Structured Reporting of Medical Findings: Evaluation of a System in Gastroenterology

1992 ◽  
Vol 31 (04) ◽  
pp. 268-274 ◽  
Author(s):  
W. Gaus ◽  
J. G. Wechsler ◽  
P. Janowitz ◽  
J. Tudyka ◽  
W. Kratzer ◽  
...  
Keyword(s):  
Structured Data ◽  
Structured Reporting ◽  
Free Text ◽  
Clinical Documentation ◽  
Abdominal Sonography ◽  
Technical Examination ◽  
Medical Reports ◽  
Time Required ◽  
Upper Abdominal ◽  
Structured Approach

Abstract:A system using structured reporting of findings was developed for the preparation of medical reports and for clinical documentation purposes in upper abdominal sonography, and evaluated in the course of routine use. The evaluation focussed on the following parameters: completeness and correctness of the entered data, the proportion of free text, the validity and objectivity of the documentation, user acceptance, and time required. The completeness in the case of two clinically relevant parameters could be compared with an already existing database containing freely dictated reports. The results confirmed the hypothesis that, for the description of results of a technical examination, structured data reporting is a viable alternative to free-text dictation. For the application evaluated, there is even evidence of the superiority of a structured approach. The system can be put to use in related areas of application.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

scholarly journals Managing Free Text for Secondary Use of Health Data

2014 ◽  
Vol 23 (01) ◽  
pp. 167-169 ◽  
Author(s):  
N. Griffon ◽  
J. Charlet ◽  
S. J. Darmoni ◽  
Keyword(s):  
Language Processing ◽  
Meaningful Use ◽  
Health Data ◽  
Structured Data ◽  
The Other ◽  
Free Text ◽  
Reference Resolution ◽  
Comprehensive Review ◽  
Secondary Use ◽  
Corpus Creation

Summary Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM and natural language processing (NLP) published in 2013. Results: Four articles were selected, one focuses on Electronic Health Record (EHR) interoperability for clinical pathway personalization based on structured data. The other three focus on NLP (corpus creation, de-identification, and co-reference resolution) and highlight the increase in NLP tools performances. Conclusion: NLP tools are close to being seriously concurrent to humans in some annotation tasks. Their use could increase drastically the amount of data usable for meaningful use of EHR.

scholarly journals Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing

2021 ◽  
Vol 2 ◽  
Author(s):  
Denis Newman-Griffis ◽  
Jonathan Camacho Maldonado ◽  
Pei-Shu Ho ◽  
Maryanne Sacco ◽  
Rafael Jimenez Silva ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Functional Status ◽  
Language Processing ◽  
Free Text ◽  
Clinical Documentation ◽  
Disability Benefits ◽  
Domestic Life ◽  
Patient Functioning ◽  
Status Information

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation.Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity.Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used.Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

scholarly journals Combining structured and unstructured data in eMRs to create clinically-defined eMR-derived cohorts

2020 ◽  
Author(s):  
Charmaine Tam ◽  
Janice Gullick ◽  
Aldo Saavedra ◽  
Stephen Vernon ◽  
Gemma Figtree ◽  
...  
Keyword(s):  
Unstructured Data ◽  
Free Text ◽  
Local Health ◽  
Clinical Documentation ◽  
Inclusion Criteria ◽  
Clinical Criteria ◽  
Coronary Syndrome ◽  
Diagnostic Codes ◽  
Relative Contribution ◽  
Data Tables

Background There have been few studies describing how eMR systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process. The aim of this study is to provide a generalisable methodology for constructing clinically-defined eMR-derived patient cohorts using structured and unstructured data in eMRs. Methods Patients with possible acute coronary syndrome (ACS) were used as an exemplar. Cardiologists defined clinical criteria for patients presenting with possible ACS. These were mapped to data tables within the eMR system creating seven inclusion criteria comprised of structured data fields (orders and investigations, procedures, scanned electrocardiogram (ECG) images, and diagnostic codes) and unstructured clinical documentation. Data were extracted from two local health districts (LHD) in Sydney, Australia. Outcome measures included examination of the relative contribution of individual inclusion criteria to the identification of eligible encounters, comparisons between inclusion criterion and evaluation of consistency of data extracts across years and LHDs. Results Among 802,742 encounters in a 5 year dataset (1/1/13 to 30/12/17), the presence of an ECG image (54.8% of encounters) and symptoms and keywords in clinical documentation (41.4-64.0%) were used most often to identify presentations of possible ACS. Orders and investigations (27.3%) and procedures (1.4%), were less often present for identified presentations. Relevant ICD-10/SNOMED codes were present for 3.7% of identified encounters. Similar trends were seen when the two LHDs were examined separately, and across years. Conclusions Clinically-defined eMR-derived cohorts combining structured and unstructured data during cohort identification is prerequisite for critical validation work required for secondary use of eMR data.

scholarly journals Development of Query Strategies to Identify a Histologic Lymphoma Subtype in a Large Linked Database System

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Michael Graiser ◽  
Susan G. Moore ◽  
Rochelle Victor ◽  
Ashley Hilliard ◽  
Leroy Hill ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Sensitivity And Specificity ◽  
Cancer Registry ◽  
Medical Records ◽  
Patient Population ◽  
Registry Data ◽  
Free Text ◽  
Cancer Registry Data ◽  
Medical Reports ◽  
Lymphoma Subtype

Background Large linked databases (LLDB) represent a novel resource for cancer outcomes research. However, accurate means of identifying a patient population of interest within these LLDBs can be challenging. Our research group developed a fully integrated platform that provides a means of combining independent legacy databases into a single cancer-focused LLDB system. We compared the sensitivity and specificity of several SQL-based query strategies for identifying a histologic lymphoma subtype in this LLDB to determine the most accurate legacy data source for identifying a specific cancer patient population. Methods Query strategies were developed to identify patients with follicular lymphoma from a LLDB of cancer registry data, electronic medical records (EMR), laboratory, administrative, pharmacy, and other clinical data. Queries were performed using common diagnostic codes (ICD-9), cancer registry histology codes (ICD-O), and text searches of EMRs. We reviewed medical records and pathology reports to confirm each diagnosis and calculated the sensitivity and specificity for each query strategy. Results Together the queries identified 1538 potential cases of follicular lymphoma. Review of pathology and other medical reports confirmed 415 cases of follicular lymphoma, 300 pathology-verified and 115 verified from other medical reports. The query using ICD-O codes was highly specific (96%). Queries using text strings varied in sensitivity (range 7–92%) and specificity (range 86–99%). Queries using ICD-9 codes were both less sensitive (34–44%) and specific (35–87%). Conclusions Queries of linked-cancer databases that include cancer registry data should utilize ICD-O codes or employ structured free-text searches to identify patient populations with a precise histologic diagnosis.

scholarly journals Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Anobel Y. Odisho ◽  
Mark Bridge ◽  
Mitchell Webb ◽  
Niloufar Ameli ◽  
Renu S. Eapen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Gold Standard ◽  
Clinical Care ◽  
Data Entry ◽  
Structured Data ◽  
Lymph Node Status ◽  
Free Text ◽  
Routine Clinical Care ◽  
Pathology Reports ◽  
Pathology Data

Purpose Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postoperative pathology reports and a parallel structured data entry process for use by urologists during routine documentation care and compare accuracy when compared with manual abstraction and concordance between NLP and clinician-entered approaches. Materials and Methods From February 2016, clinicians used note templates with custom structured data elements (SDEs) during routine clinical care for men with prostate cancer. We also developed an NLP algorithm to parse radical prostatectomy pathology reports and extract structured data. We compared accuracy of clinician-entered SDEs and NLP-parsed data to manual abstraction as a gold standard and compared concordance (Cohen’s κ) between approaches assuming no gold standard. Results There were 523 patients with NLP-extracted data, 319 with SDE data, and 555 with manually abstracted data. For Gleason scores, NLP and clinician SDE accuracy was 95.6% and 95.8%, respectively, compared with manual abstraction, with concordance of 0.93 (95% CI, 0.89 to 0.98). For margin status, extracapsular extension, and seminal vesicle invasion, stage, and lymph node status, NLP accuracy was 94.8% to 100%, SDE accuracy was 87.7% to 100%, and concordance between NLP and SDE ranged from 0.92 to 1.0. Conclusion We show that a real-world deployment of an NLP algorithm to extract pathology data and structured data entry by clinicians during routine clinical care in a busy clinical practice can generate accurate data when compared with manual abstraction for some, but not all, components of a prostate pathology report.

Are Structured Data Structured Identically?

2005 ◽  
Vol 44 (05) ◽  
pp. 631-638 ◽  
Author(s):  
J. Roukema ◽  
A. M. van Ginneken ◽  
M. de Wilde ◽  
J. van der Lei ◽  
R. K. Los
Keyword(s):  
Clinical Practice ◽  
Data Use ◽  
Data Entry ◽  
Patient Data ◽  
Structured Data ◽  
Free Text ◽  
Patient Records ◽  
Paper Record ◽  
Recorded Data ◽  
Insight Into

Summary Objective: OpenSDE is an application that supports structured recording of narrative patient data to enable use of the data in both clinical practice and clinical research. Reliability and accuracy of collected data are essential for subsequent data use. In this study we analyze the uniformity of data entered with OpenSDE. Our objective is to obtain insight into the consensus and differences of recorded data. Methods: Three pediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared and all recorded findings were classified into one of six categories of difference. Results: Of all findings 22% were recorded identically; 17% of the findings were recorded differently (predominantly as free text); 61% was omitted, inferred, or in conflict with the paper record. Conclusion: The results of this study show that recording patient data using structured data entry does not necessarily lead to uniformly structured data.

scholarly journals Unstructured clinical documentation reflecting cognitive and behavioral dysfunction: toward an EHR-based phenotype for cognitive impairment

2018 ◽  
Vol 25 (9) ◽  
pp. 1206-1212
Author(s):  
Andrea L Gilmore-Bykovskyi ◽  
Laura M Block ◽  
Lily Walljasper ◽  
Nikki Hill ◽  
Carey Gleason ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Acute Care ◽  
Structured Data ◽  
Narrative Text ◽  
Unstructured Data ◽  
Cognitive Behavioral ◽  
Clinical Documentation ◽  
Behavioral Dysfunction ◽  
Increased Risk ◽  
Data Elements

Abstract Despite increased risk for negative outcomes, cognitive impairment (CI) is greatly under-detected during hospitalization. While automated EHR-based phenotypes have potential to improve recognition of CI, they are hindered by widespread under-diagnosis of underlying etiologies such as dementia—limiting the utility of more precise structured data elements. This study examined unstructured data on symptoms of CI in the acute-care EHRs of hip and stroke fracture patients with dementia from two hospitals. Clinician reviewers identified and classified unstructured EHR data using standardized criteria. Relevant narrative text was descriptively characterized and evaluated for key terminology. Most patient EHRs (90%) had narrative text reflecting cognitive and/or behavioral dysfunction common in CI that were reliably classified (κ 0.82). The majority of statements reflected vague descriptions of cognitive/behavioral dysfunction as opposed to diagnostic terminology. Findings from this preliminary derivation study suggest that clinicians use specific terminology in unstructured EHR fields to describe common symptoms of CI. This terminology can inform the design of EHR-based phenotypes for CI and merits further investigation in more diverse, robustly characterized samples.

scholarly journals An electronic documentation system improves the quality of admission notes: a randomized trial

2016 ◽  
Vol 24 (1) ◽  
pp. 123-129 ◽  
Author(s):  
Trevor Jamieson ◽  
Jonathan Ailon ◽  
Vince Chien ◽  
Ophyr Mourad
Keyword(s):  
Clinical Teaching ◽  
Free Text ◽  
Clinical Documentation ◽  
Electronic Documentation ◽  
Documentation System ◽  
General Internal ◽  
History Of ◽  
Text Content ◽  
The Impact

Objective: There are concerns that structured electronic documentation systems can limit expressivity and encourage long and unreadable notes. We assessed the impact of an electronic clinical documentation system on the quality of admission notes for patients admitted to a general medical unit. Methods: This was a prospective randomized crossover study comparing handwritten paper notes to electronic notes on different patients by the same author, generated using a semistructured electronic admission documentation system over a 2-month period in 2014. The setting was a 4-team, 80-bed general internal medicine clinical teaching unit at a large urban academic hospital. The quality of clinical documentation was assessed using the QNOTE instrument (best possible score = 100), and word counts were assessed for free-text sections of notes. Results: Twenty-one electronic-paper note pairs (42 notes) written by 21 authors were randomly drawn from a pool of 303 eligible notes. Overall note quality was significantly higher in electronic vs paper notes (mean 90 vs 69, P < .0001). The quality of free-text subsections (History of Present Illness and Impression and Plan) was significantly higher in the electronic vs paper notes (mean 93 vs 78, P < .0001; and 89 vs 77, P = .001, respectively). The History of Present Illness subsection was significantly longer in electronic vs paper notes (mean 172.4 vs 92.4 words, P = .0001). Conclusions: An electronic admission documentation system improved both the quality of free-text content and the overall quality of admission notes. Authors wrote more in the free-text sections of electronic documents as compared to paper versions.

scholarly journals Linking Free Text Documentation of Functioning and Disability to the ICF with Natural Language Processing

2021 ◽  
Author(s):  
Denis R Newman-Griffis ◽  
Jonathan Camacho Maldonado ◽  
Pei-Shu Ho ◽  
Maryanne Sacco ◽  
Rafael Jimenez Silva ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Functional Status ◽  
Language Processing ◽  
Free Text ◽  
Clinical Documentation ◽  
Disability Benefits ◽  
Domestic Life ◽  
Patient Functioning ◽  
Status Information

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation. Methods: We used NLP methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability and Health (ICF), and used the ICF's Activities and Participation domain to classify information about functioning in three key areas: Mobility, Self-Care, and Domestic Life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF codes to mentions of functional activity. Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based codes. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based codes. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF codes used. Conclusions: NLP can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation, but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning, and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

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