eQuality: electronic quality assessment from narrative clinical reports.

OBJECTIVE To evaluate an electronic quality (eQuality) assessment tool for dictated disability examination records. METHODS We applied automated concept-based indexing techniques to automated quality screening of Department of Veterans Affairs spine disability examinations that had previously undergone gold standard quality review by human experts using established quality indicators. We developed automated quality screening rules and refined them iteratively on a training set of disability examination reports. We applied the resulting rules to a novel test set of spine disability examination reports. The initial data set was composed of all electronically available examination reports (N=125,576) finalized by the Veterans Health Administration between July and September 2001. RESULTS Sensitivity was 91% for the training set and 87% for the test set (P-.02). Specificity was 74% for the training set and 71% for the test set (P=.44). Human performance ranged from 4% to 6% higher (P<.001) than the eQuality tool in sensitivity and 13% to 16% higher in specificity (P<.001). In addition, the eQuality tool was equivalent or higher in sensitivity for 5 of 9 individual quality indicators. CONCLUSION The results demonstrate that a properly authored computer-based expert systems approach can perform quality measurement as well as human reviewers for many quality indicators. Although automation will likely always rely on expert guidance to be accurate and meaningful, eQuality is an important new method to assist clinicians in their efforts to practice safe and effective medicine.

[1]  Peter J. Haug,et al.  Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports , 2000, J. Am. Medical Informatics Assoc..

[2]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[3]  Steven H. Brown,et al.  VistA - U.S. Department of Veterans Affairs national-scale HIS , 2003, Int. J. Medical Informatics.

[4]  Carol Friedman,et al.  A broad-coverage natural language processing system , 2000, AMIA.

[5]  K. S. Raghavan,et al.  Relationships in the Organization of Knowledge , 2001 .

[6]  Kent A. Spackman,et al.  SNOMED clinical terms: overview of the development process and project status , 2001, AMIA.

[7]  Carol Friedman,et al.  Facilitating Research in Pathology using Natural Language Processing , 2003, AMIA.

[8]  Peter J. Haug,et al.  Combining decision support methodologies to diagnose pneumonia , 2001, AMIA.

[9]  R A Greenes,et al.  SAPHIRE--an information retrieval system featuring concept matching, automatic indexing, probabilistic retrieval, and hierarchical relationships. , 1990, Computers and biomedical research, an international journal.

[10]  S. Kunte,et al.  Statistical computing , 1999 .

[11]  Allen C. Browne,et al.  UMLS language and vocabulary tools. , 2003, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[12]  Peter L. Elkin,et al.  Automated enhancement of description logic-defined terminologies to facilitate mapping to ICD9-CM , 2002, J. Biomed. Informatics.

[13]  Christian Lovis,et al.  Conceptual Search in Electronic Patient Record , 2001, MedInfo.

[14]  Prakash M. Nadkarni,et al.  Research Paper: Use of General-purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS , 2001, J. Am. Medical Informatics Assoc..

[15]  Peter L. Elkin,et al.  A controlled trial of automated classification of negation from clinical notes , 2005, BMC Medical Informatics Decis. Mak..

[16]  Mary F. Wisniewski,et al.  Electronic Interpretation of Chest Radiograph Reports to Detect Central Venous Catheters , 2003, Infection Control &#x0026; Hospital Epidemiology.

[17]  Clement J. McDonald,et al.  Automated Extraction and Normalization of Findings from Cancer-Related Free-Text Radiology Reports , 2003, AMIA.

[18]  J. Perlin,et al.  The Veterans Health Administration : Quality , Value , Accountability , and Information as Transforming Strategies for Patient-Centered Care , 2004 .

[19]  Peter L. Elkin,et al.  Coverage of Oncology Drug Indication Concepts and Compositional Semantics by SNOMED-CT® , 2003, AMIA.

[20]  D. S. Parker,et al.  Term Domain Distribution Analysis: a Data Mining Tool for Text Databases , 1999, Methods of Information in Medicine.

[21]  George Hripcsak,et al.  Coding Neuroradiology Reports for the Northern Manhattan Stroke Study: A Comparison of Natural Language Processing and Manual Review , 2000, Comput. Biomed. Res..

[22]  L. Tick,et al.  Medical Language Processing: Applications to Patient Data Representation and Automatic Encoding , 1995, Methods of Information in Medicine.

[23]  Thomas C. Rindflesch,et al.  EDGAR: extraction of drugs, genes and relations from the biomedical literature. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[24]  S Campbell Outcomes-based accreditation evolves slowly with JCAHO's Oryx initiative. , 1997, Health care strategic management.

[25]  Alan J. Forster,et al.  Research Paper: Electronically Screening Discharge Summaries for Adverse Medical Events , 2002, J. Am. Medical Informatics Assoc..

[26]  K Berry Legislative Forum: HEDIS 2.0: A Standardized Method to Evaluate Health Plans , 1993, Journal for healthcare quality : official publication of the National Association for Healthcare Quality.

[27]  Johnathan B Perlin,et al.  The Veterans Health Administration: quality, value, accountability, and information as transforming strategies for patient-centered care. , 2005, HealthcarePapers.

[28]  Payne,et al.  Evaluation of a Command-line Parser-based Order Entry Pathway for the Department of Veterans Affairs Electronic Patient Record , 2001 .

[29]  Robert H. Baud,et al.  Comparing General and Medical Texts for Information Retrieval Based on Natural Language Processing: An Inquiry into Lexical Disambiguation , 2001, MedInfo.

[30]  Peter J. Haug,et al.  A Comparison of Classification Algorithms to Automatically Identify Chest X-Ray Reports That Support Pneumonia , 2001, J. Biomed. Informatics.

[31]  Wanda Pratt,et al.  A Study of Biomedical Concept Identification: MetaMap vs. People , 2003, AMIA.

[32]  P. Good Permutation, Parametric, and Bootstrap Tests of Hypotheses , 2005 .

[33]  Carol A. Bean,et al.  Relationships in the Organization of Knowledge , 2001, Information Science and Knowledge Management.

[34]  Judith V. Douglas,et al.  Computerized Large Integrated Health Networks: The VA Sucess , 1997 .

[35]  Christoph Wick,et al.  Augmented Reality Simulator for Training in Two-Dimensional Echocardiography , 2000, Comput. Biomed. Res..

[36]  Christopher G. Chute,et al.  A randomized controlled trial of concept based indexing of Web page content , 2000, AMIA.

[37]  G Hripcsak,et al.  Natural language processing and its future in medicine. , 1999, Academic medicine : journal of the Association of American Medical Colleges.

[38]  J. Austin,et al.  Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. , 2002, Radiology.

[39]  N Sager,et al.  Natural language processing of asthma discharge summaries for the monitoring of patient care. , 1993, Proceedings. Symposium on Computer Applications in Medical Care.

[40]  N Sager,et al.  Automatic encoding of clinical narrative. , 1982, Computers in biology and medicine.

[41]  George Hripcsak,et al.  Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries , 1999, AMIA.

[42]  W R Hersh,et al.  Words, concepts, or both: optimal indexing units for automated information retrieval. , 1992, Proceedings. Symposium on Computer Applications in Medical Care.

[43]  Steven H. Brown,et al.  Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists. , 2006, Mayo Clinic proceedings.

[44]  Anita Burgun-Parenthoine,et al.  Automatic concept extraction from spoken medical reports , 2003, Int. J. Medical Informatics.

[45]  Carol Friedman,et al.  Natural Language Processing Challenges in HIV/AIDS Clinic Notes , 2003, AMIA.

[46]  Henry J. Lowe,et al.  Selective Automated Indexing of Findings and Diagnoses in Radiology Reports , 2001, J. Biomed. Informatics.

[47]  W. DuMouchel,et al.  Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing , 1995, Annals of Internal Medicine.

[48]  Peter L. Elkin,et al.  UMLS Concept Indexing for Production Databases: A Feasibility Study , 2001, J. Am. Medical Informatics Assoc..

[49]  C G Chute,et al.  An evaluation of concept based latent semantic indexing for clinical information retrieval. , 1992, Proceedings. Symposium on Computer Applications in Medical Care.

[50]  Theodore Speroff,et al.  A Model for Improving the Quality and Timeliness of Compensation and Pension Examinations in VA Facilities , 2003, Journal of healthcare management / American College of Healthcare Executives.

[51]  Betsy L. Humphreys,et al.  Relationships in Medical Subject Headings (MeSH) , 2001 .

[52]  W R Hersh,et al.  A Comparison of Two Methods for Indexing and Retrieval from a Full-text Medical Database , 1992, Medical decision making : an international journal of the Society for Medical Decision Making.

[53]  Yang Huang,et al.  Research Paper: A Pilot Study of Contextual UMLS Indexing to Improve the Precision of Concept-based Representation in XML-structured Clinical Radiology Reports , 2003, J. Am. Medical Informatics Assoc..

[54]  Peter L. Elkin Computerizing Large Integrated Health Networks: The VA Success (Computers in Health Care series) , 1998 .

[55]  P. Marzullo,et al.  Myocardial Viability: Nuclear Medicine Versus Stress Echocardiography , 1995, Echocardiography.

[56]  Brian Armstrong,et al.  Formative Evaluation to Guide Early Deployment of an Online Content Management Tool for Medical Curriculum , 2003, AMIA.

[57]  C Lovis,et al.  Analysis of medical texts based on a sound medical model. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[58]  Michael Krauthammer,et al.  A knowledge model for the interpretation and visualization of NLP-parsed discharged summaries , 2001, AMIA.

[59]  Peter L. Elkin,et al.  A randomized controlled trial of the accuracy of clinical record retrieval using SNOMED-RT as compared with ICD9-CM , 2001, AMIA.

[60]  Wendy W. Chapman,et al.  A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.

[61]  Carol Friedman,et al.  Limited parsing of notational text visit notes: ad-hoc vs. NLP approaches , 2000, AMIA.

[62]  Kent A. Spackman,et al.  SNOMED RT: a reference terminology for health care , 1997, AMIA.