6 Strategies to Optimise AI Costs in Healthcare

The adoption of AI in healthcare has been steadily increasing in recent years. This adoption is being driven by the technology’s proven capabilities to deliver better patient outcomes, more personalised relationships with patients and third-party providers, streamlined workflows, and higher revenues. 

Take these statistics for instance. In 2022, Deloitte released a report in which it revealed that integrating AI into the European healthcare system can help hospitals save up to 212.4 billion euros in expenses, and save up to 403,000 lives.  

In the UK, the NHS (National Healthcare System) has taken active steps to incorporate AI into the healthcare system. For example, in April 2019, NHS has formed the NHS AI Lab to find mechanisms to implement existing AI technologies into the healthcare system to improve patient care. 

Today, there are five key areas where AI is actively being adopted in the industry. 

• AI is being used to analyse medical images like MRIs, CT scans, and X-rays to detect abnormalities that human radiologists can miss at times. AI has proven to deliver quicker, more accurate diagnosis, which can in turn result in faster treatments.  
• AI is being used by doctors and healthcare professionals to detect and diagnose diseases in time. For example, AI uses machine learning technologies to analyse vast sets of data to arrive at patterns that can predict a likely disease or illness even before it happens.  
• AI, with its machine learning capabilities, is being used to accelerate the drug development lifecycle. For example, AI has the ability to identify potential drug candidates more quickly and accurately than traditional means. 
• AI is being used to analyse patient history, characteristics and other patient data to enable doctors to come up with the most effective and comprehensive personalised treatment plan.  

• AI is analysing patient data in real-time to empower doctors to provide an accurate diagnosis and treatment recommendation for patients through virtual consultations and telemedicine. 

A Merit expert says, “Despite its advantages, AI adoption is also seeing a fair share of challenges; like data security risks, non-availability of a wide spectrum of streamlined data to train AI models, lack of trained personnel to use and implement these technologies, the cumbersome process involved in incorporating it into existing systems, and lastly, the cost of implementing AI in healthcare.” 

While each of these challenges are equally important to be addressed, in this blog, we’re going to specifically look at the cost factor, and best practices to work around it. 

Understanding the Cost of AI in Healthcare 

Healthcare is such a dynamic industry that it is necessary for organisations in this space to adopt custom AI solutions that meet their unique needs. Data by Analytics Insights reveals that the cost of a custom AI solution can be anywhere between USD 20,000 to USD 1,000,000.  

Typical costs involved when implementing AI in healthcare are; 

• Development costs, which include investing in hardware and software, and hiring expertise to use an AI system. For example, the hospital may need to hire a team of data scientists, data engineers and technical staff to keep the AI system relevant, active and functioning.  
• The more the data that is fed and trained into the AI system, the more accurate the predictions are. But, acquiring, streamlining and maintaining the data can be an expensive affair.  
• Integrating AI with an existing healthcare system can be complex and time-consuming, and result in additional expenses related to development, testing and deployment. 
• AI requires continuous maintenance and updates to remain accurate and relevant. This means, spending on additional resources like technical staff to keep the system running.  
• AI technologies require specialised training and expertise to be operated at full capacity. The cost of training the staff to learn and interpret AI readings can be costly.  

Like we said earlier, even if a healthcare organisation opts for a custom solution, rather than an off-the-shelf solution, the above costs can make it cumbersome, especially for smaller organisations to implement. 

6 Strategies to Optimise AI Implementation Costs in Healthcare 

There are a number of best practices that healthcare organisations can adopt to optimise AI costs; 

1. Instead of taking on a large, complex solution, organisations can start small and scale up gradually, based on current requirements. 

2. Organisations can focus on implementing AI applications which are likely to make the biggest impact. This can also ensure more effective allocation of resources.  

3. Instead of completely investing in new systems and technologies, where possible, organisations can leverage existing data and infrastructure 

4. Being a highly interdependent ecosystem, healthcare organisations can collaborate with other industry players like vendors and data scientists to share the cost of AI implementation. 

5. Healthcare organisations can put together a strong healthcare governance framework to ensure that data is acquired and used ethically. This can mitigate cost and risk of potential data breaches.  

6. A cost-benefit analysis can help organisations understand where AI is necessary, how much it costs to implement and maintain it, and the weightage of benefits they can reap from it. This will ensure that they only implement solutions that are most beneficial. 

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Our data scientists understand your data needs and create customised tools to deliver the right data in the format you need. They scale up and scale down the data collection process based on your business needs, and validate data quality before it is used for analytics and decision-making.  

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