Usually, care providers use relational databases for storing and accessing patient information. But, relational databases are unable to manage unstructured data. Non-relational databases exploit the recorded patient details, regardless of the format.

Some care providers have adopted electronic health records, only recently. They are not ready to implement non-relational database technology. With added investment, care providers can augment database effectiveness.

Due to limited access to patient details, drug manufacturers tend to waste loads of money on research and development. This cost is then passed on to the consumers.

For catering to the requirements of the patients and improving user experience, billing statements have to be paid patient-friendly. It is necessary to offer paperless statements and different payment options.

Nevertheless, it is often costly and challenging for medical practices to set up payment and invoicing processing systems in-house. Also, the problem of submitting payments because of a lack of options is another reason for the consumers not paying their financial dues. This is mainly due to a lack of price transparency.

Healthcare organizations are facing tougher competition to attract and retain patients who are demanding an experience that matches the customer service provided by consumer brands.

So, healthcare organizations are looking for a streamlined patient experience to resolve most concerns and issues as it is more convenient for them.

Healthcare organizations that are providing different services in different locations should make sure that every employee has access to updated patient details from a centralized location.

But telehealth still faces major problems like the prospective digital health bubble. ​

A drug discovery initiative aims to find a compound that will react with the body’s targeted molecule causing any disease to cure. However, there is a possibility that the drug compounds react with the non-targeted molecule of the body negatively and could lead to life-threatening side effects.

Since pharmaceutical companies find it challenging to predict the effect, the chances of a drug failing are much higher. A machine learning-based approach to identify a toxic compound that can lead to side effects can help save many resources before it goes into clinical trials.

Machine learning and artificial intelligence healthcare technologies, such as optical character recognition, image processing, and natural language processing, helps in converting data to uniform format from multiple systems and different sources.

But there are many algorithms in the market that have been developed in a vacuum. The clinical needs have to be matched with the technology in a way that enables solutions to be integrated into the clinical workflow.

If doctors are going to use tools in practice, they have to understand a little about how they work, especially where they might go wrong and where they might fail.

For instance, deep learning, a kind of complex machine mimicking the human brain functions, is being used in medical imaging and radiology. Using neural networks, which learn from data without supervision can recognize, detect, and analyze cancerous lesions from images.

Machine learning and artificial intelligence in healthcare can detect faster processing speed for detecting anomalies in images beyond what the human eye can detect.

Future advancements in machine learning will keep transforming the clinical field.