The OpenDeID corpus for patient de-identification

For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically reduce annotation time. The OpenDeID corpus comprises 2,100 pathology reports from 1,833 cancer patients with an average of 737.49 tokens and 7.35 protected health information entities annotated per report. The overall inter annotator agreement and deviation scores are 0.9464 and 0.9726, respectively. Realistic surrogates are also generated to make the corpus suitable for distribution to other researchers.

A DICOM dataset for evaluation of medical image de-identification

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.

Pseudonymization of PHI Items in German Clinical Reports

We describe the adaptation of a non-clinical pseudonymization system, originally developed for a German email corpus, for clinical use. This tool replaces previously identified Protected Health Information (PHI) items as carriers of privacy-sensitive information (original names for people, organizations, places, etc.) with semantic type-conformant, yet, fictitious surrogates. We evaluate the generated substitutes for grammatical correctness, semantic and medical plausibility and find particularly low numbers of error instances (less than 1%) on all of these dimensions.

Protected Health Information and the Social media: Walking the tight rope Introduction (Preprint)

BACKGROUND Healthcare is changing rapidly, and consumer focus has become a priority for most organizations. In fact, found that 81% have identified “improving consumer experience” as a high priority for their organization. But only 11% of healthcare executives feel that their organization has the capabilities to deliver positive consumer experience. It’s important to understand that social media has the potential to be both enhancing and damaging, during or after a crisis. There will be numerous rumours and misinformation spreading during a crisis, creating panic among the public, with the aim of making the information ‘go viral.’ Population education or empowerment is important to ensure that the general population doesn’t fall victim to such rumours. Healthcare organisations have a duty to prevent damage in this way, by creating awareness. People should be educated to distinguish between trustworthy and misleading information. For example, we published an article on how misleading information on anorexia is promoted on YouTube, stating that “the illiterate in this ICT era will not be those who cannot read and write, but those who cannot distinguish between trustworthy and misleading information available online” (Syed-Abdul et al. 2013). OBJECTIVE na METHODS na RESULTS na CONCLUSIONS na CLINICALTRIAL na

Filtered BERT: Similarity Filter-Based Augmentation with Bidirectional Transfer Learning for Protected Health Information Prediction in Clinical Documents

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment.

Maturing an Information Technology Privacy Program

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) was initially enacted as an administrative simplification to standardize electronic transmission of common administrative and financial transactions. The program also calls for implementation specifications regarding privacy and security standards to protect the confidentiality and integrity of individually identifiable health information or protected health information. The Affordable Care Act further expanded many of the protective provisions set forth by HIPAA. Since its implementation, healthcare organizations around the nation have invested billions of dollars and have cycled through numerous program attempts aimed at meeting these standards. This chapter reviews the process taken by one organization to review the privacy policy in place utilizing a maturity model, identify deficiencies, and lead change in order to heighten the maturity of the system. The authors conclude with reflection related to effectiveness of the process as well as implications for practice.