Precision radiation oncology: predictive analytics for personalized treatment


Doctors who use data make better decisions.

Designing a radiation treatment plan requires doctors to strike a difficult balance. Delivering too much radiation will cause severe side effects. Delivering too little radiation will prevent the patient’s cancer from being contained or cured. With data about the success and failure of past treatments, radiation oncologists can optimally balance the goal of cure against the need to minimize radiation-related toxicities.


Radiation oncologists generate data, but they can't use it.

Each year roughly 1.3 million patients in the US undergo treatment with radiation. These patients generate diverse and valuable data, and that data has the potential to improve quality of care for future patients. Unfortunately, radiation oncology data is trapped in disparate hospital software systems, making it very difficult to collect and analyze. Better software tools that extract and structure this data are needed to enable predictive analytics and precision radiation oncology.


Patients deserve personalized care.

Most health systems have the data they need to create personalized treatment plans for their patients but they lack the software tools required to structure, analyze, and learn from that data. When unified data is available in the right form at the right time, physicians can bring quantitative rigor to risk-benefit analyses, catch and address subtleties that may have otherwise been overlooked, and can proactively take steps to minimize risks to their patients.

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