Indiana University-Purdue University Indianapolis (IUPUI) / There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.

Indiana University-Purdue University Indianapolis (IUPUI) / As medical data continues to transition to electronic formats, opportunities arise for researchers to use this microdata to discover patterns and increase knowledge that can improve patient care. Now more than ever, it is critical to protect the identities of the patients contained in these databases. Even after removing obvious “identifier” attributes, such as social security numbers or first and last names, that clearly identify a specific person, it is possible to join “quasi-identifier” attributes from two or more publicly available databases to identify individuals. K-anonymity is an approach that has been used to ensure that no one individual can be distinguished within a group of at least k individuals. However, the majority of the proposed approaches implementing k-anonymity have focused on improving the efficiency of algorithms implementing k-anonymity; less emphasis has been put towards ensuring the “utility” of anonymized data from a researchers’ perspective. We propose a new data utility measurement, called the research value (RV), which extends existing utility measurements by employing data constraints rules that are designed to improve the effectiveness of queries against the anonymized data. To anonymize a given raw dataset, two algorithms are proposed that use predefined generalizations provided by the data content expert and their corresponding research values to assess an attribute’s data utility as it is generalizing the data to ensure k-anonymity. In addition, an automated algorithm is presented that uses clustering and the RV to anonymize the dataset. All of the proposed algorithms scale efficiently when the number of attributes in a dataset is large.

Indiana University-Purdue University Indianapolis (IUPUI) / Reducing medical error remains in the forefront of healthcare reform. The use of health information technology, specifically the electronic health record (EHR) is one attempt to improve patient safety. The implementation of the EHR in the Emergency Department changes physician workflow, which can have negative, unintended consequences for patient safety. Inaccuracies in clinical documentation can contribute, for example, to medical error during transitions of care. In this quasi-experimental comparison study, we sought to determine whether there is a difference in document quality, error rate, error type, cognitive load and time when Emergency Medicine (EM) residents use paper charts versus the EHR to complete physician documentation of clinical encounters. Simulated patient encounters provided a unique and innovative environment to evaluate EM physician documentation. Analysis focused on examining documentation quality and real-time observation of the simulated encounter. Results demonstrate no change in document quality, no change in cognitive load, and no change in error rate between electronic and paper charts. There was a 46% increase in the time required to complete the charting task when using the EHR. Physician workflow changes from partial documentation during the patient encounter with paper charts to complete documentation after the encounter with electronic charts. Documentation quality overall was poor with an average of 36% of required elements missing which did not improve during residency training. The extra time required for the charting task using the EHR potentially increases patient waiting times as well as clinician dissatisfaction and burnout, yet it has little impact on the quality of physician documentation. Better strategies and support for documentation are needed as providers adopt and use EHR systems to change the practice of medicine.