By Dr. Elizabeth Marshall

Preserving patient privacy is of paramount importance to clinicians, which is one reason healthcare providers often shy away from opportunities to make health information available for secondary uses, such as the development of new medications.

While some clinicians may feel uncomfortable providing patient data to outside entities, even when anonymized, it is important to keep in mind that there are many benefits to reusing health data to derive insights that can drive healthcare improvements through, for example, operational efficiencies, predicting disease outcomes, or innovative treatments regimens.

Fortunately, advancements in governance and technology can now deliver unprecedented levels of privacy and security to preserve the anonymity of patients. In addition, most patients are willing to share their data for research, according to a study published in JAMA Network Open. Given evolving sentiments and technology enhancements, more providers are now realizing the benefits of sharing non-identifiable structured and unstructured health data from EHRs.

When organizations take measures to protect health data to preserve privacy, the sharing of health data to other entities can be a win for both providers and patients.

Advancing innovation with clinical data
Clinical data is often rich in details that help pharma companies, device manufacturers, and other life science companies advance the discovery and development of new life-saving therapies, identify potential cohorts for clinical trials, and test the efficacy of drugs.

Consider, for example, the tremendous push to bring COVID-19-targeted drugs and vaccines to market. As of the end of the third quarter of 2020 over 600 drugs and vaccines were in development and data from EHRs has undoubtedly helped researchers better understand the disease and its progression, including the virus’s impact on different populations and the effectiveness of various therapies. The rapid worldwide spread of COVID-19 and its potential for serious complications have underscored the need for high-quality clinical data that accelerates the development of safe and effective solutions.

To provide value on a large scale, clinical information must be in a structured format that supports data analytics and includes a full 360o-view of a patient’s health status. Unfortunately, as much as 80% of patient data within clinical records is hidden as unstructured text within doctor narratives, lab reports, discharge summaries. This makes it difficult to glean vital health information, such as potentially undiagnosed conditions, social determinants of health, or risk factors driving poorer patient outcomes. Using natural language processing (NLP) technology, however, organizations can transform data from an unstructured to structured format to facilitate additional analysis.

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