Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges

Abstract

The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing amount of data suggesting that a large amount of patient harm is preventable and can be mitigated with effective risk strategies that have not been sufficiently adopted. Adverse events from medications are part of clinical practice, but the ability to identify a patient's risk and to minimize that risk must be a priority. The ability to identify adverse events has been a challenge due to limitations of available data sources, which are often free text. The use of natural language processing (NLP) may help to address these limitations. NLP is the artificial intelligence domain of computer science that uses computers to manipulate unstructured data (i.e., narrative text or speech data) in the context of a specific task. In this narrative review, we illustrate the fundamentals of NLP and discuss NLP's application to medication safety in four data sources: electronic health records, Internet-based data, published literature, and reporting systems. Given the magnitude of available data from these sources, a growing area is the use of computer algorithms to help automatically detect associations between medications and adverse effects. The main benefit of NLP is in the time savings associated with automation of various medication safety tasks such as the medication reconciliation process facilitated by computers, as well as the potential for near-real-time identification of adverse events for postmarketing surveillance such as those posted on social media that would otherwise go unanalyzed. NLP is limited by a lack of data sharing between health care organizations due to insufficient interoperability capabilities, inhibiting large-scale adverse event monitoring across populations. We anticipate that future work in this area will focus on the integration of data sources from different domains to improve the ability to identify potential adverse events more quickly and to improve clinical decision support with regard to a patient's estimated risk for specific adverse events at the time of medication prescription or review.

Mining social media data to assess the risk of skin and soft tissue infections from allergen immunotherapy

“…17 NLP has been used in medicine and clinical research to process and analyze large amounts of free-text documents, such as clinical notes/reports and social media posts. 18,19 Treato's analysis engine combines a diverse range of medical ontologies, organized in a concept-based structure and coded similarly to the Unified Medical Language System (UMLS), 20 and patient language vocabulary for concept extraction and analysis. Among the ontologies used is the Medical Dictionary for Regulatory Activities, 21 which is used by the US Food and Drug Administration for adverse drug reaction term coding.…”

Using Electronic Health Records To Generate Phenotypes For Research

“…The access of these unstructured data, while addressing the scalability limitations of manual chart review as well as the data quality limitations of simple text searches, usually requires some form of natural language processing (NLP). Broadly defined, NLP is an approach that extracts structured natural language data from unstructured text in a high‐throughput manner (Ohno‐Machado, ) using rule‐based and/or probabilistic methods (Hirschberg & Manning, ; Joshi, ; Wong, Plasek, Montecalvo, & Zhou, ). NLP tasks can include sentence boundary detection, tokenization (using punctuation and spaces to split text roughly into words), part‐of‐speech assignment to individual words, morphological decomposition of compound words, shallow parsing or chunking of phrases, and problem‐specific segmentation (Jensen, Jensen, & Brunak, ).…”

Innovations in Medication Safety: Services and Technologies to Enhance the Understanding and Prevention of Adverse Drug Reactions

“…Large health systems also offer vital information to increase awareness of ADRs, although this information is underutilized because of the complexity in mining these data. Natural language processing is a method of programming computers to extract information from unstructured data . This approach has been available since the 1950s and has been slowly making its way to the patient safety literature in the 1990s.…”

“…Natural language processing is a method of programming computers to extract information from unstructured data. 5 This approach has been available since the 1950s and has been slowly making its way to the patient safety literature in the 1990s. Natural language processing for the purposes of identifying ADRs requires modification to address the challenges of characterizing the context of adverse events, but it is a promising method.…”

“…Natural language processing for the purposes of identifying ADRs requires modification to address the challenges of characterizing the context of adverse events, but it is a promising method. 5 Existing technology to assist with detection and prevention of medication errors and ADRs includes smart pump technology, computerized prescriber order entry, clinical decision support (CDS), bar code technology, and automated dispensing cabinets. 6 Smart pumps and CDS have been faulted with concerns of alarm and alert fatigue, respectively.…”