Breaking Down The Cost of Implementing AI in Healthcare

Where the industry is already facing challenges, from ineffective workflows to rising costs, AI will add more weight to the industry. But the return will be beyond monetary benefits. It can save thousands of lives and help address critical conditions that were previously impossible. 

Therefore, to successfully adopt and implement AI, it is important to consider every aspect that will raise the budget right from the start. Let us explore the key factors and explain where these costs typically lie. 

What is the Cost of Implementing AI in Healthcare?

Here are several factors that will require initial investment from your side. 

1.Initial Investment in AI Solutions

Whether you are developing AI healthcare solutions for hospital management or purchasing a chatbot for patient care, there will always be an initial cost. You must invest in the necessary hardware and infrastructure and hire AI developers. 

The cost of computing power, data storage, and subscribing to cloud-based services will increase the budget. 

2. Data Acquisition and Management

AI requires a huge amount of data to produce accurate and fast responses. To meet these requirements, you must hire data experts for cleaning, processing, and labeling data. Remember, quality data brings the best outcomes, so getting low-quality data services might be cost-effective, but they are a total waste. 

The cost will include data processing and acquisition. Each AI model requires a different set of data depending on where it is going to be implemented. For example, if your solution is an AI chatbot for patient assistance, then it requires training on data from EHRs and medical records. 

The type of integration and its complexity will add additional costs. Therefore, you must find the best data and AI experts who can handle the level of complexity and integration issues that might arise. 

3. Hiring AI Developers 

Without experts, you cannot enjoy the benefits of using AI in healthcare. They know how to make your AI solution best for your customers. From designing and training the AI model to optimizing its performance, AI engineers are experts at everything. 

The best part is that experienced professionals do not require training and project management from your side. Your AI partner will handle everything. They can also provide suggestions on how to improve the solution or find the best ways to reduce costs. 

Here are the key experts you must hire to develop AI solutions:

• Data scientists
• AI/ML engineers
• DevOps engineers
• Project managers
• UI/UX designers

Instead of hiring specialists for each role, hire the complete AI development team. This will reduce your costs and also the burden of managing the developers. 

4. AI Model Training 

AI solutions need training on fresh data to work smoothly. The cost of training and refining the model will depend on the dataset and complexity. For example, a basic model requires less data and is simple in its architecture, thus costing a few thousand dollars. 

On the other hand, a complex model for disease diagnosis or a more complex job will need an extensive dataset for model retraining. It will need more computational resources, increasing costs to $100,000. 

5. Infrastructure Cost 

What else is included? Infrastructure cost. It depends on whether you choose a cloud-based or on-premise solution. 

• Cloud-based: AIaaS solution requires monthly processing, storage, and feature usage fees. For example, a basic model built on a virtual CPU may cost less than $1000 per month, whereas a complex GAN model will be more expensive.
• On-premises: You can reduce costs by hosting AI applications on your servers. Simple and advanced models will range between $5000 and $100,000. 

6. Cost of Integration 

AI solutions need to work with multiple systems in healthcare organizations. Some systems might be decades old, while others use the latest technologies, like IoT. Regardless of their size and technology, integration is important to ensure AI solutions give optimal results. 

Then, there are third-party APIs that improve the functionality of AI solutions. Here is a breakdown of the costs that come with integration:

• EHR/EMR integration costs up to $10,000 or more. 
• Middleware and API development will cost the same (up to $10,000.)
• Customizing user interaction and output of the AI model will cost an additional $10,000.
• The cost of integrating the legacy system will go up to $35,000. 

Remember, this is only a tentative estimation; you must consult with an AI development company to determine the real cost. 

7. Implementation Approach

How you develop AI solutions also impacts the cost of AI in healthcare. There are following options to get started:

Off-the-shelf: Use this model if you urgently need an AI solution. It is pre-built and requires less investment, typically from integration and licensing fees. 

You can train the model on your data to customize its performance if needed. However, this will increase the expense of hiring experts for fine-tuning. Consider the size of the model in this expense, too. 

Built-from-scratch: Go fully custom if your AI health chatbot or solutions have advanced features or perform specific jobs. The initial cost will be $100,000 or more. With additional expenses like integration, fine-tuning, and data processing, expect the cost to rise beyond $500,000. However, a custom model will give more than you invest in.

8. Regulatory Compliance

The healthcare industry is heavily regulated, and every system within its scope must follow stringent regulations. AI systems must be built with these regulations in mind. Obtaining FDA approval to use AI solutions in healthcare organizations will further drive up costs. 

Is AI Worth the Investment?

Though implementing AI in healthcare is costly, its long-term benefits make it a sound investment. The improvement in clinical outcomes and patient recovery, streamlining of administrative tasks, and reduction in patient readmission rate will save more than you invest. 

Hiring AI experts will further ensure that your investment brings high returns and faster development. For the start, taking baby steps and making incremental AI initiatives is a good way to test the waters before you gain full confidence in this transformative technology.

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