Hospitals and health systems are sitting on a wealth of patient information that has the potential to transform care delivery. Yet analytics infrastructures designed to fuel performance improvement have traditionally overlooked much of that data because it resides in health IT systems as unstructured free text.
The industry has made notable inroads with structured patient data through the introduction of standards. Now, healthcare organizations must leverage strategies that enable access and retrieval of critical patient data housed in unstructured information—accounting for as much as 80 percent of clinical documentation, according to industry estimates.
Advances with natural language processing (NLP) technology hold great promise as the answer to this challenge. Increasingly recognized as a powerful tool for unlocking vital clinical data, NLP turns free text into shareable data that can be analyzed and acted upon.
The reality is that unstructured data is important to care delivery. A 2009 survey found that 96 percent of physicians were concerned about “losing the unique patient story with the transition to point-and-click (template-driven) EHRs.” Additionally, 94 percent said that “including the physician narrative as part of patients’ medical records is ‘important’ or ‘very important’ to realizing and measuring improved patient outcomes.”
The bigger question is: How much of this unstructured data is useful for complete and accurate quality measure reporting and analytics? Healthcare organizations often miss critical information when conducting analytics initiatives due to limitations with free text. In fact, one study found that EHR-derived quality measures can undercount practice performance when compared to a manual review of electronic charts.
Without the right infrastructure in place to address both structured and unstructured patient data, lab information, qualitative clinical information, and patients are often excluded from quality measure calculations. Here are some examples of how information is missed:
Information that qualifies a patient for exclusion criteria in a quality measure
Quality measure PQRS 116 (NQF 58) penalizes the performance score for every patient who receives antibiotics for acute bronchitis—antibiotics don’t help acute bronchitis and may cause harm. The measure provides exclusion criteria indicating that if the patient has a secondary condition, such as cystic fibrosis or HIV, then it is acceptable to prescribe antibiotics so they are excluded from the measure. Often, the information related to these excluded conditions resides in free text, requiring NLP for accurate identification and quality measure calculation. Thus, the right infrastructure can ultimately help a healthcare organization accurately report quality measures to gain higher scores, avoid negative payment adjustments, and generate positive payment adjustments.