and move towards customized health care, the industry faces changes in 3 core areas specifically, electronic record management, computer aided diagnosis and data integration. A new movement that revolves around a new innovative field of science and technology, bringing change in hospitals, private practices and other healthcare facilities is machine learning.

Information can be enhanced by using analytics and machine learning, a complex field in itself that offers a range of frameworks, tools and techniques that can be exploited. Machine learning can help improve patient care, enhance delivery and is critical with endless healthcare opportunities as more providers are moving towards precision medicine.

What Machine Learning Can Do

At the intersection of healthcare and machine learning is the core issue of synchronizing information and using it to enhance healthcare treatments and infrastructure. One of the solutions is finding ways to efficiently collect and use different types of data for superior analytics, treatment and prevention of conditions and diseases. Through ML, immense data sets can be converted to clinical insights that aid doctors in providing better care leading to better patient outcomes.

The value of machine learning in healthcare is its ability to process huge datasets beyond the scope of human capability and reliably converting the analysis of that data into clinical insights that assist physicians in planning and providing care. This ultimately leads to better outcomes, lower costs of care, and increased patient satisfaction.

For example following a patient’s routine checkup will make it easier to expect the vital signs and blood pressure. However, using machine learning algorithms can enable a lot more than just minor alterations. A patient’s risk for coronary artery disease, stroke or a kidney failure can be shown based on previous 50 blood pressure readings and other information such as race, gender, lab test results, family history, socioeconomic status and latest clinical trial data.

How Machine Learning Algorithms Help

Algorithms can give immediate profit to disciplines with processes that are standardized or reproducible. Those with massive image data sets like cardiology, radiology and pathology, are strong applicants. Machine learning algorithms can be trained to analyze images, determine abnormalities and position areas that require attention, thereby improving the overall accuracy of processes.

Precision medication that involves categorizing mechanisms for multi-factorial diseases and successively different paths for therapy appears to be the frontier. Machine Learning can also be used for real-time data access and remote monitoring which results in increased safety as it can alert when there are signs of harm to patients.

Why ML Is Crucial?

Healthcare industry is moving from thinking of machine learning as a futuristic concept to a real-world tool that is capable of being deployed today. An incremental approach is being taken to find specific cases where the capabilities of machine learning provide value from a specific technological application. Medical capabilities can be matched by training machine learning algorithms to identify cell patterns in a large set of tissues. ML needs to be utilized as a collaborative partner who will help identify the precise areas of focus in the medical field, especially areas with high probability concern.

Leaders in the industry are using deep machine learning algorithms to identify cancerous tumors on mammograms; identify skin cancer and help diagnose diabetic retinopathy in retinal images. This opens the pathway to incorporate more analytics, predictive algorithms and machine learning into everyday medical practices. An example of this is ML algorithm that can review pathology slides and assist with diagnosis. This means results can be recovered in a fraction of the time with a high degree of accuracy. This value is ultimately going to improve outcomes, patient care and satisfaction.

What Next?

The field of medicine is advancing into a new realm of machine learning that is capable of providing a proactive, predictive and preventive feature that is critically essential in every healthcare system. ML is being driven by data as more information can generate effective algorithms to provide better outputs. Doctors are using machine learning and predictive algorithms to give a superior predictive model of mortality which is used to educate patients.

Smaller entities are collaborating with organizations that have big datasets and merging with large data systems to build a repository of healthcare data. The widespread use of machine learning and its applications will be the most important and life-saving technology to be introduced to the healthcare industry. With limitless opportunities to improve and accelerate workflow, financial outcomes and clinical data, machine learning aids the healthcare industry to improve outcomes substantially.

Author – Matt Wilson – A Healthcare Expert, working with Aegis Health Tech as senior developer from last 5 years. He has extensive experience in patient engagement solutions, EMR & EHR Development, Implementation and Integration