Top 5 Challenges to Sharing Medical Data

We have become experts at using data for convenience.

We rely on location data to hail an Uber, use banking data to pay our friends in seconds, and Google auto-completes our questions as we type.

Sharing data in healthcare, however, is not as easy or convenient. Patients are forced to recite their medical history for the hundredth time to a new doctor. And a lack of data sharing in healthcare means a longer time to medicine for all patients. 

Innovation that benefits humanity is put on pause when researchers cannot find and access the data they need. But these problems are not caused by a lack of data – it’s quite the opposite. 

The healthcare industry is one of the biggest generators of data worldwide, accounting for an astounding 30% of all global data. Medical data increased by nearly 1000% percent in recent years, reaching an incredible 2,000 exabytes in 2020. 

This wealth of data could potentially solve some of humanity’s most pressing issues, yet 80% remains completely untapped and unutilized.

Sharing medical data can save lives. Healthcare data is essential for longitudinal care, ensures that caregivers can access complete patient information, and is vital for drug research and development. 

So, what is stopping us from translating data into priceless insights and meaningful action? Let’s look at the top five obstacles slowing down data sharing in healthcare and explain how to expedite data partnerships.

Collaboration is significantly easier when you share a common language. But when it comes to data, health organizations seem to all speak a different language, making it extremely difficult to share effectively.  

In the United States healthcare system alone, thousands of different IT platforms are used to collect and store medical information – each with different standards and privacy controls. One way to organize this data is with FHIR standardization, but many health systems still do not adhere to these standards.

Less than 40% of health systems can successfully share data with other organizations, and one in four U.S. patients report that their medical records were not transferred between providers in time for their appointment. This holds true both within and across organizations. 

Large hospitals scatter patient information across systems, leading to clinicians and researchers being forced to work with incomplete information. 

This paints a troubling picture. Medical information is siloed across many different databases, which compromises the quality of care. It also severely limits the scope of global medical breakthroughs that rely on requiring rich and diverse datasets for verified findings.

The healthcare industry is subject to over 600 regulations regarding data privacy and security – so inaction can be a tempting choice in the face of noncompliance. 

Most health systems have tokenized their data to keep up with regulations and protect patient identity. This process replaces the patient’s name with a different identifier, such as a medical reference number, in a solution that is simple, superficial, and easy to breach. 

The next possible compliance method is de-identification or anonymizing the data. Often performed manually, this task not only takes considerable time but also can pose a compliance risk as it exposes private information to analysts.

HIPAA suggests two acceptable ways to share data: either a Limited Dataset where some identifiers are removed or coded, allowed for very specific use-cases, or De-identified Datasets that result in barely usable numbers or require an expensive, formal review by a qualified expert. 

Yet, neither system eliminates the risk of identifying a patient through the data. A professional (and determined) adversary could still retrieve an identity by running different combinations of queries to re-identify the data.