Practical Challenges in the Secondary Use of Real-World Data: The Notifiable Condition Detector

Abstract

The interoperability specifications for electronic laboratory reporting specify the use of HL7, LOINC, SNOMED CT and UCUM. We explored the degree to which health care transactions comply with these standards by evaluating laboratory data captured in a health information exchange to support automated detection of public health notifiable diseases. We studied the NCD's ability to detect and report Lead, Influenza and MRSA. We found that due to incomplete LOINC mapping, alternate approaches such as keyword searches within local test names and codes could identify additional potentially reportable messages. We also found that non-adherence to HL7 messaging standards and inconsistently recorded laboratory results require the use of complex systems with complementary NLP techniques to accurately report notifiable conditions. We conclude that the incomplete adoption of and adherence to specified standards poses challenges to deploying processes that utilize real-world data for secondary purposes.