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Using large patient databases for healthcare decision-making in cancer could improve quality of life and decrease healthcare costs, according to a paper published online this week in . The analysis of patients with a specific type of leukaemia is a first step in exploring the potential benefits of precision medicine.

The goal of precision medicine is to tailor medical treatment to a patient’s unique combination of genetic profile and other characteristics. This requires large databases, or knowledge banks, of patient information.

Hartmut Döhner, Peter Campbell and colleagues analysed a knowledge bank of 1540 patients with (AML) to develop a predictive algorithm for patient survival. They used the algorithm to predict whether an individual patient would benefit from blood stem cell transplantation during first complete remission. This treatment significantly increases survival, but comes at a high risk — up to 25% — of treatment-related death. The alternative, intensive chemotherapy, has a much lower risk of death, but a high risk of other serious complications. They found that, using the knowledge bank, they could potentially increase survival by 1.3% and decrease the use of transplants in patients with AML by 20–25%, while maintaining the same overall rate of survival.

The authors suggest that use of a knowledge bank for AML treatment would improve patient health by avoiding potentially life-threatening complications from transplantation and result in substantial healthcare cost savings, considering the cost of blood stem cell transplants is US$100,000 to US$200,000. However, the authors note that knowledge banks are expensive and difficult to maintain. It is still unclear whether the benefits of knowledge banks will outweigh their costs.