Publications by authors named "Katelyn Robinson"
Article Synopsis
- Post-marketing safety surveillance can be improved by detecting clinical events through spontaneous reporting, but it requires healthcare professionals to be well-informed and aware of the reporting process.
- The study introduces a new method for identifying incidents using unstructured clinical data and natural language processing, validated against traditional methods for two specific health concerns: suicide attempts and sleep-related behaviors.
- Results showed that while the new approach effectively identified suicide attempts with decent precision, it struggled more with sleep-related behaviors; additionally, performance varied by race, highlighting the need for careful monitoring and bias reduction in healthcare AI applications.
Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risk under-coding and imprecision.
View Article and Find Full Text PDFObjective: To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.
Materials And Methods: Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017.