When I became a parent, I wanted to live the longest, healthiest life possible. The good news is that we have the potential to live longer than ever. On average, the global life expectancy since 1950 has increased by 61.7% to over 73 years. Several factors are helping, such as healthcare advances, improved living conditions, better public health measures, and lifestyle changes.
The not-so-good news is that our longer health history poses some challenges. Consider this. You go to your primary care doctor for your annual wellness visit. Your doctor and staff enter your data into their medical practice’s database. You get lab work done at their preferred lab. The results go into the lab’s database and are accessible by your physician. You may have two portals to see the lab results: the physician’s and the lab’s. Two portals, two logins. But you need to get a follow-up scan—another portal with your data. Then you see the specialist, who doesn’t use the same system as your primary care physician. Multiply this over the years, and you will find that it is almost impossible to keep up with your data.
Healthcare Data is Ripe for Innovation
The above scenario demonstrates how your data and health history are duplicated and stored in multiple places that are not readily connected. Your spread-out data is also more vulnerable to privacy breaches. If you move to another state or get a new job and new insurance provider, it’s akin to starting over. Either you help stitch together your comprehensive health history or depend on diverse databases and systems to try and connect the data points that tell your health story.
Our longer life spans mean increases in our healthcare data volume, data variety, and complexity. With healthcare’s fragmented systems, clinicians and researchers struggle to access complete healthcare records or leverage anonymized data for research. As healthcare organizations work to mitigate challenges across data access, accuracy, and protection, the industry remains ripe for innovation.
One innovative cure for healthcare data challenges may be tokenization. While tokenization is often associated with transactions, it can be useful for medical records in the important areas of privacy, clinical care, and research capabilities. Let’s look at each one.
Various types of tokens are used to represent different units of information. In healthcare, de-identification tokens can replace sensitive data. Medical code tokenization is used for diagnoses and procedural codes. Prescription tokenization anonymizes drug names, dosages, and patient names. Image tokenization can de-identify medical images. Natural language processing tokens help analyze unstructured data such as clinical notes and research articles.
Personal information is an integral part of medical records and data. It is governed by strict regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union. Regulatory requirements add more complexity to managing, sharing, and safeguarding medical records, which increases healthcare privacy challenges.
Healthcare tokenization can replace personally identifiable information (PII)–name, address, and social security number–with randomly-generated identification tokens. The tokens can’t be mathematically reversed via a decryption key and are stored in a separate, secure database. These inherent principles of tokenization create new levels of privacy, usability, and accessibility for patient data.
Improving Clinical Care
In addition to personal information, medical records contain large amounts of diverse information such as test results, imaging reports, medication history, and clinician notes. The volume and variety of data are growing, making data management and organization more difficult and complicating clinical care.
In addition, the interoperability among different healthcare systems prevents the seamless exchange of medical information between clinicians and healthcare departments and organizations. A typical healthcare organization might have dozens (or hundreds) of systems in use by different departments. The lack of standardization and inconsistent data formats across electronic health record (EHR) platforms impedes medical records integration and hinders holistic patient care.
De-identifying PII through tokenization enables more secure and seamless data transfer between healthcare databases and systems. In addition, tokenization can identify specific clinical features, symptoms, or findings and generate alerts, reminders, or recommendations for specific healthcare professionals so that they can act more quickly with more complete information.
Healthcare data also plays a vital role in research. To perform accurate analyses and devise new treatments for diseases, however, medical researchers need real-time, rapid access to precise, anonymized patient data. Tokenization can help by advancing research and clinical trials with data de-identification and broader, real-time data usability.
For example, to better treat widespread diseases like COVID, researchers may need to “segment” data from a specific zip code, such as COVID patients from the 70090 zip code. Today, researchers must remove PII and link all the siloed data across numerous platforms and databases while trying to remove duplicates. The process is time-consuming and prone to errors. Using tokenization, researchers can quickly and accurately aggregate anonymized data for improved research while fortifying data privacy practices.
A Healthier Future
As we collectively live longer, we also want to live healthier. Through its unique and transformative potential, tokenization can help us achieve a healthier future via better data access, privacy, and management. That’s a future we can all look forward to.
Read more about the value of data and data management in healthcare here.
Learn about other trends in healthcare uncovered by Iron Mountain research here.