We have entered the era of the internet of things as pervasive devices including smartphones, computers, cars, fitness monitors, and monitoring devices in hospitals and factories are all generating massive amounts of electronic, machine-produced data every second.

In fact, more data have been created since 2003 than in all of previous recorded history.

Researchers, data analysts, and scientists are having to shift from examining typical datasets in traditional fashion to petabytes of data, looking for deeper and complex relationships in the data that can help us gain insights and improve outcomes in many areas including healthcare and diseases such as cancer. In healthcare, the volume of published cancer research has increased dramatically in tandem with an explosion in patient-specific information such as genomic data.

However, while much of these data—such as individual characteristics, treatment regimens, and outcomes&mdash;are leveraged for decision making for the individual patient during treatment, our ability to leverage the data of previously treated patients in aggregate to continuously learn from and improve real-world results is severely limited outside of the small minority enrolled in clinical trials.

What if we could change that? Technology solutions powered by rich datasets and high-performance, in-memory computing capabilities have the potential to facilitate cancer care transformation. By building platforms and networks that allow oncologists and researchers to harness and utilize the vast amount of health data globally, physicians can make treatment choices based on real-world outcomes from a broad group of cancer patients, closely matched to the patient at hand.

Delivering Evidence-Based Care With CancerLinQ

An integrated platform allows those in the cancer care ecosystem to leverage the collective experience and knowledge of entire organizations, systems, regions, and countries, which can supercharge a physician’s decision-making power. This new approach—with a focus on personalized, data-driven decisions&mdash;is remaking healthcare, and more specifically, changing how oncologists treat cancer.

In-memory computing is revolutionizing how we diagnose, treat, and prevent cancer. One groundbreaking platform is CancerLinQ (

An initiative launched in 2012 by the American Society of Clinical Oncology (ASCO), CancerLinQ is making unprecedented use of massive amounts of patient data, already analyzing nearly hundreds of thousands of medical records of patients with cancer to uncover patterns and trends and to measure their care against that of their peers and recommended guidelines.

It’s surprising to many patients with cancer and their families, but the data oncologists typically leverage for deciding on chemotherapeutic regimens are based on a tiny subset—only 3%&mdash;of the more than 1.7 million people in the United States diagnosed each year with cancer.

 This might be acceptable if the enrolled patients represented the general population of patients with cancer. However, real-world patients with cancer tend to be older, sicker, and more ethnically diverse than the typical study patients who tend to be in good health—except for the cancer.

Oftentimes, the available clinical trials would have excluded patients like the one the oncologist is treating, prompting clinicians to extrapolate based on their experience treating similar patients blended with gut feeling and intuition. CancerLinQ allows oncologists to learn from the 97% of patients with cancer not involved in clinical trials and help deliver better, more data-driven decision making based on real-world results of patients closely matched to the patient at hand.

As of June 2016, CancerLinQ has 58 vanguard practices using the platform with the records of 750,000 patients with cancer stored in the system. The growth rate for collecting data in Cancer- LinQ is exceeding expectations.

And more importantly, growth is currently limited by the ability to onboard practices as the demand among practices greatly exceeds the ability to enroll them. Participants range from small private practices to some of the nation’s leading cancer centers. Approximately 200 additional practices are in line today to participate in CancerLinQ.

Ingesting the data into the CancerLinQ platform is no easy task. It integrates structured and unstructured information coming from disparate electronic health record (EHR) systems, with highly variable data quality from system to system and field to field. The persistent challenge for the platform is to cleanse, normalize, and gauge the accuracy of the deluge of data used in cancer care. With the momentum of CancerLinQ, the platform has reached statistical significance for major disease types, such as breast and colon cancers.

Oncologists using CancerLinQ now have growing amounts of usable, searchable, real-world cancer information to help them provide better care, quality assessments, and care management. Monitoring, Reporting, and Data Visualization CancerLinQ runs on the SAP Connected Health Platform built on SAP HANA, an in-memory computing platform providing both speed of execution along with simplicity to rapidly create value from large, disparate, disconnected datasets.

Continual clinical quality measure performance tracking

Patient cohort identification based on shared characteristics

Integrated patient timeline visualization from disparate clinical systems

Providers using CancerLinQ have access to metrics and tools that support high-quality, efficient care. One feature of the CancerLinQ system is its capability to provide feedback for physicians’ performance based on quality measures from ASCO’s Quality Oncology Practice Initiative (QOPI), an oncologist-led, practice-based tool for care assessment and quality improvement.

QOPI provides a standard methodology, robust library of quality metrics for oncology, and a collection tool to reliably and routinely assess care, inform quality improvement activities, and demonstrate quality to patients and external stakeholders. Early adopters of QOPI are well positioned to meet external reporting requirements for payers and the government, and to participate in new payment models focused on quality.

CancerLinQ is one of many initiatives that will use in-memory computing to improve care. The potential for precision medicine to improve outcomes while reducing cost is too great for healthcare groups to ignore. The government and corporate entities funding healthcare and the awakening giant of patient consumerism are escalating demands to accelerate this transformation.

Last year, the White House committed the nation to a $215 million investment in precision medicine, and in his 2016 presidential address, President Obama announced the Cancer Moonshot, a $1 billion government initiative to cure cancer. The investments and media attention are further driving public awareness and support, along with a cultural shift around data sharing.

With CancerLinQ, we are augmenting evidence-based medicine with insights gleaned from massive datasets about the real-world results of treating real-world patients. That leads to powerful, informed decision making that is based on treatment outcomes of real-world patients. CancerLinQ helps practitioners learn from real-world cancer treatment data, as well document and improve their quality of care.

Initiatives such as CancerLinQ demonstrate how we can use in-memory technology to revolutionize oncology. Providing personalized care for patients with cancer will improve outcomes and can positively impact care value as well. Oncologists will gain insights and be able to hone treatments by making sense of the enormous amounts of available cancer data.

Of course, hurdles remain, including the ongoing need for innovation, investment, quality assessment, and data sharing. But CancerLinQ is already proving how a collaborative community of partners can harness data to drive more informed decisions that will ultimately improve the care of people living with cancer.

1. The Human Face of Big Data [video trailer]. Public Broadcasting Service. February 24, 2016. http://www.pbs.org/video/2365668518/.

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This new approach&mdash;with a focus on personalized, data-driven decisions&mdash;is remaking healthcare, and more specifically, changing how oncologists treat cancer.

Kevin Fitzpatrick

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