Theranos: Tracking a Startup in Trouble Through Public Data
By Rashida Kamal
Theranos, a medical technology startup born in the dormitories of Stanford University, had a meteoric rise and an equally spectacular fall.
To better understand how Theranos rose to prominence only to succumb to its present, diminished state, we traced the company’s footprint through public data and news reports. In doing so, we gained a nuanced view into the health of the company over time. While some of the information became publicly available only after hard-hitting news stories, the data provides valuable context around both Theranos and the potential risk associated with other similar companies.
During our research we uncovered additional valuable information sources that could, and should, be used in future analyses of companies and related risk.
A revolutionary startup becomes a cautionary tale
For insurance companies — and others — hoping to gain an understanding of rising medical startups, Theranos was an interesting case study.
Theranos founder Elizabeth Holmes hated needles. In 2003, she dropped out of Stanford to launch a startup that promised to revolutionize the way medical testing was conducted.
When she penned “How to Usher In a New Era of Preventive Health Care,” in mid-2015, the company was worth billions. Meanwhile, reports of inspections by the CMS and the FDA began to reveal that all was not what it seemed. By mid-2016, the company was in serious trouble — not only for failure to implement proper quality assurance, but also for potentially placing patients in harm’s way.
The Theranos study makes a strong argument in favor of leveraging multiple (and multiple types of) information sources to comprehensively analyze a company and its related risk. Often, data sources outside the usual filing and inspection reports reveal new—and often unexpected—insights and provide valuable context for existing information.
One such non-traditional—and oft underutilized—source is the Freedom of Information Act (FOIA) logs of regulatory agencies. In our case, there was a lot to learn from the due diligence efforts of news organizations, law firms, and in some cases, political campaigns.
The CMS’s FOIA logs showed that in the mid-2015, when Theranos began to attract additional attention from regulators, it also drew more attention from journalists. Though in and of itself, that attention is not necessarily indicative of any wrongdoing, it could be a signal for the companies who underwrite endeavors like Theranos to employ the same level of scrutiny.
In this case, the spike in attention came shortly before the regulatory deathblows and related reporting in 2016. Interestingly, in CMS’s 2016 logs, we also see that the McCain Senatorial Campaign Committee requested all communications between Senator John McCain (R-AZ) and CMS regarding Theranos.
In order to make sense of the facts that can be gleaned from a company’s public data footprint, it is important to not only consider the individual moments within a company’s history, but also to consider how these signals compare to the public data footprints of other like-companies. In the case of Theranos, the overwhelming amount of journalistic coverage provided additional context to the underlying public data footprint (that is present for all companies, big and small).
The challenge is not only to utilize as much publicly available information as possible, but also, to transform that information into insight by considering the larger universe of public data that continues to thrive—with greater fidelity—each year.
Theranos, due to its oddball nature and hopeful prospectus, highlights this challenge, and points to what could be possible with the right combination of data accessibility and analytic tools.
Seeing the big picture
FOIA logs are one of many public data sources that organizations (and curious individuals) can leverage to surface signals or gain added context. While a wide array of publicly-accessible information is available on companies like Theranos by sheer force of regulation, that isn’t the case for the 99.7 percent of businesses in the U.S. considered small-and medium-sized (SMBs).
Given the sparse data coverage on SMBs, it is often necessary to bring together multiple public data sets to get the big picture on these companies. Enigma offers comprehensive SMB data by algorithmically-matching companies across a vast repository of public data, allowing users to build holistic profiles of SMBs and surface valuable signals and new risk indicators.
Ready to learn how Enigma can help your organization access and unlock new value from public data? Get in touch.