Consolidated List of Requirements

This document is a consolidated catalogue of requirements for the Electronic Health Care Record (EHCR) and Electronic Health Care Record Architecture (EHCRA), gleaned largely from work done in the EU Framework III and IV programmes and CEN, but also including input from other sources including world-wide standardisation initiatives. The document brings together the relevant work done into a classified inventory of requirements to inform the on-going standardisation process as well as act as a guide to future implementation of EHCRA-based systems. It is meant as a contribution both to understanding of the standard and to the work that is being considered to improve the standard. Major features include the classification into issues affecting the Health Care Record, the EHCR, EHCR processing, EHCR interchange and the sharing of health care information and EHCR systems. The principal information sources are described briefly. It is offered as documentation that is complementary to the four documents of the ENV 13606 Parts I-IV produced by CEN Pts 26,27,28,29. The requirements identified and classified in this deliverable are referenced in other deliverables

Developing a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease

<p>Abstract</p> <p>Background</p> <p>Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers.</p> <p>Methods</p> <p>Using a set of clinical documents from the VA EMR for a particular use case of interest we identify specific challenges and present several opportunities for annotation tasks. We demonstrate specific methods using an open source annotation tool, a customized annotation schema, and a corpus of clinical documents for patients known to have a diagnosis of Inflammatory Bowel Disease (IBD). We report clinician annotator agreement at the document, concept, and concept attribute level. We estimate concept yield in terms of annotated concepts within specific note sections and document types.</p> <p>Results</p> <p>Annotator agreement at the document level for documents that contained concepts of interest for IBD using estimated Kappa statistic (95% CI) was very high at 0.87 (0.82, 0.93). At the concept level, F-measure ranged from 0.61 to 0.83. However, agreement varied greatly at the specific concept attribute level. For this particular use case (IBD), clinical documents producing the highest concept yield per document included GI clinic notes and primary care notes. Within the various types of notes, the highest concept yield was in sections representing patient assessment and history of presenting illness. Ancillary service documents and family history and plan note sections produced the lowest concept yield.</p> <p>Conclusion</p> <p>Challenges include defining and building appropriate annotation schemas, adequately training clinician annotators, and determining the appropriate level of information to be annotated. Opportunities include narrowing the focus of information extraction to use case specific note types and sections, especially in cases where NLP systems will be used to extract information from large repositories of electronic clinical note documents.</p

Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review

Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification

Clinical Text Mining: Secondary Use of Electronic Patient Records

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields

BMC Bioinformatics

BackgroundNatural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers.MethodsUsing a set of clinical documents from the VA EMR for a particular use case of interest we identify specific challenges and present several opportunities for annotation tasks. We demonstrate specific methods using an open source annotation tool, a customized annotation schema, and a corpus of clinical documents for patients known to have a diagnosis of Inflammatory Bowel Disease (IBD). We report clinician annotator agreement at the document, concept, and concept attribute level. We estimate concept yield in terms of annotated concepts within specific note sections and document types.ResultsAnnotator agreement at the document level for documents that contained concepts of interest for IBD using estimated Kappa statistic (95% CI) was very high at 0.87 (0.82, 0.93). At the concept level, F-measure ranged from 0.61 to 0.83. However, agreement varied greatly at the specific concept attribute level. For this particular use case (IBD), clinical documents producing the highest concept yield per document included GI clinic notes and primary care notes. Within the various types of notes, the highest concept yield was in sections representing patient assessment and history of presenting illness. Ancillary service documents and family history and plan note sections produced the lowest concept yield.ConclusionChallenges include defining and building appropriate annotation schemas, adequately training clinician annotators, and determining the appropriate level of information to be annotated. Opportunities include narrowing the focus of information extraction to use case specific note types and sections, especially in cases where NLP systems will be used to extract information from large repositories of electronic clinical note documents.1 PO1 CD000284-01/CD/ODCDC CDC HHS/United States19761566PMC274568

Development and Evaluation of an Ontology-Based Quality Metrics Extraction System

The Institute of Medicine reports a growing demand in recent years for quality improvement within the healthcare industry. In response, numerous organizations have been involved in the development and reporting of quality measurement metrics. However, disparate data models from such organizations shift the burden of accurate and reliable metrics extraction and reporting to healthcare providers. Furthermore, manual abstraction of quality metrics and diverse implementation of Electronic Health Record (EHR) systems deepens the complexity of consistent, valid, explicit, and comparable quality measurement reporting within healthcare provider organizations. The main objective of this research is to evaluate an ontology-based information extraction framework to utilize unstructured clinical text for defining and reporting quality of care metrics that are interpretable and comparable across different healthcare institutions. All clinical transcribed notes (48,835) from 2,085 patients who had undergone surgery in 2011 at MD Anderson Cancer Center were extracted from their EMR system and pre- processed for identification of section headers. Subsequently, all notes were analyzed by MetaMap v2012 and one XML file was generated per each note. XML outputs were converted into Resource Description Framework (RDF) format. We also developed three ontologies: section header ontology from extracted section headers using RDF standard, concept ontology comprising entities representing five quality metrics from SNOMED (Diabetes, Hypertension, Cardiac Surgery, Transient Ischemic Attack, CNS tumor), and a clinical note ontology that represented clinical note elements and their relationships. All ontologies (Web Ontology Language format) and patient notes (RDFs) were imported into a triple store (AllegroGraph?) as classes and instances respectively. SPARQL information retrieval protocol was used for reporting extracted concepts under four settings: base Natural Language Processing (NLP) output, inclusion of concept ontology, exclusion of negated concepts, and inclusion of section header ontology. Existing manual abstraction data from surgical clinical reviewers, on the same set of patients and documents, was considered as the gold standard. Micro-average results of statistical agreement tests on the base NLP output showed an increase from 59%, 81%, and 68% to 74%, 91%, and 82% (Precision, Recall, F-Measure) respectively after incremental addition of ontology layers. Our study introduced a framework that may contribute to advances in “complementary” components for the existing information extraction systems. The application of an ontology-based approach for natural language processing in our study has provided mechanisms for increasing the performance of such tools. The pivot point for extracting more meaningful quality metrics from clinical narratives is the abstraction of contextual semantics hidden in the notes. We have defined some of these semantics and quantified them in multiple complementary layers in order to demonstrate the importance and applicability of an ontology-based approach in quality metric extraction. The application of such ontology layers introduces powerful new ways of querying context dependent entities from clinical texts. Rigorous evaluation is still necessary to ensure the quality of these “complementary” NLP systems. Moreover, research is needed for creating and updating evaluation guidelines and criteria for assessment of performance and efficiency of ontology-based information extraction in healthcare and to provide a consistent baseline for the purpose of comparing alternative approaches