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Mobile phones, smart watches, wearable health gears… there is avalanche of information and data about people’s health and activities today that can be leveraged. The data can be used to have more a clear idea of a person’s health and fitness needs. Hospitals and healthcare providers have a huge opportunity in front of them to leverage this data and analyze it to make significant improvements in patient care quality.

The augmented systems in hospitals and healthcare clinics do a lot of machine-to-machine data transfer. This not only makes patient care better, but faster too. It lets healthcare providers to decide the case priorities and improved patient outcomes in a more efficient and timely manner.

However, the sheer volume of data often makes this task every difficult.

The Promise and the “Scare” of AI

AI and machine learning here to help handle this vast volume of data in an efficient manner. However, healthcare providers are particularly reluctant to take advantage of this opportunity. The reasons can be many, including the cost implication, the fear of taking the human element out of medical judgments by using analytics and data, the fear of being replaced by a machine. But whatever the reason may be, the adoption process of AI in healthcare has been slow, and these systems are still far behind in harnessing the power of AI.

In some cases the workforce is just not skilled enough for the new technologies. For the ability to start the acceptance of AI in practice, the need is to start with small project that can show quick results and grow from there. The change will offer better patient care outcomes and more streamlined care processes. It is beneficial in the long run and pays off the setup cost.

Being Future Ready

However, despite the reluctance to accept AI in healthcare, this transformation is inevitable, as we are inching closer to a more data driven world every day. It is definitely necessary to become ready for this future state that will be upon us very soon.

What being “future ready” means is having a data foundation strategy in place. Data is the building block for AI tools and technology. The entire organization needs to ensure that they capture high quality data and relevant information, cleanse it and organize and store it in a way that makes it easier to use for analytics. It is the most critical part of the success of AI applications. High quality data will be critical for quality insights. It is a process that will take effort, time and money but will result in far more improved patient care.

Healthcare organizations must also begin training the staff and become ready for the changes as they occur. The competence of the staff and readiness of the organization will make a huge difference in smooth transition to a more data driven approach.