Coreference resolution in clinical discharge summaries, progress notes, surgical and pathology reports: a unified lexical approach

We developed a lexical rule-based system that uses a unified approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA) provided for the fifth i2b2/VA shared task. Taking the unweighted mean between 4 coreference metrics, validation of the system against the i2b2/VA corpus attained an overall F-score of 87.7% across all mention classes, with a maximum of 93.1% for coreference of persons, and a minimum of 77.2% for coreference of tests. For the ODIE corpus the overall F-score across all mention classes was 79.4%, with a maximum of 82.0% for coreference of persons and a minimum of 13.1% for coreference of diagnostic reagents. For the ODIE corpus our results are comparable to the mean reported inter-annotator agreement with the gold standard. We discuss the four categories of errors we identified, and how these might be addressed. The system uses a number of reusable modules and techniques that may be of benefit to the research community

Lexical patterns, features and knowledge resources for coreference resolution in clinical notes

Generation of entity coreference chains provides a means to extract linked narrative events from clinical notes, but despite being a well-researched topic in natural language processing, general- purpose coreference tools perform poorly on clinical texts. This paper presents a knowledge-centric and pattern-based approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA). In addition, a method for generating coreference chains using progressively pruned linked lists is demonstrated that reduces the search space and facilitates evaluation by a number of metrics. Independent evaluation results show an F-measure for each corpus of 79.2% and 87.5%, respectively, which offers performance at least as good as human annotators, greatly increased performance over general- purpose tools, and improvement on previously reported clinical coreference systems. The system uses a number of open-source components that are available to download

A systematic review and meta-analysis of the effectiveness of pharmacist-led medication reconciliation in the community after hospital discharge

BACKGROUND Pharmacists’ completion of medication reconciliation in the community after hospital discharge is intended to reduce harm due to prescribed or omitted medication and increase healthcare efficiency, but the effectiveness of this approach is not clear. We systematically review the literature to evaluate intervention effectiveness in terms of discrepancy identification and resolution, clinical relevance of resolved discrepancies and healthcare utilisation, including readmission rates, emergency department attendance and primary care workload. DESIGN Systematic literature review and meta-analysis of extracted data. METHODS Medline, CINHAL, EMBASE, AMED, ERIC, SCOPUS, NHS evidence and the Cochrane databases were searched using a combination of Medical Subject Heading (MeSH) terms and free text search terms. Controlled studies evaluating pharmacist-led medication reconciliation in the community after hospital discharge were included. Study quality was appraised using CASP. Evidence was assessed through meta-analysis of readmission rates. Discrepancy identification rates, emergency department attendance and primary care workload were assessed narratively. RESULTS Fourteen studies were included comprising five RCTs, six cohort studies and three pre-post intervention studies. Twelve studies had a moderate or high risk of bias. Increased identification and resolution of discrepancies was demonstrated in the four studies where this was evaluated. Reduction in clinically relevant discrepancies was reported in two studies. Meta-analysis did not demonstrate a significant reduction in readmission rate. There was no consistent evidence of reduction in emergency department attendance or primary care workload. CONCLUSIONS Pharmacists can identify and resolve discrepancies when completing medication reconciliation after hospital discharge but patient outcome or care workload improvements were not consistently seen. Future research should examine the clinical relevance of discrepancies and potential benefits on reducing healthcare team workload

U.S.-EU Safe Harbor Framework; A Guide to Self-Certification

[Excerpt] In this guide, we have provided an outline of the most critical pieces of the Safe Harbor Framework. The application is made available, along with a Helpful Hints Guide that explains how to fill it out. The Safe Harbor Principles and FAQs are also provided for easy reference. There is also an explanation and listing of third party dispute resolution providers (or Independent Resource Mechanisms) with descriptions of the services provided by three dispute resolution providers that work with Safe Harbor. Finally, we’ve also included several sample company privacy policies for reference, and a glossary that explains key terms. We’ve broken this Guide into nine major sections, each to address different questions you might have. What follows is a brief description of each section: Overview: The overview gives some background on the Safe Harbor Framework, how it came about, and explains many of the certification requirements. The overview also lists the principles of the Safe Harbor program. Application: The Application is provided for easy reference. Applicants should apply online at http://export.gov /safeharbor (click on “Certification Form” in the right sidebar). Certification Mark: The Commerce Department’s International Trade Administration has recently developed a certification mark for the Safe Harbor Framework. The mark may be used by companies on their websites to signify that they have self-certified compliance with the provisions of the Safe Harbor Framework. Instructions for use of the certification mark are provided. Helpful Hints Guide (to Certification): The Helpful Hints Guide is meant to give quick answers to any questions a U.S. company might have about the certification process. It should be used in conjunction with the rest of the Guide, however it answers many of the most common questions about the certification process. Safe Harbor Principles: We have provided the full text of the official declaration of the Safe Harbor Principles as announced on July 21, 2000. This text is helpful for understanding the foundation of the Safe Harbor Principles and the Framework. Frequently Asked Questions: We have provided the Frequently Asked Questions in full text because they answer many of the most commonly asked questions about the Safe Harbor Framework. Dispute Resolution Providers: Here we have provided a short description of the role of dispute resolution providers (also referred to as Independent Recourse Mechanisms) and descriptions of the services they offer. Sample Privacy Policies: Here we have provided three sample privacy policies for reference, which may serve as guidance when creating a new Privacy Policy or updating an existing Privacy Policy to align it with the Safe Harbor Framework. The Safe Harbor Framework requires an affirmative commitment in the Privacy Policy to the principles of the Safe Harbor Framework. Glossary: A short glossary is also provided for many of the technical terms frequently used in the Guide

Building a semantically annotated corpus of clinical texts

In this paper, we describe the construction of a semantically annotated corpus of clinical texts for use in the development and evaluation of systems for automatically extracting clinically significant information from the textual component of patient records. The paper details the sampling of textual material from a collection of 20,000 cancer patient records, the development of a semantic annotation scheme, the annotation methodology, the distribution of annotations in the final corpus, and the use of the corpus for development of an adaptive information extraction system. The resulting corpus is the most richly semantically annotated resource for clinical text processing built to date, whose value has been demonstrated through its use in developing an effective information extraction system. The detailed presentation of our corpus construction and annotation methodology will be of value to others seeking to build high-quality semantically annotated corpora in biomedical domains