  Background: Clinical decision support systems (CDSS) are a category of health
information technologies that can assist clinicians to choose optimal
treatments. These support systems are based on clinical trials and expert
knowledge; however, the amount of data available to these systems is limited.
For this reason, CDSSs could be significantly improved by using the knowledge
obtained by treating patients. This knowledge is mainly contained in patient
records, whose usage is restricted due to privacy and confidentiality
constraints.
  Methods: A treatment effectiveness measure, containing valuable information
for treatment prescription, was defined and a method to extract this measure
from patient records was developed. This method uses an advanced cryptographic
technology, known as secure Multiparty Computation (henceforth referred to as
MPC), to preserve the privacy of the patient records and the confidentiality of
the clinicians' decisions.
  Results: Our solution enables to compute the effectiveness measure of a
treatment based on patient records, while preserving privacy. Moreover,
clinicians are not burdened with the computational and communication costs
introduced by the privacy-preserving techniques that are used. Our system is
able to compute the effectiveness of 100 treatments for a specific patient in
less than 24 minutes, querying a database containing 20,000 patient records.
  Conclusion: This paper presents a novel and efficient clinical decision
support system, that harnesses the potential and insights acquired from
treatment data, while preserving the privacy of patient records and the
confidentiality of clinician decisions.
