Digital Intern®

Consistent Approach. Individualized Care.

Frequently Asked Questions

Is the Digital Intern® FDA approved?

We’ve worked with the FDA and determined that the Digital Intern® is a clinical decision support system which does not currently require FDA approval or clearance.

Can the Digital Intern® integrate with… ?

Probably! We have standard HL7 interfacing capabilities which allow us to connect with other systems using these standard specifications which include most electronic medical record systems. We’ve also successfully created custom interfaces to connect with a few electronic medical record systems used by OPOs that do not have HL7 capabilities. We’re always open to working with you to integrate with the systems you already use whenever possible. The Digital Intern® is most useful when its integrated into a provider’s existing workflow.

Can the Digital Intern® help with problems related to therapeutic duplication?

Yes! The Digital Intern®’s consistent approach can help resolve issues with non-specific orders that cause issues with therapeutic duplication while still ensuring each patient’s individual needs are met. Your providers will answer a few quick questions about their goals for the patient when placing orders, but can rely on the Digital Intern®’s algorithms to help select appropriate medications at the right time. Learn more here.

How much does the Digital Intern® cost?

The Digital Intern® comes in many different formats but our goal with pricing is always to ensure that the benefits significantly outweigh the cost of the product.

Our products for hospitals are priced per module and based on the size of the hospital (bed count). Substantial discounts are available for organizations willing to share data and feedback with us. Please contact us for pricing specific to your organization and the modules you are interested in using.

The Digital Intern® for Organ Procurement Organizations is priced based on the size of the OPO (using the number of donors to represent size) and substantial discounts are available for organizations willing to share data and feedback with us. Please contact us for pricing information specific to your organization.

Our Educator Applications are available in the Apple App store for $9.99.

My organization hasn’t purchased the Digital Intern®. How can I see it in action?

You have several options!

First, check out our web demonstration tools. These tools require you to enter information that we would usually pull automatically from an integrated medical record system, but with a little quick data entry, you can see the types of recommendations the Digital Intern® is capable of making.

Similarly, we have educational apps available in the Apple app store. These also require some quick data entry, but let you see the results from a few modules that aren’t available in our web demonstrations. Please note that both the web demonstration tools and the educator apps are not for use with actual patients or patient data. The data entered is not protected and you must not use either the educator apps or web demonstration tools in place of your own medical decision making and judgment (see the full terms here).

You can always contact us to setup a presentation at your site. We will be able to answer your questions about how the various modules work and how they can be implemented at your organization. We are available for presentations in person or remotely.

I think my organization could benefit from the Digital Intern®. How can I help bring this technology to my work place?

We are thrilled that you are interested in using the Digital Intern®! We will need to get in touch with leadership at your organization to help make this happen. Please fill out this contact form with as much information as possible and we’ll reach out to setup a meeting.

Does the Digital Intern® use Artificial Intelligence or Machine Learning?

Artificial Intelligence is defined as “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” The concept was developed by Alan Turing in 1950. There are many other definitions for AI however as the field of study is ever changing and expanding.

Machine Learning is a subset of artificial intelligence and is defined as “the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task.” With that definition however, a computer algorithm with predetermined weights, set by a priori knowledge, could be considered to have learned. Thus, a more recent definition has specifically addressed machine learning as a field of artificial intelligence that uses statistical techniques to give computer systems the ability to “learn” from data, without being explicitly programmed on how to interpret that data.

Often adaptive algorithms are synonymous with machine learning. However, not all adaptive algorithms need to have been learned by a machine that is analyzing data. If an algorithm takes into consideration specific parameters and one or more responses to at least one of those parameters is used to vary the output of the algorithm, the algorithm can be considered adaptive. As an example, if an algorithm considers prior responses to blood transfusions of multiple patients and predicts a transfusion amount will last for a presumed period of time for an individual patient, its adaptability may be very limited. However, if the algorithm uses patient specific prior responses and only those patient’s responses are used to predict a response to a transfusion, the algorithm becomes more adaptive. In this manner the algorithm does not give an overall result for all patients, it gives a result that is tailored for the individual patient. This has considerably more power to deliver on the idea of precision medicine.

If an algorithm is designed by a human rather than a machine, it is not truly considered machine learning. It is still artificial intelligence because there is intelligence built in to the system that can be communicated out of the system. Had the mathematical approach with its weights and general shape been chosen and optimized by a machine, then machine learning can be said to have been employed in the process. The Digital Intern® is adaptive and yields patient specific results but it does not truly involve machine learning.

What are the Digital Intern® algorithms based on?

The Digital Intern® makes decisions based on best practices in literature, human physiology, and cost savings strategies where appropriate. Decisions are generally not made in isolation and often multiple Digital Intern® modules will work together to address the needs of the patient.

How did you decide which modules to build?

The Digital Intern® algorithms were designed to address the needs of many areas of medical care. The decisions to build specific Digital Intern® algorithms were based on high yield medical cost reduction strategies that are realizable in a measurable manner, the need to meet specific CMS/JCAHO requirements, and to tackle problems where quantifiable data is recorded within the EMR and where the resulting calculations can yield a quantifiable result.

Where can I find iVMD’s privacy policy and other terms?

If your organization has purchased the Digital Intern®, terms will have been supplied through that transaction. Our privacy policy and the terms of use for our Educator Applications and web demonstration tools may be found here.