In the digital age, data are all around us. How these data are captured, formatted and analysed determine if they are pieces of information or kernels of intelligence that give us the power to take meaningful action. The explosion of data in healthcare is creating the opportunity to gain important insights that can improve patients’ experiences and outcomes and make the delivery of healthcare more efficient and cost effective.
The incidence of cancer continues to rise, and strategic leveraging of technology and big data will be essential to addressing this global healthcare challenge. The incidence of cancer in the United States is expected to increase 35% over the next fourteen years, resulting in 2.3 million new diagnoses by 2030, and the incidence of cancer worldwide is expected to increase 54% by 2030, resulting in 22 million new diagnoses annually. The dynamic and evolving landscape of cancer care provides a model environment in which effective harnessing of big data can drive substantive improvements for patients, clinicians, care centres and payers.
Current Challenges in Cancer Care
Doing more with less is today’s top mandate for the modern cancer centre. Under constant pressure to cut costs and improve quality of care, many oncology centres face difficult decisions around treatment paradigms and resource utilization while lacking a comprehensive picture of the data that drives these decisions.
With increased specialisation of care, patients undergoing treatment for cancer see multiple providers, frequently in multiple facilities. This can complicate treatment and lead to missed appointments and tests, which may require cancelling or delaying subsequent care if test results aren’t available in a timely manner or the patient’s status has changed or declined. Additionally, many clinicians face increased caseloads and may struggle to coordinate care with other providers, all while trying to ensure that their patients receive optimum and timely treatment.
The convergence of genomics, precision medicine techniques and easier access to large electronic health record (EHR) datasets is also driving rapid change in the oncology arena. Clinicians have limited time to consume and analyse all the data that could help to inform treatment decisions for individual patients and, concurrently, the increasing use of multi-modality treatments is leading to an explosion of potential therapeutic options. Today’s oncologists are challenged to consistently follow treatment recommendations that are dictated by the most current clinical practice guidelines while cancer care centres are challenged to ensure standardised care across multiple facilities while improving outcomes and decreasing costs.
Care centres also face a growing need to improve the quality of care they deliver, which requires understanding whether deviations from these guidelines are due to guideline improvements or medical errors. As a result, there is an increasing demand for enhanced decision support capabilities that can transform millions of data points into patient-specific, evidence-based cancer care.
Additionally, basic access to quality oncology care can be a major challenge for patients in many emerging economies. Resource constraints resulting from a shortage of qualified clinicians, the cost of cutting-edge technologies, and the need to travel to reach care centres makes it challenging to provide all patients with optimal care. Increasing patient throughput without compromising the quality of care requires a versatile solution that can improve the workflow, safety and clinical decision making that patients and physicians in these emerging markets deserve.
Big data doesn’t have to give you a big headache
Centralizing data sets from multiple sources and functional groups is essential to creating a data ecosystem that can be analysed comprehensively and inform clinical and operational decision-making. Robust algorithms can transform patient, financial and operational data into actionable intelligence that helps care centres provide the best care most efficiently. However, the value of any big data technology is tied to how it can be customized to meet the needs of individual end users with respect to both data collection and data outputs. The integration of MOSAIQ and Watson for Oncology is an example of how big data can be used to transform care for individual patients. Data sets are getting larger, and it’s up to technology innovators, such as Elekta and our partners, to cut them down to size and put them in the palm of the provider’s hand.
By Yunus Munga, Business Unit Manager for Africa at Elekta