Patient privacy — you might be scratching your head right now. NLM is a research enterprise and a LIBRARY for heaven’s sake! What does a library have to do with patient privacy? NLM protects the privacy of all people who use our resources, which are free and accessible 24/7. NLM complies with requirements for privacy and security established by the Office of Management and Budget, Department of Health and Human Services, and NIH. I encourage you to visit our Privacy and Security Policy guidelines.
No personal identifying information is required to search and access our vast data repositories and library resources. Anyone, sick or well, who wants trusted information about a disease, illness, or health condition can search through our MedlinePlus online health information service. With data available in English and Spanish, MedlinePlus offers high quality, relevant health information for patients and their families on more than 1,000 topics such as children’s growth and development, gene therapies, and self-care after surgery.
We do not link search strategies to any specific patron without their permission. NLM only links information for those patrons who sign up for My NCBI, which is a service that allows patrons to save and return to previous search results. This information is held in a safe, secure part of our computer systems open only to the individual.
NLM also provides expert guidance to other federal agencies for the most effective approaches to preserving patient privacy. Clem Mc Donald, MD, our Chief Health Data Standards Officer, serves as a member of the Health Information Technology Advisory Committee, which is an advisory committee to the Office of the National Coordinator for Health Information Technology that oversees a range of issues from promoting health IT excellence in communities to collaborations among federal agencies. We recently participated in the federal response to Executive Order 13994, Ensuring a Data-Driven Response to COVID-19 and Future High-Consequence Public Health Threats, leveraging our expertise in protecting patient data and preventing inadvertent re-identification from genomic information.
Patient participation in clinical trials and other research efforts advances science and creates the pathways to discover new clinical therapies and interventions. Sometimes, data generated in one study becomes useful in future studies; for example, when trying to understand how different groups of patients respond to the same therapy. NLM provides technical assistance to the National Institutes of Health in creating ways to store participant-level study information safely and securely making information useable for other researchers while making sure that personally identifiable information is not disclosed. We also help NIH create safe, secure data repositories of research data and implement mechanisms and oversight measures to ensure that data is available to researchers and managed in a way consistent with the original agreements for use of the data. We helped establish NIH’s Researcher Auth Service Initiative, a single sign-on for researchers that allows access to specific data sets in a controlled manner.
Our researchers also develop computational methods to protect patient privacy. This includes research investigating how to remove traces of identifying data from clinical records, while making those records useful for researchers to better understand the course of disease and determine the effectiveness of treatments. NLM’s Dr. Mehmet Kayaalp develops ways to let approved researchers use clinical records for clinical studies in a way that protects patient privacy. He describes his work this way:
Narrative clinical reports contain a rich set of clinical knowledge that could be invaluable for clinical research. However, they usually also contain personal identifiers that are considered protected health information and are associated with use restrictions and risks to privacy. Computational de-identification seeks to remove all of the identifiers in such narrative text in order to produce de-identified documents that can be used in research while protecting patient privacy. Computational de-identification uses natural language processing tools and techniques to recognize patient-related individually identifiable information (e.g., names, addresses, and telephone and social security numbers) in the text and redacts them. In this way, patient privacy is protected, and clinical knowledge is preserved.
Dr. Mehmet Kayaalp
So – we’re more than a library. We are a partner in preserving patient privacy while making sure that researchers and clinicians can discover the best new ways for taking care of patients.
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