August 4th, 2025
2 min read
Silicon Valley tech start-up veteran Z Ziegler sees the future in the intersection of technology and healthcare. Her current venture – Elev8 Data – addresses the protection of sensitive health data, particularly Electronic Health Records (EHR), which have become prime targets for cyberattacks. There is a wealth of personal and financial information, from bank accounts to social security numbers, not to mention health history, that are stored in Electronic Healthcare Records.
Healthcare data has been historically a walled garden but new technologies, including AI, make this data more vulnerable to identity theft. At the same, researchers and startups want to be able to access portions of individuals’ healthcare information – recognizing the need to keep it secure in both directions – so it can be shared with standards bodies, researchers, or entities conducting clinical trials, without jeopardizing data privacy. The data must be kept secure but is also needed for all kinds of training models to improve healthcare.
This is where Ziegler’s company comes in. Elev8 Data uses technology to ensure that personally identifiable information in health records is safely de-identified, tokenized or masked when it’s shared with external bodies and then returned to the original provider.
We talked with Ziegler about the role of AI in transforming healthcare.
Bridges and Barriers
Ziegler was one of the inventors of the technology behind “Tap and Pay,” used in all kinds of point-of-sale (POS) systems around the world. Leveraging this same technology, healthcare providers can share sensitive data such as name, date of birth, and date of an illness: Elev8 Data uses AI to secure the data by tokenizing, masking or redacting it.
Ziegler explains that they might mask data and keep just an individual’s birth year or the first digits of their zip code, for instance, because it’s needed for clinical care to know if there are any environmental health issues. Or they would redact data by essentially stripping out critical information such as the social security number.
Tokenized data, meanwhile, is data that could be re-identified via a remote token vault so the name could be re-instated if the data owner wants to do that or if they designate a party to have access. Clinical trials are a great example: drug companies look for a certain patient profile through de-identified data lakes and then the hospital or the physician can re-identify the patient.
We can really unleash that data and look at important health initiatives, population health, social determinants of health, health equity. This is a way to really see the whole population, not just your own backyard.
AI & Society
AI in healthcare can significantly improve outcomes, such as precision medicine, by using data to predict and pre-empt diseases like diabetes or heart conditions. And, Ziegler says, partnerships with AI providers and healthcare distributors play a vital role in enabling broader, more effective data use across hospitals and healthcare providers. These partnerships are crucial in ensuring that the ethical implementation of AI doesn’t get lost in the push for innovation.
Ultimately, Ziegler’s view is that businesses in AI and healthcare need to foster collaboration with all stakeholders, from developers to healthcare providers, to ensure that AI is developed and used responsibly. This includes working together to create solutions that protect individual privacy, promote equitable access to health information, and lead to better healthcare outcomes powered by secure patient data.
