As genomic medicine becomes part of standard clinical care, Precision Medicine faces a daunting computational challenge in scaling up to support the genomic, image processing and analytics workloads required for millions of patients, especially in oncology clinics. Computational solutions based on heterogeneous hardware platforms like FPGAs have the potential to enable rollout of personalized care for large numbers of patients. We review several clinical use cases to shed light on how FPGA-based solutions can lead to large performance gains and tackle the computational bottlenecks in precision medicine. The biggest barrier to FPGA adoption is their accessibility and the steep learning curve for many bioinformatics and precision medicine codebase development groups. We describe new standard libraries and development environments that will facilitate FPGA-based development and show how they enable performance improvements with modest effort investment.