Beyond the algorithm, what engineering challenge is critical for successful deployment of systems like ODS?

Building clean, standardized input streams via data governance

While the complexity of the machine learning algorithm receives significant academic focus, the practical success of automated detection systems hinges heavily on the preceding, less glamorous aspect: data governance. If data input streams flowing from various hospital sources are not standardized—meaning different facilities record diagnoses like 'pneumonia' using inconsistent codes or terminology within their EHRs—the machine learning model will fail to accurately aggregate signals across the network. The algorithm learns based on input; therefore, engineering clean, standardized, and consistent input data is as critical as the algorithm itself for ensuring the system accurately detects true pathogen signals rather than merely learning to recognize systemic data messiness.