Venture: Trying to Fix Healthcare
Nym Health
The Israel-based company Nym Health has recently raised another $16.5 million to start rolling out their new healthcare solutions. Nym provides an auto-machine learning tool for hospital billing, which has proven to be a necessity amidst a plague of misbilling in patient accounting.
Nym was able to confirm new financing from high-end investors, such as GV (Google Ventures). GV is a useful partner, and will allow the company to expand its technology development and marketing programs. With a company like Google on your side there are few limits, such a backer can provide Nym with myriad resources and connections.
There are money problems in the US healthcare system, and billing proves to be a large factor. Though the endless bureaucracy of healthcare makes it difficult to isolate the root of the issue. When most patients go to the hospital, regardless of insurance status, they are treated, and payment is dealt with after the patient is in a more stable condition.
Doctors must correspond with the billing department to make sure that patients are properly billed and their insurance status considered. This is where problems arise. With many doctors short staffed, it becomes a game of telephone regarding billing. This leads to over or under charging, and puts patients in difficult situations. These situations are not resolved quickly either. It can take weeks for patients to receive initial bills, and even longer to rectify faulty ones.
Nym has developed software that is able to convert medical records and charts to a uniform format, then apply the necessary billing codes automatically. Nym Health makes use of natural language processing algorithms to allow for usability across various platforms. This speech processing system is designed to understand medical terminology, and then determine the correct cost for a given procedure or examination.
Nym was founded in 2018 by two ex-Israeli military officers, Amihai Neiderman and Adam Rimon. Both have worked in the medical field and wanted to use their experience to address larger systematic issues. Neiderman and Rimon want to make medical charting understandable to all, “Because of our approach we can generate full audit trails, we can explain how we understood everything in patient charts.”
Nym’s autonomous coding is different from most, it requires no human intervention and is run completely by artificial intelligence. Removing any human interaction will allow for a steep decrease in mistakes and increase efficiency. Nym’s artificial intelligence can decode and translate the physical language inputs into universally applicable outputs.
Nym Step by Step
Doctors or medical scribes document the patient visit
Nym identifies any inaccuracies and assigns medical codes to the language
If any part of the medical chart is not understood, it goes into the coders queue for further action
Though it sounds simple, the technology behind is very complex. Amihai stated “At the end of the day, our goal is to get an accurate code we can explain. That helps us stay focused on developing our technology and raise funds, which is important as Nym goes to market and we review our analytics and feedback. Explainability is also essential for startups who want to secure FDA-approval for healthcare technology that is more than an assistant.”
This pandemic has revealed many flaws in the healthcare system and consumers are demanding change. The time of transparency is upon us, and patients will no longer stand idly by receiving atrocious inaccurate bills.
Overall, Nym is a great solution to a persistent issue. However, Nym cannot address all problems with billing. One being how doctors can take advantage of patient’s trust and ‘pad’ bills. These doctors rightly assume many patients will not question their bills and simply pay up.
Regardless, Nym will open a gateway for many more solutions to come, and could possibly increase patient knowledgeability and medical fluency. A product like Nym’s is long overdue and the pandemic has proven to be a catalyst in its development.