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Top 3 Challenges to AI Success in the Healthcare Industry

Recently, there has been significant progress in the healthcare and Artificial Intelligence industries. In healthcare alone, the AI market is estimated to be a $10 billion industry by 2024. To give you an idea of magnitude, health spending in the US alone represents 17.1% of GDP, globally the number is a little a smaller at 9.9%, according to World Bank data.

 

 

The figures paint a clear picture: this industry is ripe for digital transformation with the help of AI. Earlier this year, Accenture released a report on the digital health tech industry and the number one trend was listed as “AI is the New UI.” As the health is something that impacts us all, it makes sense to find new ways of applying pre-existing technology to all facets of the health industry.

 

Current State of AI and Healthcare

A common example we hear about technological developments in health is diagnosis support for doctors. This is an example where significant progress has been made, with the help of AI, to provide analytical support. However, the healthcare industry encompasses many fields, like pharmaceuticals, medical, IoT, healthcare/insurance, and patient care industries to give a few examples. When dealing with such a diverse industry, there are an abundant amount of use cases that have yet to utilize new technological solutions.

For example, we are working with one of the leading international healthcare company, helping them to achieve their goals by automating the writing of certain data-driven reports with the help of Natural Language Generation (NLG). As a result, they’ve been able to automate 80% of the work with 100% accuracy in certain sections of reports, freeing up employees to focus on more creative, non-routine tasks.

This particular example is still relatively unique. So, the question still remains, why are parts of the healthcare industry slow to adopt new technology?

 

Pre-Existing Challenges in Healthcare

There are three main challenges that are unique to the healthcare industry, making it difficult to adopt new technology:

  1. Lack of Digitalization: due to legacy systems, paper and physical documentation used to be a fundamental part of the industry. Today, digitization of medical data is going to be a critical step to create a foundation for the industry to move forward in this technological world.
  2. Regulatory Demands: similar to finance, the healthcare industry is highly regulated. It’s crucial to adopt technologies that can incorporate, apply, and adapt to regulatory constraints.
  3. Security Concerns: Data breaches are a constant concern among many industries, and the health industry is no exception. Pharmacies, hospitals, and laboratories are all examples of places where data breaches can take place.

In order for AI technology in the healthcare industry to be adopted, tech companies must develop solutions that can address each of these concerns.

 

Today, it’s as if there are two worlds existing in the same industry. There are areas where so much progress has been made with regards to embracing new technological advancements and incorporating automation into existing business processes. However, there is still another part, where the digital transition is occurring at a much slower rate. It’s clear that the health industry will be transforming drastically over the next few decades.

Download this e-book to learn more about the different types of analytics (descriptive, predictive, and prescriptive).