Machine Learning with Premier Healthcare Data to inform predictive modeling of antibiotic use
Description
Surveillance data, including the Emerging Infections Program (EIP) and the National Healthcare Safety Network (NHSN) can be linked to and other inpatient data to better characterize risk factors and outcomes of important healthcare associated infections (HAI) and antimicrobial resistance (AR). However, discharge data often lacks detailed information about inpatient antibiotic use.
Detailed example
These models will allow us to fill in gaps in antibiotic use information in Medicare claims and discharge datasets to better leverage EIP and NHSN data to better understand how cumulative antibiotic use may impact patients risk for HAIs and AR infections.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
This project uses the Premier Healthcare Database (PHD), an electronic health database, to predict inpatient antibiotic use and length of therapy using data readily available in claims and other electronic health record databases. This adds additional potential sources of information to support insights.
Controls / human review
ATO: Yes; PIA: Not published