Patients with newly diagnosed carcinoma of the breast: validation of a claim-based identification algorithm.

The objectives of this study were to validate a claims-based algorithm for identification of patients with newly diagnosed carcinoma of the breast and to optimize the algorithm. Claims data from all females aged 21 years or older who enrolled in a large California health maintenance organization during the study period from October 1, 1994 through March 31, 1996 were analyzed. Medical records of the patients identified through the claims-based algorithm were reviewed to determine whether the patients were correctly identified. The initial algorithm had a positive predictive value of 84% which was similar to the previous study. The percentages of correct identification significantly increased with the patient's age at diagnosis. Other patient demographic characteristics and facility characteristics were not related to the accuracy of the identification. Using a classification tree procedure and additional information from the false-positive cases, the initial algorithm was modified for improvement. The best-modified algorithm had a positive predictive value of 92% while only 0.5% (4/837) of the true-positive cases were excluded. The results once again demonstrated that patients with newly diagnosed carcinomas of the breast can be identified using claims data. These databases provide an efficient and effective tool for performing health services studies on large patient populations.

[1]  R. Brook,et al.  The Appropriateness of Using a Medical Procedure: Is Information in the Medical Record Valid? , 1987, Medical care.

[2]  J. Wennberg,et al.  Dealing with medical practice variations: a proposal for action. , 1984, Health affairs.

[3]  J. Samet,et al.  Geographic variation in the treatment of localized breast cancer. , 1992, The New England journal of medicine.

[4]  J. Avorn,et al.  Medicaid data as a resource for epidemiologic studies: strengths and limitations. , 1989, Journal of clinical epidemiology.

[5]  C. Eggers,et al.  CANCER OF THE BREAST , 1941, Annals of surgery.

[6]  N. Roos,et al.  Use of claims data systems to evaluate health care outcomes. Mortality and reoperation following prostatectomy. , 1987, JAMA.

[7]  E P Steinberg,et al.  Impact of Claims Data Research on Clinical Practice , 1990, International Journal of Technology Assessment in Health Care.

[8]  Spector Wd Utilization and charges for terminal cancer patients in Rhode Island. , 1984 .

[9]  J L Warren,et al.  Determination of lung cancer incidence in the elderly using Medicare claims data. , 1993, American journal of epidemiology.

[10]  The use of administrative data as the first step in the continuous quality improvement process. , 1996, American journal of medical quality : the official journal of the American College of Medical Quality.

[11]  L L Roos,et al.  Using computers to identify complications after surgery. , 1985, American journal of public health.

[12]  A. Buist,et al.  Use of an automated prescription database to identify individuals with asthma. , 1995, Journal of clinical epidemiology.

[13]  V. Devita,et al.  Cancer : Principles and Practice of Oncology , 1982 .

[14]  Trevor Hastie,et al.  Statistical Models in S , 1991 .

[15]  K. Cleary Using claims data to measure and improve the MMR immunization rate in an HMO. , 1995, The Joint Commission journal on quality improvement.

[16]  Long Sh,et al.  Medical expenditures of terminal cancer patients during the last year of life. , 1984 .

[17]  A B Nattinger,et al.  Geographic variation in the use of breast-conserving treatment for breast cancer. , 1992, The New England journal of medicine.

[18]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[19]  J L Warren,et al.  Measuring the incidence of cancer in elderly americans using medicare claims data , 1994, Cancer.

[20]  N. Roos,et al.  Centralization, Certification, and Monitoring: Readmissions and Complications After Surgery , 1986, Medical care.

[21]  J. Glick,et al.  Breast Cancer Treatment: A Comprehensive Guide to Management , 1991 .

[22]  K. Lohr,et al.  Geographic variations in the use of services: do they have any clinical significance? , 1984, Health affairs.

[23]  W. Ray,et al.  Identification of fractures from computerized Medicare files. , 1992, Journal of clinical epidemiology.

[24]  N. Powe,et al.  Costs vs quality in different types of primary care settings. , 1994, JAMA.

[25]  A. Lawthers,et al.  Developing a Quality Improvement Database Using Health Insurance Data: A Guided Tour with Application to Medicare's National Claims History File , 1995, American journal of medical quality : the official journal of the American College of Medical Quality.

[26]  K. Lohr Use of Insurance Claims Data in Measuring Quality of Care , 1990, International Journal of Technology Assessment in Health Care.

[27]  K. Lohr,et al.  Geographic Variations in the Use of Services , 1991 .

[28]  Hsia Dc,et al.  Accuracy of Diagnostic Coding for Medicare Patients under the Prospective-Payment System , 1988 .

[29]  E. Hannan,et al.  Using Medicare claims data to assess provider quality for CABG surgery: does it work well enough? , 1997, Health services research.

[30]  J. Burkhardt,et al.  Utilization of radiologic services in different payment systems and patient populations. , 1996, Radiology.