@article{780,
  keywords = {Analytics, EHR, Health IT, Patient Safety},
  author = {David Classen and Michael Li and Suzanne Miller and Drew Ladner},
  title = {An Electronic Health Record-Based Real-Time Analytics Program For Patient Safety Surveillance And Improvement.},
  abstract = {<p>Twenty years after publication of the report To Err Is Human, studies demonstrate persisting high levels of patient harm. Most patient safety measurement remains highly retrospective, relying on voluntary reporting and post discharge administrative coding. Progress has been limited by the lack of advances in measurement accuracy, detection sensitivity, and timely actionability. The broad adoption of electronic health records (EHRs) offers a significant opportunity to leverage digital information to improve safety measurement and management using real-time data. We developed a novel method to extract safety indicators from EHRs to identify harm and its precursors by implementing a patient safety active management system (PSAM) in hospitals within a national Patient Safety Organization (PSO). The PSAM generated validated adverse event outcomes and leveraged EHR data to develop a real-time safety predictive model. This study describes the PSAM's pilot at two large community hospitals in 2014-17. We found that the PSAM could detect harm in real time, at higher rates than current levels are detected, and that such harm could be predicted. In addition to outlining future opportunities and challenges with this EHR-enabled PSAM approach, we discuss implications and next steps for policy and practice.</p>},
  year = {2018},
  journal = {Health Aff (Millwood)},
  volume = {37},
  pages = {1805-1812},
  month = {12/2018},
  issn = {1544-5208},
  doi = {10.1377/hlthaff.2018.0728},
  language = {eng},
}