Patient safety is an issue that healthcare organizations (HCOs) must prioritize – but how can they improve efficiency when it comes to reviewing the 80% of relevant patient information that is locked in unstructured data?
Under pressure to provide value-based care and adhere to quality measures, HCOs are increasingly turning to AI-based technologies such as Natural Language Processing (NLP), which makes unstructured data usable – thereby improving the efficiency of quality initiatives, quality measure reporting and, most importantly, patient safety.
Addressing the healthcare safety and quality challenge with NLP
While the U.S. health system has made progress in recent years, patient safety continues to be a challenge that all HCOs must prioritize. An estimated 1.7 million healthcare-associated infections occur each year in the U.S. leading to 99,000 deaths. Moreover, adverse medication events cause more than 770,000 injuries and deaths each year at a cost as high as $5.6 billion annually, according to statistics cited by the Center for Patient Safety.
NLP workflows can help reduce the likelihood of error and improve patient safety by automating the identification and extraction of key concepts from large volumes of clinical documentation. Findings are transformed into structured data to simplify chart review and speed the identification of high-risk patients.