OBJECTIVES: To develop and test a new method of estimating the ICER utilizing the harmonic mean. METHODS: A statistically-efficient point and 95% confidence interval (CI) estimator of the ICER was derived that utilized the harmonic mean of costs and effects, hence applying the inverse of the mean of the inverses for use in statistical summarization.  An additional correction factor developed through computational intelligence was also incorporated to capture existing information from the usual bootstrap ICER estimator.  A simulation study of 1111 replications with 999 bootstrap resamples each utilizing Matlab R2012b was undertaken across illustrative positive and negative correlation structures of costs and outcomes for varying sample sizes of treatment and referent groups.  Results were presented as relative efficiencies for point estimators, while coverage probability, coverage error, length, left and right bias, and relative bias were presented for the 95% CI. RESULTS: Compared to the usual bootstrap approach, optimal methods based upon the harmonic mean yielded point estimates with greater relative efficiency across all analytic scenarios, ranging from 103.22% to 111.03%.  The 95% CI coverage error was also consistently lower, deviating from the population value by 0.0005 to 0.0257 versus the usual bootstrap range of 0.0031 to 0.0302.  Thus, an improved shortening of the CI length was found across all cases.  The maximum relative bias of the new estimator was 0.7714 versus 0.2703, which reflected a somewhat higher left bias and lower right bias among positive correlation structures, and a greater right bias and lesser left bias among negative correlation structures. CONCLUSIONS: The new approach to estimate the ICER that utilized the harmonic mean allowed for more statistically-efficient point estimation.  The 95% CIs presented with less coverage error and shorter lengths, though typically at the cost of a potential increase in relative bias.