Comparison of information content of structured and narrative text data sources on the example of medication intensification.

OBJECTIVE To compare information obtained from narrative and structured electronic sources using anti-hypertensive medication intensification as an example clinical issue of interest. DESIGN A retrospective cohort study of 5,634 hypertensive patients with diabetes from 2000 to 2005. MEASUREMENTS The authors determined the fraction of medication intensification events documented in both narrative and structured data in the electronic medical record. The authors analyzed the relationship between provider characteristics and concordance between intensifications in narrative and structured data. As there is no gold standard data source for medication information, the authors clinically validated medication intensification information by assessing the relationship between documented medication intensification and the patients' blood pressure in univariate and multivariate models. RESULTS Overall, 5,627 (30.9%) of 18,185 medication intensification events were documented in both sources. For a medication intensification event documented in narrative notes the probability of a concordant entry in structured records increased by 11% for each study year (p < 0.0001) and decreased by 19% for each decade of provider age (p = 0.035). In a multivariate model that adjusted for patient demographics and intraphysician correlations, an increase of one medication intensification per month documented in either narrative or structured data were associated with a 5-8 mm Hg monthly decrease in systolic and 1.5-4 mm Hg decrease in diastolic blood pressure (p < 0.0001 for all). CONCLUSION Narrative and structured electronic data sources provide complementary information on anti-hypertensive medication intensification. Clinical validity of information in both sources was demonstrated by correlation with changes in blood pressure.

[1]  Diane L. Seger,et al.  Application of Information Technology: Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care , 2006, J. Am. Medical Informatics Assoc..

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

[3]  Christian Lovis,et al.  Power of expression in the electronic patient record: structured data or narrative text? , 2000, Int. J. Medical Informatics.

[4]  V. Durkalski,et al.  Therapeutic Inertia Is an Impediment to Achieving the Healthy People 2010 Blood Pressure Control Goals , 2006, Hypertension.

[5]  R. Weinshilboum,et al.  The sixth report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure. , 1997, Archives of internal medicine.

[6]  Richard S. Cooper,et al.  Hypertension prevalence and blood pressure levels in 6 European countries, Canada, and the United States. , 2003, JAMA.

[7]  R. Tamblyn,et al.  The use of prescription claims databases in pharmacoepidemiological research: the accuracy and comprehensiveness of the prescription claims database in Québec. , 1995, Journal of clinical epidemiology.

[8]  P Parks,et al.  Documentation guidelines for evaluation and management services. , 1995, Bulletin of the American College of Surgeons.

[9]  R. Holman,et al.  Risk factors for coronary artery disease in non-insulin dependent diabetes mellitus: United Kingdom prospective diabetes study (UKPDS: 23) , 1998, BMJ.

[10]  Jonathan S Einbinder,et al.  Effect of Board Certification on Antihypertensive Treatment Intensification in Patients With Diabetes Mellitus , 2008, Circulation.

[11]  M. Engelgau,et al.  A Diabetes Report Card for the United States: Quality of Care in the 1990s , 2002, Annals of Internal Medicine.

[12]  C J McDonald,et al.  A Framework for Capturing Clinical Data Sets from Computerized Sources , 1997, Annals of Internal Medicine.

[13]  S. R. Searle,et al.  Linear Models For Unbalanced Data , 1988 .

[14]  S. de Lusignan,et al.  The use of routinely collected computer data for research in primary care: opportunities and challenges. , 2006, Family practice.

[15]  Detection The sixth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC VI) , 1997 .

[16]  George Hripcsak,et al.  Automated encoding of clinical documents based on natural language processing. , 2004, Journal of the American Medical Informatics Association : JAMIA.

[17]  Ward Hj,et al.  Inadequate management of blood pressure in a hypertensive population. , 1999 .

[18]  J. Meigs,et al.  Low rates of medical regimen change , 2005 .

[19]  A Hasman,et al.  Medical narratives in electronic medical records. , 1997, International journal of medical informatics.

[20]  R. Ratner,et al.  Achievement of American Diabetes Association clinical practice recommendations among U.S. adults with diabetes, 1999-2002: the National Health and Nutrition Examination Survey. , 2006, Diabetes care.

[21]  S. Urbina,et al.  Psychological testing, 7th ed. , 1997 .

[22]  R. Simes,et al.  An improved Bonferroni procedure for multiple tests of significance , 1986 .

[23]  Dean F Sittig,et al.  Application of Information Technology j MediClass : A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical , 2005 .

[24]  Daniel W. Jones,et al.  Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure and evidence from new hypertension trials. , 2004, Hypertension.

[25]  Peter J. Haug,et al.  Natural language processing to extract medical problems from electronic clinical documents: Performance evaluation , 2006, J. Biomed. Informatics.

[26]  S Greenland,et al.  Principles of multilevel modelling. , 2000, International journal of epidemiology.

[27]  Clement J. McDonald,et al.  Viewpoint: The Barriers to Electronic Medical Record Systems and How to Overcome Them , 1997, J. Am. Medical Informatics Assoc..

[28]  Marla R. Miller,et al.  Increasing adherence to a community-based guideline for acute sinusitis through education, physician profiling, and financial incentives. , 2004, The American journal of managed care.

[29]  Marc Berg,et al.  The contextual nature of medical information , 1999, Int. J. Medical Informatics.

[30]  Peter J. Haug,et al.  MPLUS: a probabilistic medical language understanding system , 2002, ACL Workshop on Natural Language Processing in the Biomedical Domain.

[31]  Alexander Turchin,et al.  Identification of patients with diabetes from the text of physician notes in the electronic medical record. , 2005, Diabetes care.

[32]  Daniel W. Jones,et al.  The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. , 2003, JAMA.

[33]  Christopher G. Chute,et al.  Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier , 2005, J. Biomed. Informatics.

[34]  Richard W. Grant,et al.  Case Report: Using Regular Expressions to Abstract Blood Pressure and Treatment Intensification Information from the Text of Physician Notes , 2006, J. Am. Medical Informatics Assoc..

[35]  Shayle R. Searle,et al.  Linear Models for Unbalanced Data. , 1990 .

[36]  R. Hayward,et al.  Avoiding pitfalls in chronic disease quality measurement: a case for the next generation of technical quality measures. , 2001, The American journal of managed care.

[37]  H. Parving,et al.  Predictors for the development of microalbuminuria and macroalbuminuria in patients with type 1 diabetes: inception cohort study , 2004, BMJ : British Medical Journal.

[38]  D. Bates,et al.  Comprehensive computerised primary care records are an essential component of any national health information strategy: report from an international consensus conference. , 2004, Informatics in primary care.

[39]  G. Molenberghs,et al.  Models for Discrete Longitudinal Data , 2005 .

[40]  Stephen J. Aldington,et al.  UKPDS 50: Risk factors for incidence and progression of retinopathy in Type II diabetes over 6 years from diagnosis , 2001, Diabetologia.

[41]  Y. Hochberg A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[42]  A. Giani,et al.  UKPDS 50 : risk factors for incidence and progression of retinopathy in type II diabetes over 6 years from diagnosis , 2022 .

[43]  Martin Romacker,et al.  MedSynDikate - a natural language system for the extraction of medical information from findings reports , 2002, Int. J. Medical Informatics.

[44]  William Branch,et al.  Clinical Inertia , 2001, Annals of Internal Medicine.

[45]  Stephen H Walsh The clinician's perspective on electronic health records and how they can affect patient care , 2004, BMJ : British Medical Journal.

[46]  S Greenland,et al.  Multilevel Modeling in Epidemiology with GLIMMIX , 2000, Epidemiology.