A statistical model for predicting the outcome in breast cancer malpractice lawsuits.
OBJECTIVE
To analyze specific medical, legal, and cost factors that predict the probability of successfully defending lawsuits filed because of failure to diagnose breast cancer.


METHODS
Seventy-six malpractice cases handled by the Massachusetts Medical Professional Insurance Association between June 29, 1983 and December 30, 1993 were abstracted and analyzed using univariate analysis, multivariate stepwise logistic and least-square regression analysis, and the Cox proportional hazards model to identify statistically significant associations between clinical factors and medicolegal outcomes.


RESULTS
Obstetrician-gynecologists were defendants in the largest number of cases (38) and incurred the highest total indemnity ($7,629,570). The probability of defending a suit successfully increased with smaller tumor size and younger patients (less than 40 years of age). The failure to perform a biopsy was associated with a decreased probability of successful defense. Variables predicting high case cost included younger patient age, an increased length of delay in diagnosis, and the failure to perform a biopsy. The presence of metastasis at diagnosis was associated with an increased interval from diagnosis to the initiation of a suit.


CONCLUSIONS
Statistical models that use medicolegal and cost factors can predict both the probability of a successful defense and the total cost of a breast cancer malpractice case.
Zylstra
S
S
Department of Obstetrics and Gynecology, University of Massachusetts Medical School, Worcester.
Bors-Koefoed
R
R
Mondor
M
M
Anti
D
D
Giordano
K
K
Resseguie
L J
LJ
D000328:Adult; D001943:Breast Neoplasms / Q000453:epidemiology; D003365:Costs and Cost Analysis; D005260:Female; D006176:Gynecology / Q000331:legislation & jurisprudence*; D006801:Humans; D016015:Logistic Models; D008318:Malpractice / Q000191:economics / Q000331:legislation & jurisprudence* / Q000706:statistics & numerical data*; D008404:Massachusetts / Q000453:epidemiology; D015233:Models, Statistical; D009774:Obstetrics / Q000331:legislation & jurisprudence*; D012044:Regression Analysis; D012308:Risk Management