Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-the-art results. Unlike other systems, it does not rely on human-engineered features, which allows it to be quickly deployed, but does not leverage knowledge from human experts or from electronic health records (EHRs). In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system. Our results show that the addition of features, especially the EHR-derived features, further improves the state-of-the-art in patient note de-identification, including for some of the most sensitive PHI types such as patient names. Since in a real-life setting patient notes typically come with EHRs, we recommend developers of de-identification systems to leverage the information EHRs contain.
A legal prerequisite for a patient note to be shared with a medical investigator is that it must be de-identified. The objective of the de-identification process is to remove all Protected Health Information (PHI). Not appropriately removing PHI may result in financial penalties @cite_27 @cite_32 . In the United States, the Health Insurance Portability and Accountability Act (HIPAA) @cite_19 defines PHI types that must be removed, ranging from phone numbers to patient names. Failure to accurately de-identify a patient note would jeopardize the patient's privacy: the performance of a de-identification system is therefore critical.
@cite_19 @cite_27 @cite_32
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With nearly $30 billion in incentives available, it is critical to know to what extent US hospitals have been able to respond to those incentives by adopting electronic health record (EHR) systems that meet Medicare’s criteria for their “meaningful use.” Medicare has provided aggregate incentive payment data, but still missing is an understanding of how these payments are distributed across hospital types and years. Our analysis of Medicare data found a substantial increase in the percentage of hospitals receiving EHR incentive payments between 2011 (17.4 percent) and 2012 (36.8 percent). However, this increase was not uniform across all hospitals, and the overall proportion of hospitals receiving a payment for meaningful use was low. Critical-access, smaller, and publicly owned or nonprofit hospitals appeared to be at particular risk for failing to meet Medicare’s meaningful-use criteria, and the overall proportion of hospitals receiving a payment for meaningful use was low. Starting in 2015, hospitals t... The HITECH Act created incentives to encourage adoption of electronic health records. As of May 2012, only 12.2 of 62,226 eligible professionals had attested to meaningful use, including 9.8 of specialists and 17.8 of primary care providers.