Applications of AI in Healthcare

The world of AI

Artificial Intelligence has been around the block for a while now. It aims to imitate the way cognitive functions work in humans. Essentially, AI is the application of machine learning concepts, be it support vector machines, neural network, deep learning or natural language processing. The most important features of AI are its self-learning and correcting abilities. By analyzing large volumes of data, AI can be used to assist all healthcare entities in hospitals, insurance and clinical trials. It can be leveraged to build proactive protocols where real-time inferences are used to make predictions and take action before an adverse event occurs.

AI Applications in Healthcare

Automation of Jobs¹

Providers and radiologists spend a lot of time analyzing test results, CT scans, X-rays, etc. These are mundane tasks that can easily be taught to a machine based on rules and repetition. Image recognition/computer vision technologies are capable enough to analyze and draw inferences from images. AI-based systems can reduce the workload on medical personnel tremendously, while delivering results fast and more accurately.

Complex data analysis²

Clinical data takes diverse shapes as medical notes, doctor’s prescriptions, EMR data, lab images, patient history and so on. Specific applications of AI are capable of making sense of this data and finding hidden patterns that can be used to discover new ways of handling sick patients. Unstructured data is usually difficult to leverage and there are possibilities that some critical information is missed by medical professionals. Using AI we can analyze this data more effectively and provide additional information to doctors to make the process of diagnosing diseases faster and more efficient.

Health Treatment^{1, 2, 3}

Disease management is another area where AI can help physicians in coordinating the care and managing chronic diseases. By identifying which patients display high-risk probability, physicians can focus their resources on the more critical patients. Customized treatment paths that are specific to an individual can be devised and monitored even on a real-time basis. Patients’ social and economic factors can be taken into consideration to deliver much more effective care. Predictive analytics can aid clinical decision-making.

Precision Medicine^{1, 3}

Personalized care by understanding genetic data, EHR data, wearables and other lifestyle data has gained popularity in recent times. AI can be used by doctors to develop precision treatments for complex diseases. The insights can come handy in discovering new ways of using old drugs, creating new ones and predicting risk of diseases in patients. Drug discovery costs and errors can be brought down by creating huge datasets of patients and running AI algorithms to develop precision treatments.

Drug Discovery¹

A lot of research is involved in the process of creating drugs. AI can be used to analyze clinical trials, research papers, patient records and patents. Relationships between biological entities such as symptoms, proteins, diseases and genes can be inferred easily using machine learning algorithms. Assessment of drugs during clinical trials requires sophisticated pattern recognition and AI is capable of performing this. The whole process of drug discovery can be made cost effective and faster by using machine-learning-driven programs.

Member Experience

Healthcare members today are demanding a retail-like experience. They want quick responses and expect the healthcare insurers to not only pay their bills but also make sure they are healthy. AI-powered Chatbots and Virtual Assistants can speed up the dissemination of information to members and reduce errors. Another use of AI is in bringing out the element of personalization. By using non-traditional data sets like social determinants of health (poverty, economic condition, etc.), demographics, psychographics and attitudes towards health in hand with traditional data sets of claims and medical records, 360-degree member views can be created. The approach is to use machine learning algorithms across these views to predict member behaviors. Using this information, targeted and relevant communication and recommendations can be sent to members to improve efficacy of care activities. Apart from this, AI can also be used to analyze member feedback and predict which members are likely to be dissatisfied with services rendered, making preemptive action possible.

The ITC Infotech Advantage

ITC Infotech offers its expertise in AI and machine learning along with deep domain knowledge. Using, machine learning algorithms, our solutions offer advanced analysis and insight capabilities working on large volumes of data. We endeavor to change the existing healthcare landscape and solve critical problems using advanced analytics and AI-based solutions which will improve patient lives, reduce mortality rates, reduce costs, improve profitability and lead to a much enhanced patient/member experience, consequently high patient/member satisfaction.