A statistical model is demonstrated for finding outpa tient clinical laboratories having high frequencies of bad patient outcomes, after testing, that may indicate testing errors. A sample of Medicare Part A and Part B claims for 30,685 digoxin tests for 1985 through 1987 was analyzed. Deaths or digoxin-related hospitaliza tions within 14 days of digoxin testing are defined as "adverse events" potentially influenced by test informa tion. Approximately 3.3% of digoxin tests were followed by adverse events with two-thirds resulting in hospital ization or death. Adverse events were (a) lower in states with stronger laboratory regulations, (b) 15% higher whenever the site of testing switched, (c) unre lated to testing in low volume physician office labora tories, and (d) unrelated to frequency of digoxin testing in previous 6 months. Results a and b are consistent with findings for prothrombin testing but c and d are not. Differences are consistent with technical charac teristics of the tests.
Get full access to this article
View all access options for this article.
References
1.
U. S. Department of Health and Human Services, Health Care Financing Administration.Medicare, Medicaid and CLIA Programs ; Regulations Implementing the Clinical Laboratory Improvement Amendments of 1988 (CLIA'88) Proposed Rules. Federal Register No. 98, May 21, 1990;55:20896-20959.
2.
Kenney ML, Greenberg DPFinal Report on Assessment of Clinical Laboratory Regulations. Washington D.C.: U..S Department of Health and Human Services, Office of Assistant Secretary for Planning and Evaluation HHS 10084-0035, April 1986.
3.
U. S. Department of Health and Human Services, Public Health Service, Agency for Health Care Policy and Research.Research Activities, No. 138, February 1991.
4.
Mennemeyer ST , Winkelman JWDownstream outcomes: using insurance claims data to screen for errors in clinical laboratory testing. Qual Rev Bull1991;17:194-199.
5.
Mennemeyer, ST, Winkelman JWSearching for inaccuracy in clinical laboratory testing using Medicare data: evidence for prothrombin time. JAMA1993;269:1030-1033.
6.
Nelson CRDrug utilization in office practice, National Ambulatory Medical Care Survey, 1990. Advance Data from Vital and Health Statistics, No. 232. Hyattsville, MD: National Center for Health Statistics , 1993.
7.
Pauly MV, Mennemeyer ST, Eisenberg JM, Reardon LBAssessment of the Effects of Reimbursement Policy on the Utilization of Clinical Laboratory Testing and the Propensity of Physicians to Perform In-office Testing. Contract HHS-100-89-0017. Cambridge, MA: Abt Associates, Inc., 1991.
8.
Hosmer DW, Lemeshow S.Applied Logistic Regression. New York; John Wiley & Sons, 1989.
9.
Steinberg D. , Cola R Logit: A Supplementary ModuleforSYSTAT. Evanston, IL: ILSYSTAT, Inc., 1991
10.
Keil JE, Sutherland SE, Knapp RG, Lackland DT, Gazes PC, Tyroler HAMortality rates and risk factors for coronary disease in black as compared to white men and women. N Engl J Med1993;329:73-78.
11.
Iezzoni LI, Burnside S., Sickles L., et al. Coding of acute myocardial infarction: clinical and policy implications . Ann Intern Med1988:109:745-751.
12.
Iezzoni LI, Foley SM, Daley J., et al. Comorbidities, complications, and coding bias. JAMA1992;267:2197-2203.
13.
Hannan EL, Kilburn M., Lindsey ML, Lewis R.Clinical versus administrative data bases for CABG surgery: does it matter?Med Care1992:30:892-907.
14.
Keeler EB, Rubenstein LV, Kahn KL, et al. Hospital characteristics and quality of care. JAMA1992268:1709-1714.
15.
Quam L., Ellis Lbm, Venus P., Clouse J., Taylor CG, Leatherman S.Using claims data for epidemiological research: the concordance of claims-based criteria with the medical record and patient survey for identifying a hypertensive population. Med Care1993;31:498-507.
