In recent years, the number of healthcare professionals has dwindled, but the number of patients has increased. This could be for a myriad of reasons, but the issue remains that there are not enough doctors, nurses, technicians, and other healthcare professionals to handle the influx of patients. There’s an urgent need to lighten the load, and artificial intelligence might be the solution.
However, beyond the initial cost of acquiring an AI system, there are also several hidden costs that hospitals may not consider or are not informed about. It is essential for healthcare providers to understand what they are actually signing up for.
1. Data preparation
Hospitals store their data in many different ways. Whether it is stored in a file or in an electronic health record to automate access and streamline workflows, AI models need to have access to it if hospitals want to maintain detailed records. Preparing that data for AI training or operations requires costs.
AI is intelligent, but it requires specific input to perform accurately. Ensuring the data being provided is formatted and worded correctly for the AI to process, categorize, and store is crucial to its success as a helpful tool.
2. Model design and training
There are essentially two types of AI healthcare professionals purchase. Existing models designed specifically for healthcare are an option, and building and training a model is another. Both are expensive ventures, and the complexity of the information is a significant factor.
If hospitals are building one, the training required to make it operationally ready must be considered. Buying one right off the market might also require some additional training, and complex models built for more extensive information cost more.
3. Equipment incorporation
Healthcare facilities already have digital systems in place. One key and costly aspect of implementing AI is ensuring it integrates seamlessly into existing systems. This process takes time and is dependent on the scale of the venture. Simpler models may be easier to integrate, while more complex ones can be more difficult.
There is also often the added step of making the AI model easy for nurses, technicians, and other healthcare workers to use and understand. This is another lengthy process that requires time and money, as hospital staff must upskill to manage new technology and procedures. This incurs costs for training resources and may necessitate higher wages.
4. Regulation adherence
Most countries have laws and regulations regarding the sharing of personal information. For example, Japan has the Protection of Personal Information Law, and China has the Personal Information Protection Law. Both laws outline the regulations in place to protect individuals’ personal information from theft or unauthorized access.
AI models in healthcare store patient data, including test results, medical history, and other relevant information. Hospitals must adhere to the laws in place in their respective countries to ensure that violations do not occur once they start using AI, as this can result in fines and reputational damage. Sometimes, clinical trials and clearances are required, which can also be expensive.
5. System monitoring
AI is not yet advanced enough to handle itself independently, especially in a sensitive field like healthcare. Therefore, continuous monitoring of the systems is required to ensure accuracy and regulatory compliance. This requires the attention of designated healthcare workers, which will cost hospitals.
6. Security risks
AI is not foolproof, and accidents and issues occur. Sometimes, the system does not run smoothly and can make tasks more difficult for healthcare professionals who are now used to relying on the AI. This often results in workers using shortcuts or unapproved methods to keep the system running. This can create security issues and expose sensitive data, resulting in costly consequences for hospitals and other medical providers.
7. Renewal and usage costs
Subscription services and user fees that occur on a monthly or annual basis are another expense. Some models even require customers to pay a small amount each time they use them, which is often thousands of times daily. It is important to be mindful of these fees before purchasing an AI.
8. Reputational risks
AI can risk the reputation of a healthcare service if it makes mistakes or gives advice based on biased data. When patients use chatbots or virtual assistants, the risk of the system providing incorrect information and eroding patient trust can increase. Proper monitoring of these applications can help mitigate this risk, but it is essential to be aware of issues like this.
Is AI actually making healthcare more efficient?
There is an ongoing debate about how much time, money, and energy AI actually saves. After all, there are at least eight hidden costs associated with using it. Additionally, the time it takes healthcare professionals to implement and train on it might outweigh the potential to save time in the future. While AI often promises reduced processing time, it is essential to consider any additional expenses that may arise.
How to combat these costs
The first step in deciding whether to implement AI in a healthcare setting is to be mindful of both the hidden and obvious costs associated with it. Once hospitals and other facilities are aware of the actual expense, they can work on finding innovative solutions to limit the costs. That can involve choosing the lowest subscription service, creating AI algorithms in-house, or employing other similar methods.
Making informed decisions about AI in healthcare
Due to the influx of patients and the limited number of staff at medical facilities, the need for AI’s automated services has increased. While it may seem that the system’s initial cost is the most expensive, there are numerous hidden costs that accumulate over time, which healthcare professionals must also be aware of.
Zac Amos is the Features Editor at ReHack Magazine, where he covers business tech, HR, and cybersecurity. He is also a regular contributor at AllBusiness, TalentCulture, and VentureBeat. For more of his work, follow him on X (Twitter) or LinkedIn.
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