ICMCC

the international council on medical & care compunetics

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24
April, 2014
Thursday

narrative

Chapter 13: Mining Electronic Health Records in the Genomics Era

Denny JC. PLoS Comput Biol, 8(12)

The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information.
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4 January 2013 | No Comments »
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Automating classification of free-text electronic health records for epidemiological studies

Schuemie MJ et al, Pharmacoepidemiology and Drug Safety, 2012

PURPOSE:
Increasingly, patient information is stored in electronic medical records, which could be reused for research. Often these records comprise unstructured narrative data, which are cumbersome to analyze. The authors investigated whether text mining can make these data suitable for epidemiological studies and compared a concept recognition approach and a range of machine learning techniques that require a manually annotated training set. The authors show how this training set can be created with minimal effort by using a broad database query.
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26 January 2012 | No Comments »
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You and your EMR: the research perspective: Part 2. How structure matters

Ryan BL et al, Canadian Family Physician, 57(12)

Inputting information into your EMR requires a balance between being efficient and being complete and accurate. Both goals are critical for patient care. When you intend to conduct research using data from your EMR, there is an additional goal of being able to retrieve the data in a reliable and consistent manner.
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16 December 2011 | No Comments »
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Patient Expectations in the Digital World

Bos L. Future Visions on Biomedicine and Bioinformatics 1, 2011

This chapter is based on my keynote during the Estonian e-Health Conference in Tallinn, october 2010.
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4 September 2011 | No Comments »
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Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions

Chapman WW et al, J Am Med Inform Assoc, 18(5)

This issue of JAMIA focuses on natural language processing (NLP) techniques for clinical-text information extraction. Several articles are offshoots of the yearly ‘Informatics for Integrating Biology and the Bedside’ (i2b2) (http://www.i2b2.org) NLP shared-task challenge, introduced by Uzuner et al (see page 552) and co-sponsored by the Veteran’s Administration for the last 2 years. This shared task follows long-running challenge evaluations in other fields, such as the Message Understanding Conference (MUC) for information extraction, TREC for text information retrieval, and CASP for protein structure prediction. Shared tasks in the clinical domain are recent and include annual i2b2 Challenges that began in 2006, a challenge for multi-label classification of radiology reports sponsored by Cincinnati Children’s Hospital in 2007, a 2011 Cincinnati Children’s Hospital challenge on suicide notes, and the 2011 TREC information retrieval shared task involving retrieval of clinical cases from narrative records.
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18 August 2011 | No Comments »
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Collaborative search in electronic health records

Zheng K et al, J Am Med Inform Assoc, 18(3)

Objective
A full-text search engine can be a useful tool for augmenting the reuse value of unstructured narrative data stored in electronic health records (EHR). A prominent barrier to the effective utilization of such tools originates from users’ lack of search expertise and/or medical-domain knowledge. To mitigate the issue, the authors experimented with a ‘collaborative search’ feature through a homegrown EHR search engine that allows users to preserve their search knowledge and share it with others. This feature was inspired by the success of many social information-foraging techniques used on the web that leverage users’ collective wisdom to improve the quality and efficiency of information retrieval.
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1 May 2011 | No Comments »
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Patients’ needs assessment documentation in multidisciplinary electronic health records

Häyrinen K, Saranto K. MEDINFO 2010

The purpose of this study is to describe and discuss physicians’ and nurses’ documentation of the patient’s needs assessment in electronic health records (EHR) in the neurological care setting. Both physicians and nurses collect, record and interpret data during patient care episodes. Assessment of patient’s need for care and treatment is an important part of the care process. Planning, implementation and outcome assessment of the care process are based on needs assessment data. The data of this study consist of 48 neurological medical narratives and nursing care plans. The data were analyzed using descriptive statistics and content analysis.
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19 January 2011 | No Comments »
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Extracting medical information from narrative patient records: the case of medication-related information

Deléger L et al, J Am Med Inform Assoc, 17(5)

Objective
While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as well as different strategies to improve the extraction.
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24 September 2010 | No Comments »
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Automatic de-identification of textual documents in the electronic health record: a review of recent research

Meystre SM et al, BMC Medical Research Methodology, 10(1)

Background
In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA “Safe Harbor” technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here.
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5 August 2010 | No Comments »
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What I’ve learned from E-patients

Hoch D, Ferguson T, PLoS Medicine, 2(8)

As a neurologist subspecializing in epilepsy at a respected academic institution, I (DH) assumed that I knew everything I needed to know about epilepsy and patients with epilepsy. I was wrong.
In September of 1994, John Lester, my colleague in the Department of Neurology at Massachusetts General Hospital, showed me an online bulletin board for neurology patients that he had created [1]. In reading through the online messages, I observed hundreds of patients with neurological diseases sharing their experiences and discussing their problems with one another.
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17 May 2010 | No Comments »
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Can Electronic Clinical Documentation Help Prevent Diagnostic Errors?

Schiff GD, Bates DW. N Engl J Med, 362(12)

The United States is about to invest nearly $50 billion in health information technology (HIT) in an attempt to push the country to a tipping point with respect to the adoption of computerized records, which are expected to improve the quality and reduce the costs of care. A fundamental question is how best to design electronic health records (EHRs) to enhance clinicians’ workflow and the quality of care.
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25 March 2010 | No Comments »
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Quantifying clinical narrative redundancy in an electronic health record

Wrenn JO et al, J Am Med Inform Assoc, 17(1)

Objective
Although electronic notes have advantages compared to handwritten notes, they take longer to write and promote information redundancy in electronic health records (EHRs). We sought to quantify redundancy in clinical documentation by studying collections of physician notes in an EHR.
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17 December 2009 | No Comments »
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MedEx: a medication information extraction system for clinical narratives

Xu, Hua et al, J Am Med Inform Assoc, 17(1)

Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes.
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16 December 2009 | No Comments »
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Disparities by race and ethnicity in cancer survivor stories available on the web

Eddens, Katherine S. et al, J Med Internet Res, 11(4)

Background:
The rapid growth of eHealth could have the unintended effect of deepening health disparities between population subgroups. Most concerns to date have focused on population differences in access to technology, but differences may also exist in the appropriateness of online health content for diverse populations.
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1 December 2009 | No Comments »
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Participatory Storytelling Online: A Complementary Model of Patient Satisfaction

Born, Karen et al, ElectronicHealthcare, 8(2)

Measuring patient satisfaction is an important quality improvement technique. The World Wide Web offers new approaches to understanding patient satisfaction and stories about healthcare encounters. In this paper, we suggest that there is a wealth of patients’ stories being told online, in real-time, on social networking and on social rating Web sites.
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9 November 2009 | No Comments »
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Towards automated processing of clinical Finnish: sublanguage analysis and a rule-based parser

Laippala, Veronika et al, International Journal of Medical Informatics, 78(12)

Introduction
In this paper, we present steps taken towards more efficient automated processing of clinical Finnish, focusing on daily nursing notes in a Finnish Intensive Care Unit (ICU). First, we analyze ICU Finnish as a sublanguage, identifying its specific features facilitating, for example, the development of a specialized syntactic analyser. The identified features include frequent omission of finite verbs, limitations in allowed syntactic structures, and domain-specific vocabulary. Second, we develop a formal grammar and a parser for ICU Finnish, thus providing better tools for the development of further applications in the clinical domain.
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8 November 2009 | No Comments »
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Characterizing environmental and phenotypic associations using information theory and electronic health records

Wang, Xiaoyan et al, BMC Bioinformatics, 10(s9)

Background
The availability of up-to-date, executable, evidence-based medical knowledge is essential for many clinical applications, such as pharmacovigilance, but executable knowledge is costly to obtain and update. Automated acquisition of environmental and phenotypic associations in biomedical and clinical documents using text mining has showed some success. The usefulness of the association knowledge is limited, however, due to the fact that the specific relationships between clinical entities remain unknown. In particular, some associations are indirect relations due to interdependencies among the data.
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27 September 2009 | No Comments »
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Cohort study of structured reporting compared with conventional dictation

Johnson, Annette J. et al, Radiology, 253(1)

Purpose:
To determine if radiology residents who used a structured reporting system (SRS) produced higher quality reports than residents who used conventional free-text dictation to report cranial magnetic resonance (MR) imaging in patients suspected of having a stroke.
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29 August 2009 | No Comments »
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Exploring the Ability of Natural Language Processing to Extract Data From Nursing Narratives

Hyun, Sookyung et al, Computers Informatics Nursing, 27(4)

Natural Language Processing (NLP) offers an approach for capturing data from narratives and creating structured reports for further computer processing. We explored the ability of a NLP system, Medical Language Extraction and Encoding (MedLEE), on nursing narratives. MedLEE extracted 490 concepts from narrative text in a sample of 553 oncology nursing process notes.
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4 July 2009 | No Comments »
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Comparison of information content of structured and narrative text data sources on the example of medication intensification

Turchin, Alexander et al, J Am Med Inform Assoc, 16(3)

OBJECTIVE
To compare information obtained from narrative and structured electronic sources using anti-hypertensive medication intensification as an example clinical issue of interest.
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19 May 2009 | No Comments »
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