In the past few years, the healthcare industry has leveraged artificial intelligence to create many success stories.
From vaccine development and treatment designs to even using AI for healthcare system analysis, AI has revolutionized healthcare in unimaginable ways.
Apart from this, AI has also been instrumental in reducing unnecessary emergency department visits, saving an estimated $11 billion for US hospitals.
The results of numerous optimistic studies, observed growth, and improvements in productivity and efficiency have definitely brought on the AI fever. Investors are frantically looking for opportunities, and the pandemic has only exacerbated their appetite.
However, let’s take a step back.
While AI has a wide range of applications in healthcare and investors waiting in line to fund the next innovative solution, AI adoption is easier said than done.
A thorough look at the current state of healthcare systems reveals that, in reality, the adoption of AI-based solutions is still lagging behind.
The truth is that most healthcare systems would need to undergo a complete digital transformation before they could implement AI into their ecosystem. And this is just one hurdle.
Beyond this, there are significant challenges of AI in healthcare.
Fortunately, with careful consideration, innovation, and strategy, healthcare systems can be equipped with the capability to overcome these challenges.
So then, let’s take a deep dive into four of the most pressing challenges of AI in healthcare. This article will also extensively cover how these challenges can be mitigated.
The four major challenges of AI in healthcare and their solutions
Challenge: Consolidating valuable data for training AI systems
Artificial intelligence-based systems need vast amounts of data because they train on highly relevant data in order to produce accurate responses. And this is one of the biggest challenges of AI in healthcare.
Unlike a number of other industries, consolidating relevant data in healthcare is no easy feat. No two patient experiences are the same, which also means that the healthcare data available is erratic in nature.
This is because the healthcare approach taken by different medical institutions as well as medical staff to a single healthcare concern can be radically different.
There is no standardization, too many variables, and higher complexities, making it difficult to capture optimized data to train your AI systems.
Obviously, this is a major setback.
Now, the problem with healthcare data is not a lack thereof. In fact, in the US alone, over 1.2 billion clinical documents are produced annually, and this number keeps growing by 48% per year!
The challenge is to improve the data quality and standardize this data.
For instance, say there are two patients, Patient A and Patient B.
Both patients are due for their annual health checkups, and they go to two different diagnostic centers.
Patient A goes to diagnostic center A, and Patient B goes to diagnostic center B. Now, diagnostic center A records their measurements in milligrams per liter (mg/L). On the other hand, diagnostic center B records their measurements in milligrams per deciliter (mg/dL).
This is a minor yet relevant example of how different diagnostic centers use different units to denote the results of the same test. This discrepancy extends into how different healthcare systems use various terminologies, medical symbols, and more. Ultimately, AI systems can barely recognize any and all available data. This is due to a lack of uniformity and unstructured formats.
So how can we solve this dilemma?
Solution: Creating transparent algorithms through data management
The first step to resolving the challenges of AI in healthcare is to focus on standardizing medical data. This means defining clear rules while recording patient data, using globally recognized terminologies and medical symbols, etc.
The easiest way to do this is by ensuring the digitization of medical records. Digitization with a defined set of rules with respect to the use of medical terminologies, coding values, and symbols can create unified data even across different healthcare platforms.
Another important aspect of resolving the challenges with data is to ensure proper data management. It enables healthcare providers to meticulously analyze patient records to create inferences and draw out valuable insights.
This, in turn, helps in improving healthcare outcomes and, of course, creates more accurate databases.
Data management and standardization can help healthcare systems across the country to eliminate siloed data. It can also help develop transparent algorithms , which can help train AI systems.
“The AI algorithms are only as good as the underlying data,” Dr. John Halamka, MD, MS, President – Mayo Clinic
Challenge: The lack of technical know-how
Despite the advancement of artificial intelligence, most healthcare organizations lack the technical expertise to implement AI-based solutions.
What is required to drive better outcomes with AI-based solutions?
Most organizations are unclear on the objectives, use cases, and even the infrastructure needed to achieve this.
Consider a simple use case: Using conversational AI-powered chatbots as the first point of contact between a patient and their healthcare provider.
A simple yet effective use case of an AI-based solution. It can cut down on overhead costs as well as free up human agents from redundant tasks such as collecting patient information.
Even this small use case has extensive technical requirements. That is why a number of smaller healthcare practices tend to shy away from realizing the benefits of artificial intelligence.
Suggested reading: Top 5 use cases of chatbots in healthcare
Solution: AI solution providers
The proliferation of AI means that today there is a higher number of solution providers that can build and execute AI solutions at highly affordable costs. Many of these have highly efficient, easy-to-use automation solutions that can improve the patient experience.
For instance, take Kommunicate. A conversational AI chatbot solution, Kommunicate enables healthcare organizations to build customized conversational AI solutions. These solutions can seamlessly automate the healthcare experience.
Their chatbot builder uses a drag and drop function to equip your chatbot with all the required features using zero code.
Challenge: Misconstrued perceptions about AI in healthcare
One of the biggest misconceptions propagated about AI is that it will replace human involvement in core functions.
This is among one of the biggest challenges of AI in healthcare since it interferes with how healthcare staff engages with AI-based solutions.
Artificial intelligence has massive applications in healthcare, but until they are used efficiently, it can be quite difficult to define its ROI. This, in turn, slows down the deployment of AI in healthcare.
However, this is far from the truth. AI is simply an efficient way to aid human agents in increasing their productivity by cutting down on redundant tasks. This frees up a lot of time for live agents to tackle highly complex tasks and focus on delivering high-quality healthcare.
Solution: Invest in training healthcare staff and hiring the right talent
With the kind of benefits that AI brings to healthcare, there is little doubt that a higher number of healthcare institutions will deploy AI solutions within their ecosystem in the coming years.
The only question is how well can your healthcare staff leverage these solutions?
Recent research by Gartner states that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them.
Clearly, enabling your teams with the right set of tools so as to make the most out of your AI solutions is the way to go. This includes detailed and structured change management and digital transformation initiatives.
These initiatives range right from the highest level of hierarchy in a healthcare business right down to the person using these systems.
Healthcare staff need consistent and periodic training to enable them to completely understand how they can leverage artificial intelligence to their maximum potential.
Challenge: Concerns about the lack of privacy
Data privacy is a huge concern across the globe.
And with the vast amounts of data required to train AI systems, privacy is another one of the major challenges of AI in healthcare. This is simply because healthcare data contains highly sensitive data, including payment information, personal information, medical records, etc.
Any leakage of data could put thousands of people at risk posing a significant hurdle to deploying AI in healthcare.
Solution: Privacy-enhancing systems and compliances
To curb the issues associated with data privacy, healthcare systems need to have the required compliance certifications in place.
All systems need to be GDPR and HIPAA compliant to ensure that there is no data leakage.
Additionally, healthcare systems also need to invest in privacy-enhancing technologies such as data masking, encryption, secure multi-party computations, etc. This will help protect highly sensitive data.
Clearly, there are a number of challenges to AI in healthcare. However, with AI presenting innovative and intriguing opportunities in critical areas of healthcare. With areas such as drug development and treatment, one cannot ignore the future of AI in healthcare.
Chatbots are an interesting application of AI. If you want to read more about how chatbots are going to affect the medical industry, follow this link.
The way forward for AI in healthcare is for medical ecosystems to invest in data management and privacy. Investment should also be made in automation expertise, and strategic digital transformation.
It is also key for organizations to understand individual use cases and objectives and implement AI systems that are precisely aligned to their specifications.