Fueled by an abundance of cheap processing power and a never-ending deluge of data, AI and machine learning are accomplishing amazing things for organizations around the globe. Unfortunately, a few of the industries that stand to gain incredible benefits from these advanced technologies are also highly regulated, adding friction to what can already be a complex implementation.
Healthcare is the posterchild of a heavily regulated industry, and organizations in the United States have had to handle protected health information (PHI) in accordance with the Health Insurance Portability and Accountability Act (HIPAA) for almost 25 years. Today, however, regulations on all sorts of personally identifiable information (PII) are converging, including Europe’s General Data Protection Regulation (GDPR), Singapore’s Personal Data Protection Act (PDPA), and many others.
While regulations are commonly focused on inhabitants of a specific area, accurate AI models require large data sets that are diversified in terms of the age, gender, race, ethnicity, and geographic location of their subjects. That means companies hoping to offer the next generation of AI solutions to healthcare providers must jump through an equally numerous and diverse range of regulatory hoops or risk creating tools with built-in biases that contaminate results.
De-Identifying the Data
Coming up with enough data to effectively “teach” AI takes time, and de-identifying that data to ensure the protection and anonymity of its owners can be an even bigger undertaking. That’s why Shaip offers licensed healthcare data that’s designed to help construct AI models — including text-based patient medical records and claims data, audio such as physician recordings or patient/doctor conversations, and even images and video in the form of x-rays, CT scans, and MRI results.
Let’s discuss your AI Training Data requirement today.
Our highly accurate API solutions ensure that all 18 fields (as required by Safe Harbor Guidelines) are completely de-identified and free of PHI, and Expert Determination with Humans in the Loop (HITL) ensures that nothing can fall through the cracks. Shaip also includes medical data annotation capabilities that are crucial for scaling a project. The annotation process involves clarifying the project’s scope, conducting training and demo annotations, and a final feedback cycle and quality analysis that ensures the resulting annotated documents meet the given requirements.
By utilizing our cloud platform, clients gain access to the data they need in a medium that’s secure, compliant, and scalable to meet any demand. In cases where a manual data exchange is undesirable, our APIs can often be integrated directly into a client platform to facilitate near-real-time access to both the data and de-identification APIs
Building AI models is difficult enough without having to source your own data sets, which is why it’s almost always better to outsource this labor-intensive task to a dedicated provider. Our team of dedicated de-identification transcriptionists are highly trained in PHI protection and medical terminology in order to ensure the delivery of the highest quality data. In addition to saving time and money, you also avoid potentially crippling penalties that can accompany the mistaken use of non-compliant data.
To help you determine if Shaip is the partner you’ve been looking for, we offer a variety of sample data sets that you can use to begin training your algorithms today. We hope you’ll join us and watch your AI initiative take off.