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 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.

A Synoptic Reporting System for Bone Marrow Aspiration and Core Biopsy Specimens

2006 ◽  
Vol 130 (12) ◽  
pp. 1825-1829 ◽  
Author(s):  
Manjula Murari ◽  
Rakesh Pandey
Keyword(s):  
Bone Marrow ◽  
Reporting System ◽  
Data Entry ◽  
Structured Data ◽  
Pathology Report ◽  
Free Text ◽  
Electronic Systems ◽  
Report Generation ◽  
Pathology Reports ◽  
Synoptic Reporting

Abstract Context.—Advances in information technology have made electronic systems productive tools for pathology report generation. Structured data formats are recommended for better understanding of pathology reports by clinicians and for retrieval of pathology reports. Suitable formats need to be developed to include structured data elements for report generation in electronic systems. Objective.—To conform to the requirement of protocol-based reporting and to provide uniform and standardized data entry and retrieval, we developed a synoptic reporting system for generation of bone marrow cytology and histology reports for incorporation into our hospital information system. Design.—A combination of macro text, short preformatted templates of tabular data entry sheets, and canned files was developed using a text editor enabling protocol-based input. The system is flexible and has facility for appending free text entry. It also incorporates SNOMED coding and codes for teaching, research, and internal auditing. Results.—This synoptic reporting system is easy to use and adaptable. Features and advantages include pick-up text with defined choices, flexibility for appending free text, facility for data entry for protocol-based reports for research use, standardized and uniform format of reporting, comparable follow-up reports, minimized typographical and transcription errors, and saving on reporting time, thus helping shorten the turnaround time. Conclusions.—Simple structured pathology report templates are a powerful means for supporting uniformity in reporting as well as subsequent data viewing and extraction, particularly suitable to computerized reporting.

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.

Structured Data Entry for Reliable Acquisition of Pharmacokinetic Data

1996 ◽  
Vol 35 (03) ◽  
pp. 261-264 ◽  
Author(s):  
T. Schromm ◽  
T. Frankewitsch ◽  
M. Giehl ◽  
F. Keller ◽  
D. Zellner
Keyword(s):  
Text Processing ◽  
Data Entry ◽  
Processing System ◽  
Database System ◽  
Structured Data ◽  
Pharmacokinetic Parameters ◽  
Pharmacokinetic Data ◽  
Probability Of Error ◽  
Error Frequency ◽  
Estimated Error

Abstract:A pharmacokinetic database was constructed that is as free of errors as possible. Pharmacokinetic parameters were derived from the literature using a text-processing system and a database system. A random data sample from each system was compared with the original literature. The estimated error frequencies using statistical methods differed significantly between the two systems. The estimated error frequency in the text-processing system was 7.2%, that in the database system 2.7%. Compared with the original values in the literature, the estimated probability of error for identical pharmacokinetic parameters recorded in both systems is 2.4% and is not significantly different from the error frequency in the database. Parallel data entry with a text-processing system and a database system is, therefore, not significantly better than structured data entry for reducing the error frequency.

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 127-128
Keyword(s):  
Decision Support ◽  
Postoperative Delirium ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Free Text ◽  
Health Records ◽  
Support Tool ◽  
Team Selection ◽  
Order Sets

Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017 Jun;102:71-9 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28495350/ Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: A decision support tool for surgical team selection. Artif Intell Med 2017 Feb;76:16-26 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28363285/ Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017 Jul 1;24(4):806-12 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocx010 Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017 Dec;152:105-14 https://linkinghub.elsevier.com/retrieve/pii/S0169-2607(17)31154-9

scholarly journals Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents

2021 ◽  
pp. 1063293X2098297
Author(s):  
Ivar Örn Arnarsson ◽  
Otto Frost ◽  
Emil Gustavsson ◽  
Mats Jirstrand ◽  
Johan Malmqvist
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Unstructured Data ◽  
Free Text ◽  
Engineering Change ◽  
Engineering Documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

Consultation of Medical Narratives in the Electronic Medical Record

1999 ◽  
Vol 38 (04/05) ◽  
pp. 289-293 ◽  
Author(s):  
H. J. Tange
Keyword(s):  
Information Retrieval ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Data Entry ◽  
Daily Practice ◽  
Evaluation Study ◽  
System Level ◽  
Free Text ◽  
Patient Case ◽  
Medical Narratives

AbstractThis article presents an overview of a research project concerning the consultation of medical narratives in the electronic medical record (EMR). It describes an analysis of user needs, the design and implementation of a prototype EMR system, and the evaluation of the ease of consultation of medical narratives when using this system. In a questionnaire survey, 85 hospital physicians judged the quality of their paper-based medical record with respect to data entry, information retrieval and some other aspects. Participants were more positive about the paper medical record than the literature suggests. They wished to maintain the flexibility of data entry but indicated the need to improve the retrieval of information. A prototype EMR system was developed to facilitate the consultation of medical narratives. These parts were divided into labeled segments that could be arranged source-oriented and problem-oriented. This system was used to evaluate the ease of information retrieval of 24 internists and 12 residents at a teaching hospital when using free-text medical narratives divided at different levels of detail. They solved, without time pressure, some predefined problems concerning three voluminous, inpatient case records. The participants were randomly allocated to a sequence that was balanced by patient case and learning effect. The division of medical narratives affected speed, but not completeness of information retrieval. Progress notes divided into problem-related segments could be consulted 22% faster than when undivided. Medical history and physical examination divided into segments at organ-system level could be consulted 13% faster than when divided into separate questions and observations. These differences were statistically significant. The fastest divisions were also appreciated as the best combination of easy searching and best insight in the patient case. The results of our evaluation study suggest a trade-off between searching and reading: too much detailed segments will delay the consultation of medical narratives. Validation of the results in daily practice is recommended.

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.

Live-action mass-casualty training and virtual world training

2016 ◽  
Vol 60 (1) ◽  
pp. 2103-2107 ◽  
Author(s):  
Keith T. Shubeck ◽  
Scotty D. Craig ◽  
Xiangen Hu
Keyword(s):  
Virtual World ◽  
Domain Knowledge ◽  
Mass Casualty ◽  
Learning Technologies ◽  
Learning Needs ◽  
Web Based ◽  
Tutoring Systems ◽  
Aeromedical Evacuation ◽  
User Input ◽  
Live Action

Live-action training simulations with expert facilitators are considered by many to be the gold-standard in training environments. However, these training environments are expensive, provide many logistical challenges, and may not address the individual’s learning needs. Fortunately, advances in distance-based learning technologies have provided the foundation for inexpensive and effective learning environments that can simultaneously train and educate students on a much broader scale than live-action training environments. Specifically, intelligent tutoring systems (ITSs) have been proven to be very effective in improving learning outcomes. The Virtual Civilian Aeromedical Evacuation Sustainment Training (VCAEST) interface takes advantage of both of these technologies by enhancing a virtual world with a web-based ITS, AutoTutor LITE (Learning in Interactive Training Environments). AutoTutor LITE acts as a facilitator in the virtual world by providing just-in-time feedback, presenting essential domain knowledge, and by utilizing tutoring dialogues that automatically assess user input. This paper will discuss the results of an experimental evaluation of the VCAEST environment compared to an expert-led live-action training simulation.

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