Impact of different definitions on estimates of accuracy of the diagnosis data in a clinical database.

Computerized medical databases are increasingly used for research. The influence of different definitions of the accuracy of matching on the estimated accuracy of diagnosis data was assessed in a database of visits to a public pediatric clinic. Differences between definitions involved 1) unit of analysis, 2) number of diagnoses required to match per visit, and/or 3) whether database contents are required to match the medical record or medical record contents are required to be matched in the database. Overall, 90% of diagnoses in the database (391/435) were accurately coded relative to the medical record. Alternatively, 77% of diagnoses listed in the medical record (391/506) were accurately coded in the database. When individual visits were used as the unit of analysis, estimates of accuracy using six definitions ranged from 65% to 92%. The most appropriate definition to use for estimating accuracy of diagnosis data likely depends on the purpose of the study. Use of two or more such definitions may enhance portrayal of the accuracy of diagnosis data.

[1]  M. Segal,et al.  Postmarketing surveillance in rheumatology: analysis of purpura and upper abdominal pain. , 1988, The Journal of rheumatology.

[2]  S A Spooner,et al.  Medical informatics and pediatrics. , 1996, Archives of pediatrics & adolescent medicine.

[3]  W. Baine,et al.  Epidemiologic trends in the evaluation and treatment of lower urinary tract symptoms in elderly male Medicare patients from 1991 to 1995. , 1998, The Journal of urology.

[4]  A. Krahn,et al.  The Costs of Recurrent Syncope of Unknown Origin in Elderly Patients , 1999, Pacing and clinical electrophysiology : PACE.

[5]  H T Sorensen,et al.  A framework for evaluation of secondary data sources for epidemiological research. , 1996, International journal of epidemiology.

[6]  M. Liang,et al.  Utilization of rheumatology physician services by the elderly. , 1998, The American journal of medicine.

[7]  E. Berkanovic.  An Appraisal of Medicaid Records as a Data Source , 1974, Medical care.

[8]  Jesse Green,et al.  How Accurate are Hospital Discharge Data for Evaluating Effectiveness of Care? , 1993, Medical care.

[9]  N. Christakis,et al.  The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims. , 1999, Medical care.

[10]  G. Samsa,et al.  Epidemiology of recurrent cerebral infarction: a medicare claims-based comparison of first and recurrent strokes on 2-year survival and cost. , 1999, Stroke.

[11]  C Safran,et al.  Using routinely collected data for clinical research. , 1991, Statistics in medicine.

[12]  R. Moore,et al.  Hospital admissions of HIV-infected patients from 1988 to 1992 in Maryland. , 1995, Journal of acquired immune deficiency syndromes and human retrovirology : official publication of the International Retrovirology Association.

[13]  E. Fisher,et al.  The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. , 1992, American journal of public health.

[14]  S. Greenland,et al.  A case-control study of prosthetic implants and selected chronic diseases in Medicare claims data. , 1998, Annals of epidemiology.

[15]  M A Hlatky,et al.  Clinical experience and predicting survival in coronary disease. , 1989, Archives of internal medicine.

[16]  B. Strom,et al.  Epidemiological pitfalls using Medicaid data in reproductive health research. , 1997, The Journal of maternal-fetal medicine.

[17]  D. Simborg,et al.  Classification of Ambulatory Care Using Patient-based, Time-oriented Indexes , 1985, Medical care.

[18]  D M Steinwachs,et al.  A Comparison of Ambulatory Medicaid Claims to Medical Records: A Reliabiligy Assessment , 1998, American journal of medical quality : the official journal of the American College of Medical Quality.

[19]  A. Mainous,et al.  The cost of antibiotics in treating upper respiratory tract infections in a medicaid population. , 1998, Archives of family medicine.

[20]  G. Cooper,et al.  The sensitivity of Medicare claims data for case ascertainment of six common cancers. , 1999, Medical care.

[21]  E. Fisher,et al.  Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? , 1992, JAMA.

[22]  J L Warren,et al.  Use of Medicare hospital and physician data to assess breast cancer incidence. , 1999, Medical care.

[23]  J. Fries,et al.  ARAMIS (the American Rheumatism Association Medical Information System). A prototypical national chronic-disease data bank. , 1986, The Western journal of medicine.

[24]  R. Stern,et al.  Using a claims database to investigate drug-induced Stevens-Johnson syndrome. , 1991, Statistics in medicine.

[25]  J. Luxenberg,et al.  Misclassification and Selection Bias When Identifying Alzheimer's Disease Solely from Medicare Claims Records , 1999, Journal of the American Geriatrics Society.

[26]  R. Hand,et al.  Secondary Diagnoses as Predictive Factors for Survival or Mortality in Medicare Patients with Acute Pneumonia , 1996, American journal of medical quality : the official journal of the American College of Medical Quality.

[27]  M. J. Feldman,et al.  Medical informatics and pediatrics. Decision-support systems. , 1995, Archives of pediatrics & adolescent medicine.

[28]  P. J. Smith,et al.  Otitis media-related antibiotic prescribing patterns, outcomes, and expenditures in a pediatric medicaid population. , 1997, Pediatrics.

[29]  C J McDonald,et al.  Practice databases and their uses in clinical research. , 1991, Statistics in medicine.