The importance of laboratory data for comparing outcomes and detecting 'outlier' wards in the treatment of patients with pneumonia.
OBJECTIVE
To evaluate whether routine laboratory data can improve the ability to compare risk-adjusted outcomes of different medical wards, and to detect 'outlier' wards with significantly better or worse outcome.


METHODS
Patient data were taken from the Combined Patient Database Systematic Management and Research Tool, a database created by merging different computerized sources at a tertiary care hospital. All patients admitted to internal wards with the diagnosis of pneumonia during the years 1991-1995 were included (n = 2734). The outcome variable was mortality 30 days post-admission. We used three comorbidity measures based on ICD-9-CM codes as possible predictors of mortality: secondary diagnoses; the Health Care Financing Administration severity index; and the Charlson comorbidity index. Models were created using logistic regression. To each model, laboratory data gathered in the first 48 hours after admission were added. To identify 'outlier' services we determined whether the patients' ward was an independent predictor of mortality. The area under the receiver operator curve (ROC) of the models was used for comparisons.


RESULTS
The area under the ROC was 0.65-0.72 for the models based on age and comorbid diagnoses. The addition of laboratory data improved markedly the discriminatory ability of each of the models, as reflected by an increase in the area under the ROC to 0.83-0.84. An 'outlier' ward with a higher risk-adjusted mortality rate was identified only by the models that included laboratory data.


CONCLUSIONS
Basic, automated, routinely gathered laboratory data added significantly to the discriminatory power of risk models based on administrative data with abstracted diagnoses. Addition of laboratory data improved the ability to identify providers with possible exceptional quality of care.
Maor
Y
Y
Gertner Institute for Health Services Research, Tel Aviv University, Sheba Medical Center, Israel.
Rubin
H R
HR
Gabbai
U
U
Mozes
B
B
D000367:Age Factors; D000368:Aged; D002984:Clinical Laboratory Information Systems / Q000706:statistics & numerical data*; D015897:Comorbidity; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D005500:Follow-Up Studies; D006757:Hospital Units / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D007557:Israel; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016311:Outliers, DRG; D011014:Pneumonia / Q000175:diagnosis / Q000401:mortality*; D018570:Risk Assessment; D012720:Severity of Illness Index