Digitalization has paved the way for the healthcare industry to flourish. According to the report by IBEF, the healthcare industry is expected to grow at a CAGR of 22% from 2016-2022. It is going to reach the $372 billion mark by 2022 end.

One of the primary reasons behind this exponential growth is the adoption of state-of-art technology to cater to the changing and rising preferences and requirements of the consumers.

The amalgamation of digitalization with healthcare is reducing the communication and physical divide. So, it is contributing to the betterment of patient care service.

If a breach occurs, not only is it going to compromise patient information but also faces a significant penalty if you are found violating the compliant standards that regulate the industry.

Nevertheless, the telehealth industry still faces prime issues, such as an uncertain regulatory future and a digital health bubble.

To cater to the expectations of patients and improve user experience, healthcare facilities are now using paperless statements and various other payment options through the online patient portal and use the latest payment technologies.

But it is often costly and challenging for medical practices to set up such payment processing and invoicing systems in-house. They have to negotiate the terms with the payment processors and create the infrastructure. They also have to deal with the ongoing administrative cost to maintain such technologies. ​

So, healthcare facilities are going to face tougher competition when it comes to retaining and attracting patients who are demanding an experience at par with other consumer brands. They want a streamlined patient service.

Healthcare organizations that are providing services in different locations should make sure that their employees have updated patient information from a centralized location. For this, the healthcare facility needs a patient portal that will keep the details in one place.

With big data analytics, it is possible to gather relevant details. Also, AI can help in incorporating self-correcting facilities and abilities auto updation of medical details in various healthcare devices. It can reduce therapeutic and diagnostic errors, predict health outcomes, and create real-time health risk alerts.

AI in the healthcare industry helps in restoring controlling movements in patients having quadriplegia for regulating upper-limb prostheses.

With AI, it is easier to diagnose heart disease through heart picture and come up with a treatment through automated editable ventricle division.

• Diagnosing: AI has been deployed in a few hospitals for diagnosing critical diseases. This is beneficial as it is more accurate in detecting a condition at an early stage.
• Data Mining: AI is being used in data mining medical records: ResolveData is helping various healthcare organizations to apply cognitive technology for unlocking various health data for powering diagnosis.
• Personal Trainers and Health Assistants: AI-based chatbots are used as personal trainers and health assistants. A few of the use cases of chatbots in healthcare are scheduling doctor appointments, identifying the condition on the basis of symptoms, and providing medical reminders.
• Surgical Robots: AI surgical robots are conceptualized by various technology companies by leveraging the capabilities of Machine Learning applicatio
• Drug Discovery: The increasing application to AI technologies can be seen in drug discovery, too. An AI startup, Helix uses machine learning for responding to verbal requests and questions. This increases research efficiency and improves lab safety.

• Errors and Injuries: The primary risk is healthcare with AI systems can sometimes be wrong and that will result in healthcare issues and injuries. In case AI recommends the wrong drug for the patient or fails to notice a tumor, the patient’s life might be at risk.
• Data Availability: Training in AI systems needs a large amount of data from sources, such as pharmacy records, electronic health records, or consumer-generated information. However, health data can be problematic.
• Privacy Concerns: Another risk is privacy. The need for a large amount of data can incentivize the developers to collect it from several patients. This leads to the concerns of data privacy. Previously lawsuits have been filed between big healthcare facilities and AI developers. AI can also predict private information.
• Professional Realignment: Longer-term risk involves a shift in the medical professional. Some medical specialties are likely to shift when much of their work becomes automatable. Experts are concerned that AI use will result in decreased human knowledge. So, providers will lose the ability to detect and correct AI errors and develop medical knowledge.