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Asking ChatGPT about the Economics of Itself in Healthcare

A robot stands in contemplation in front of a chalkboard full of algorithmic formulas

I’ve become very curious about how healthcare enterprises will pay for generative AI platforms and initiatives like ChatGPT. There was a similar haziness around deployment costs when healthcare began to migrate from private data center servers to various forms of the cloud (whether private, hybrid, or public).

So I thought there would be no better source than generative AI to see how it saw pricing models for itself.

So the question was simple: How do healthcare enterprises pay for generative AI?

And ChatGPT responded with the following:

Enterprises typically pay for generative AI through various models, depending on the specific use case and arrangements with AI providers. Here are some common payment models:

1. Licensing: Enterprises may enter into licensing agreements with AI companies to use their generative AI technologies. Licensing fees can vary based on factors such as the scope of usage, the number of users, and the desired level of support. These fees may be one-time or recurring, such as annual or monthly payments.

2. Subscription-based: Some generative AI platforms offer subscription-based pricing models. Enterprises pay a regular fee, typically on a monthly or annual basis, to access and use the generative AI services. Subscription models often provide flexibility and scalability, allowing organizations to adjust their usage as needed.

3. Usage-based: In this model, enterprises pay based on their actual usage of the generative AI technology. The pricing is determined by metrics like the number of API calls, the volume of data processed, or the computing resources utilized. This approach allows businesses to pay according to their specific needs and can be suitable for fluctuating workloads.

4. Customized agreements: For larger enterprises or complex projects, customized pricing agreements may be negotiated. These agreements are tailored to the specific requirements of the organization, taking into account factors like the scale of deployment, additional services, exclusivity, or special support needs. Pricing is typically determined through direct negotiations with the AI provider.

5. Consulting and services: Some enterprises engage AI providers to offer consulting or professional services alongside the generative AI technology. These services may include customization, integration, training, or ongoing support. The cost structure may involve a combination of upfront fees, hourly rates, or project-based payments.

It’s important to note that pricing models can vary among different AI providers and may be influenced by factors like the sophistication of the generative AI technology, the industry vertical, the scale of deployment, and the level of ongoing support required. It’s advisable for enterprises to reach out to AI vendors directly to discuss their specific needs and obtain accurate pricing information.

A computer screen open to ChatGPT.

Now I’ve admittedly been skeptical of asking ChatGPT for advice ever since it confused me with professional golf champion Ben Crenshaw. Indeed, not just a misspelling error for Frank Cutitta!

However, as someone who has spent the better part of his recent career analyzing the neuroscience of healthcare technology buying, I was humbled by the AI output and even more how it suggested that a ‘human vendor” be contacted for pricing specifics.

As with any technology, cost-benefit analysis has both qualitative and quantitative implications. Given that it’s not a sentient algorithm, the qualitative and emotional domains have not been one of ChatGPT’s strong suits.

So it will be interesting to track how these platforms are used to make considered purchases usually done by human buying teams. Or there may be an evolution to algorithmic buying bots that need to be supplemented by humans with more emotional buying triggers. We shall see.

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