US Sift MD raises $670k to improve healthcare providers revenues with AI

US medical analytics startup Sift Medical Data has raised $665,000 in seed funding to further develop its AI-powered revenue cycle management system, the company said.

The round was led by The Winnebago Seed Fund investment fund, specializing in early-stage startup funding throughout Wisconsin. The investment is reportedly the first funding carried out by the fund. Additional investments also came from Jeff DeAngelis, President of Northwest Passage Capital, based in Sift MD’s home town Milwaukee.

“The financing will be used to expand Sift MD’s data science team and will allow for further investment in its proprietary healthcare analytics platform”, the company said in a press release.

Sift MD uses artificial intelligence and deep learning to help medical institutions improve their revenue cycles.

More specifically, the company is focused on denials management and improving healthcare providers’ bill write-offs.

Sift MD claims to be developing “the first artificial-intelligence driven and vertically integrated platform for Revenue Cycle Management (RCM)” in the healthcare industry.

The platform uses medical data from different sources and applies, machine learning and a proprietary text mining technology to uncover “key data points that enable revenue cycle management companies to reduce insurance claim denials and increase patient collection rates”, the company explains.

Thanks to these technologies it can identify patients who are most likely to come verses such that are unlikely to come. The company can then alert providers of potentially problematic bills well in advance.

SiftMD’s platform visualization. Credit: SiftMD

In a similar fashion, companies are using AI to identify potentially risky customers across multiple industries.

US company Argyle Data for example is using AI and machine learning to predict how likely telecom clients are to default on their bill payments in the next 60 to 90 days.

According to Sift MD, its technology can identify the 20% of patient visits that are most likely to result in writing off a portion of the bill. Those 20% represent 75% of the write-offs in nominal terms.

The company then uses deep learning and text mining to further segment those 20% to identify the top 5% of the visits that account for 68% of all the write-off dollars.