Background: Health information technology offers a powerful tool to monitor the performance of a healthcare system. Advances in computer technology and capacity combined with lower start-up costs will allow developing countries to achieve greater impact when they initiate electronic health information systems. We focused on the integrated health information system that was established in Taiwan in conjunction with the launch of the National Health Insurance (NHI) programme. We used data from that health information system to conduct a cost-effectiveness analysis of chemotherapy use among breast cancer patients. We then used this analysis to discuss what policy makers can learn from this type of analysis. Methods: We identified a cohort of patients in the NHI Research Database who had been diagnosed with breast cancer in 2001 and had received chemotherapy following surgical removal of the tumour. We followed these patients for 3 years and conducted a cost-effectiveness analysis from the payer's perspective. Using the net benefit regression approach, we compared the cost effectiveness of the two most commonly prescribed first-line chemotherapy regimens for the treatment of breast cancer in 2001 in Taiwan. The dependent variable of the regression model was the individual-level net benefit, and the independent variables included a binary variable indicating the choice of chemotherapy regimen, the patients' age, co-morbidity, type of surgery, geographic region and type of treatment facility. We employed both frequentist and Bayesian approaches in our net benefit regression analyses. In the Bayesian analysis, we applied non-informative priors to all parameters in the base-case analyses. We then explored the use of informative priors in the sensitivity analysis, using cost-effectiveness data published in the literature to form the prior distributions for the relevant parameters. Results: Over 60% of surgically treated breast cancer patients received either CMF (cyclophosphamide, methotrexate, fluorouracil) or CEF (cyclophosphamide, epirubicin, fluorouracil). A comparison of patient characteristics indicated that patients in the CEF group tended to be younger (47.8 vs 49.1 years; p= 0.016), and were significantly more likely to have undergone a mastectomy (84% vs 76%; p < 0.001) and to have been treated in a teaching hospital (26% vs 13%; p < 0.001). We also observed significant variations in geographic region of the location of facilities between treatment groups. On average, CEF was not cost effective in the treatment of patients with breast cancer in Taiwan, although analyses stratified by geographic region suggested a wide variation across regions. At a societal willingness to pay (WTP) of new Taiwanese dollar ($)1 500 000 ($US80 000), the probability that CEF was more cost effective than CMF was 0.0%, 0.0%, 0.0% and 3.9% for the Taipei metropolitan area, and the north, middle and the combined south and east region, respectively; the probability became 0.6%, 0.0%, 1.3% and 54.5%, respectively, at a WTP of $N5 000 000 ($US270 000). After co-variate adjustments, the probabilities were 0.0%, 0.0%, 0.0% and 0.8%, respectively at a WTP of $1500 000, and were 0.0%, 0.0%, 1.4% and 34.7% at $5 000 000. Sensitivity analyses showed that CEF potentially could have been more cost effective than CMF within a reasonable range of societal WTP (i.e. $1 000000-3000000 or $US55 000-160000) had the optimal dosage level for CEF been established for breast cancer patients in Taiwan. Conclusions: A population-based, fully integrated electronic health information system provides useful data to assess the cost effectiveness of competing treatments and interventions in current practice. This research may potentially inform policy makers of modifications that can be instituted to improve the cost effectiveness of a new therapy. However, findings from this study need to be interpreted with caution because the study provided information only on the short-term cost effectiveness (i.e. 3 years) of CEF compared with CMF. It is possible that a future analysis will reach a different conclusion when more years of follow-up data become available.