Prognostic index is valuable to clinicians advising patients, as well as policy makers and epidemiologists' interest in risk adjustment. However, for prognostic index developed for outpatients, a fraction of risk measurements often relied on subjective descriptions of health status along with administrative data with variable resources, which limits their usage for larger populations. The increasingly adopted electronic health record (EHR) systems provides a feasible and valuable approach for automated implementation of a mortality risk prediction tool. When researchers carried the existing risk indexes to EHR, however, problems often arose including coarse or partial risk factors are available in EHR. Integrating the original mortality index with the EHR data, we proposed to estimate the optimal equivalent value for the partially collected risk factors by minimizing various loss functions. Simulation studies showed that the proposed methods outperformed the other commonly used approaches in practice. The proposed approaches were applied to the EHR data to develop an electronically-adapted mortality risk score index.