[ad_1]
After working as an incident investigator for risk management firms such as Kroll and FTI Consulting, Aaron Narva (pictured above) was working for a large international bank client at compliance software maker Exiger. He was responsible for monitoring this client’s legal compliance after it made headlines a decade ago due to a money laundering scandal.
“While I was at Exiger, we acquired some software companies, including an AI software tool that helped pull risks from unregulated public records. We built a tool to help identify corruption and sanctions risks in the business relationships of large companies,” Narva told TechCrunch. very”.
This work gave him an idea Conflixis. Hospitals and other large medical practices face similar risks of corruption as banks. Pharmaceutical companies and device manufacturers have become so cozy with doctors that doctors are required to disclose conflicts of interest: trips, consulting fees, sponsorship of research grants, and the like.
a lot Research shows Those who are too friendly are more likely to prescribe those medications and devices, whether or not they produce better outcomes for the patient. The risk is so great that the government runs a database called… OpenPaymentsData.comwhere anyone can see conflict of interest disclosures.
However, revealing such conflicts does not stop the problem, putting hospitals in legal jeopardy. A body of laws prohibits such behavior by doctors, everything From the Stark Law to the Anti-Kickback Statute (AKS).
At the same time, commercial interests need to work with doctors – medical experts – to help them research new drugs and build devices. So not every interaction is forbidden.
Narva envisioned an AI-powered software as a service that would identify for hospitals and major medical practices actual situations that put the hospital — if not the patient — at risk.
“A large health system may have 200,000 relationships between its doctors and vendors and suppliers,” Narva said. “Which of these 200,000 relationships affect you from any one of these six similar risks?”
The risks range from violating laws to unfavorable medical outcomes. The federal government too Provides a database Which publishes information about the quality of care in the hospital.
Narva called a friend he had known since eighth grade, Joseph Bergen, BuzzFeed’s director of engineering at the time, to ask Bergen what he thought about the idea. Bergen loved it so much, he quit his job and became a co-founder.
Conflixis works by ingesting data from OpenPaymentsData.com, hospital purchasing data, claims data, patient outcome records, conflict of interest forms, and other sources. It analyzes all conflict points to determine which points the hospital should investigate.
“Well, we looked at all 5,000 or 10,000 relationships (and these are the seven you actually need to look at),” Narva describes as an example. “Just like we boiled the ocean and here are the seven.”
Conflixis takes it a step further and can also predict hospital spending and suggest ways to reduce it. For example, would a hospital purchase a more expensive piece of equipment based on the recommendation of a physician with a relationship with that vendor, rather than purchasing a less expensive piece of equipment?
“We can make hospitals significantly reduce their regulatory risks, increase their trust and transparency with their patients, yes, but also make better operational decisions about what they buy,” he says.
The company, founded in 2023, already has a handful of clients, with revenue of less than $5 million, Narva said. It just announced a $4.2 million seed round led by Lerer Hippeau (the fund founded by former BuzzFeed CEO Kenneth Lerer) and Origin Ventures, with participation from mark vc, Springtime Ventures, and seed investor Cretiv Capital.
Conflixis joins a crowded field of health industry compliance software companies like Compliatric and Symplr, though some are more focused on protecting patient data than on corruption and procurement.
Narva says what sets Conflixis apart is the way it has married its employees’ investigative work careers with LLMs. I modified off-the-shelf models to look for patterns in the data based on “our backgrounds in transaction monitoring and corruption in big data investigations,” he says.
TechCrunch has an AI-focused newsletter! Register here Get it in your inbox every Wednesday.
[ad_2]