Big Data in Health Care: Speaking with Dr. Clifford Hudis
Real World Health Care is pleased to bring you the final interview in our series on Big Data and its impact on health care. Here, we spoke with Dr. Clifford Hudis about how Big Data will impact cancer care. Dr. Hudis is Chief, Breast Medicine Service, Department of Medicine, Memorial Sloan Kettering Cancer Center; Vice President for Government Relations and Chief Advocacy Officer for MSKCC; and Professor of Medicine, Department of Medicine, Weill Cornell Medical College. He also serves on the Board of Governors of the American Society of Clinical Oncology’s CancerLinQ project.
Real World Health Care: In a recent article, you write that big data represents a new opportunity to increase our understanding of cancer care. How is that so?
Clifford Hudis: The ongoing conversion of medical record keeping in oncology from paper-based records to electronic format means that for the first time in history we have potential access to the treatment and outcomes for the vast majority of adults with cancer who are not treated on prospective clinical trials. This means that we can explore treatment effects including both efficacy and toxicity in patients who might not have participated in the usual, tightly controlled, prospective studies that are used to gain regulatory approval. For example, older (or younger) patients, those with co-morbidities, other malignancies, and so on — all of whom are frequently under-represented in prospective drug-development trials — can be studied.
RWHC: What sort of knowledge gaps do you think big data will be able to identify in the area of cancer care?
CH: Key gaps include toxicities and efficacy in special populations, but also use of drugs “off label” based on either classical histopathologic tumor features or newer genomic testing. Another key area is to study drug-drug interactions or drug-genotype interactions.
RWHC: Can you give us an example of how big data has overcome a known limitation of randomized clinical trials in evidence development?
CH: In other disease areas, such as interventional cardiology, large registries have allowed clinical investigators to refine their understanding of the benefits and harms of specific approaches without the use of conventional prospective randomized trials.
RWHC: What are some of the biggest challenges facing the health care industry in terms of its ability to use big data to improve health care delivery, treatment optimization, and cost containment?
CH: They key challenges may be outside the realm of big data per se. We have a societal challenge in the uniform definition of benefit, efficacy and ultimately value. This is especially true in oncology where drug development costs are high, many diseases are life-threatening, and the pace of innovation has to continue to accelerate. It is possible that big data will allow us to gain deeper and faster insights into some of these issues as new treatments first permeate the treatment arena. At a more mundane level, we would benefit from even greater interoperability and standardization of data storage and access.
RWHC: Much of the literature published on the use of big data in health care focuses on cancer care. Why is cancer care such a ripe area for implementing big data initiatives?
CH: Among the reasons are the myriad diseases — and therefore complexity — that comprise cancer, the acuity of the illness, the broad reach, and the large price we pay in overall public health. In the face of this massive set of challenges, only three percent of adults participate in clinical research that defines and advances the standards of care. To accelerate progress, we need to innovate in the area of data development. Big data is one key opportunity in that regard as it simultaneously offers to provide new insights, broaden the distribution of evolving knowledge, and improve the efficiency of the entire drug development enterprise.
RWHC: How has the use of big data impacted you personally in your practice?
CH: We increasingly have access to patterns of care, treatment decision-making, and patient outcomes across a large and geographically distributed group of clinicians and investigators working in one traditional disease are. All of this can be used to improve patient care in an iterative fashion.