OBJECTIVE: In recognition of potential barriers that may inhibit the widespread adoption of biomedical software, the 2014 i2b2 Challenge introduced a special track, Track 3 - Software Usability Assessment, in order to develop a better understanding of the adoption issues that might be associated with the state-of-the-art clinical NLP systems. This paper reports the ease of adoption assessment methods we developed for this track, and the results of evaluating five clinical NLP system submissions. MATERIALS AND METHODS: A team of human evaluators performed a series of scripted adoptability test tasks with each of the participating systems. The evaluation team consisted of four "expert evaluators" with training in computer science, and eight "end user evaluators" with mixed backgrounds in medicine, nursing, pharmacy, and health informatics. We assessed how easy it is to adopt the submitted systems along the following three dimensions: communication effectiveness (i.e., how effective a system is in communicating its designed objectives to intended audience), effort required to install, and effort required to use. We used a formal software usability testing tool, TURF, to record the evaluators' interactions with the systems and 'think-aloud' data revealing their thought processes when installing and using the systems and when resolving unexpected issues. RESULTS: Overall, the ease of adoption ratings that the five systems received are unsatisfactory. Installation of some of the systems proved to be rather difficult, and some systems failed to adequately communicate their designed objectives to intended adopters. Further, the average ratings provided by the end user evaluators on ease of use and ease of interpreting output are -0.35 and -0.53, respectively, indicating that this group of users generally deemed the systems extremely difficult to work with. While the ratings provided by the expert evaluators are higher, 0.6 and 0.45, respectively, these ratings are still low indicating that they also experienced considerable struggles. DISCUSSION: The results of the Track 3 evaluation show that the adoptability of the five participating clinical NLP systems has a great margin for improvement. Remedy strategies suggested by the evaluators included (1) more detailed and operation system specific use instructions; (2) provision of more pertinent onscreen feedback for easier diagnosis of problems; (3) including screen walk-throughs in use instructions so users know what to expect and what might have gone wrong; (4) avoiding jargon and acronyms in materials intended for end users; and (5) packaging prerequisites required within software distributions so that prospective adopters of the software do not have to obtain each of the third-party components on their own.

OBJECTIVE: Secondary use of clinical text is impeded by a lack of highly effective, low-cost de-identification methods. Both, manual and automated methods for removing protected health information, are known to leave behind residual identifiers. The authors propose a novel approach for addressing the residual identifier problem based on the theory of Hiding In Plain Sight (HIPS). MATERIALS AND METHODS: HIPS relies on obfuscation to conceal residual identifiers. According to this theory, replacing the detected identifiers with realistic but synthetic surrogates should collectively render the few 'leaked' identifiers difficult to distinguish from the synthetic surrogates. The authors conducted a pilot study to test this theory on clinical narrative, de-identified by an automated system. Test corpora included 31 oncology and 50 family practice progress notes read by two trained chart abstractors and an informaticist. RESULTS: Experimental results suggest approximately 90% of residual identifiers can be effectively concealed by the HIPS approach in text containing average and high densities of personal identifying information. DISCUSSION: This pilot test suggests HIPS is feasible, but requires further evaluation. The results need to be replicated on larger corpora of diverse origin under a range of detection scenarios. Error analyses also suggest areas where surrogate generation techniques can be refined to improve efficacy. CONCLUSIONS: If these results generalize to existing high-performing de-identification systems with recall rates of 94-98%, HIPS could increase the effective de-identification rates of these systems to levels above 99% without further advancements in system recall. Additional and more rigorous assessment of the HIPS approach is warranted.

OBJECTIVE: De-identified medical records are critical to biomedical research. Text de-identification software exists, including "resynthesis" components that replace real identifiers with synthetic identifiers. The goal of this research is to evaluate the effectiveness and examine possible bias introduced by resynthesis on de-identification software. DESIGN: We evaluated the open-source MITRE Identification Scrubber Toolkit, which includes a resynthesis capability, with clinical text from Vanderbilt University Medical Center patient records. We investigated four record classes from over 500 patients' files, including laboratory reports, medication orders, discharge summaries and clinical notes. We trained and tested the de-identification tool on real and resynthesized records. MEASUREMENTS: We measured performance in terms of precision, recall, F-measure and accuracy for the detection of protected health identifiers as designated by the HIPAA Safe Harbor Rule. RESULTS: The de-identification tool was trained and tested on a collection of real and resynthesized Vanderbilt records. Results for training and testing on the real records were 0.990 accuracy and 0.960 F-measure. The results improved when trained and tested on resynthesized records with 0.998 accuracy and 0.980 F-measure but deteriorated moderately when trained on real records and tested on resynthesized records with 0.989 accuracy 0.862 F-measure. Moreover, the results declined significantly when trained on resynthesized records and tested on real records with 0.942 accuracy and 0.728 F-measure. CONCLUSION: The de-identification tool achieves high accuracy when training and test sets are homogeneous (ie, both real or resynthesized records). The resynthesis component regularizes the data to make them less "realistic," resulting in loss of performance particularly when training on resynthesized data and testing on real data.

This paper presents a method for interpreting metaphoric language in the context of a portable natural language inferface. The method licenses metaphoric uses via coercions between incompatible ontological sorts. The machinery allows both previously-known and unexpected metaphoric uses ot be correctly interpreted and evaluated with respect to the backend expert system.