Clinical abbreviation ambiguity: a barrier to effective care and clinical automation
Time is a critical resource in any clinical setting, and every second saved is a moment reinvested into improving the lives of patients. A common way hospital staff save time is through the use of acronyms and abbreviations when writing clinical records, which is especially useful in a field as jargon-heavy as medicine. You can imagine the lost time and wrist pain that accompany dutifully writing phrases like “percutaneous transluminal coronary angioplasty” or “medical resonance cholangiopancreatography ” all day. Acronyms and abbreviations can, however, have ambiguous or overlapping definitions. For example, while a cardiologist may read “LA” as “left atrium”, an infectious disease specialist might incorrectly read the acronym as “lymphadenopathy” or “local anesthetic”.
This ambiguity has a negative impact on hospitals, propagating clinical miscommunication and disrupting patient care. In fact, a national review of medication errors found that nearly 5% of errors could be attributed to ambiguous acronym use, and resulted in improper prescribing, dosing, and medication preparation
1. For example, in a prescription for “10U of insulin”, a hastily scrawled “U” may falsely mimic the number “0”. This extraordinarily dangerous mistake is reported to be the second most common abbreviation error, and can transform a prescribed “10 unit” dose of insulin into a “100 Unit” dose of insulin.
Physicians from unrelated disciplines are particularly susceptible to incorrectly interpreting clinical abbreviations, with one study showing that physicians from outside of pediatric care could only identify correctly abbreviations within pediatric records 31-63% of the time
2. These errors of interpretation could result in patient harm in today’s highly trans-disciplinary practice of medicine.
Not only does this ambiguity confound clinicians reading clinical text, it also serves as a barrier to analyses of clinical text via natural language processing (NLP) systems
3.
NLP is a discipline of artificial intelligence design that is concerned with analyzing and interpreting human language. NLP systems are a part of our everyday lives, powering the listening ears of “Siri” and “Alexa” while they translate our instructions, or autocompleting autocomplete the words we text. You can also blame NLP for every time you accidentally summoned Clippy (the defunct and much-maligned Microsoft Word helper) to tell you “it looks like you’re writing a letter”.
While these flashy tech applications of NLP are the most apparent examples, the clinical applications of NLP are among the most groundbreaking. NLP systems have the potential to rapidly sort through millions of medical files within hospital systems, and retrieve meaningful data that could improve medical research and clinical decision support. For example, a key bottle-neck in clinical research is the ability to identify a large population of eligible patients to enroll into a clinical trial. This is currently done manually, or at an expense of a large amount of time and money. NLP processing could quickly comb through medical records to identify and “match” patients to promising clinical trials.
However, just like our hypothetical physician who confuses “left atrium” with “lymphadenopathy”, computers operating these systems are similarly vexed by ambiguous acronyms in medical records.
UPMC Enterprises, the innovation and commercialization arm of
UPMC, has recognized the problem of clinical abbreviation ambiguity and has been investing in development of NLP pipelines to improve data analytics at UPMC. UPMC Enterprises is part of the
Pittsburgh Health Data Alliance (PHDA), which is a collaboration between UPMC, the
University of Pittsburgh, and
Carnegie Mellon University, that brings together large amounts of clinical data, and expertise in biomedical and machine learning research to develop innovative healthcare solutions. In March 2018, through the PHDA’s
Center for Commercial Applications of Healthcare Data (CCA), housed at the University of Pittsburgh,
Daqing He, PhD, a Professor at the School of Computing and Information of the University of Pittsburgh, was awarded with a translational research grant to address the unmet need in clinical abbreviation disambiguation. In collaboration with a team led by UPMC Enterprises’ Vice President of Analytics
Rebecca Jacobson, PhD, Dr. He has created a promising new technology called Clinical Abbreviation Resolution Engine (CARE) to solve this major clinical problem.