Cancer patients should have access to the best possible care, no matter where they live. In Asia, the estimated number of cancer cases will grow from 9.3 million in 2020 to over 14 million by 2040.1 Almost half of these patients will have no access to appropriate treatment. For those who do have access, there can be considerable variability in the quality of care available.
Access to cancer care is a universal challenge. It impacts patients and providers in every country. However, in low to middle income countries (LMICs), it can be more pronounced. Radiation therapy typically forms part of best practice cancer care for around 50-60 per cent of patients, but less than 10 per cent have access in LMICs.2 This is primarily due to a shortage of equipment and trained oncology professionals.
Even in high-income countries, patients face a significant challenge accessing cancer care. Data from Australia shows that patients in rural areas are up to 35 per cent more likely to die within five years of a cancer diagnosis.3
These challenges require a renewed focus on digital transformation of the entire cancer care continuum, from diagnostics and imaging to interventional radiology and radiotherapy, through to follow-up care.
We can quickly make use of advances in cancer care by accelerating the adoption of new technologies, understanding and creating greater awareness of the cancer needs, and training digitally-enabled oncology professionals.
Here are five ways digital transformations can increase access to advanced cancer care.
Transforming every stage in the cancer care journey
Every cancer patient is different. There is a significant need for sustainable solutions that can be customised and used to address each patients’ unique needs. Today, care providers are asked to navigate an array of specialised resources, tools, and solutions that don’t always work efficiently together.
Cancer care must be redefined through a fully integrated care pathway. One that addresses the entire patient journey—from early detection to diagnosis, therapy, and follow-up care.
Each stage can be made more efficient and integrated, creating an intelligent oncology operation that will give cancer teams the power to be more patient-focused.
Artificial intelligence (AI) and machine learning have been shown to improve the early detection of cancer by accurately identifying at-risk patients. They also make diagnostic tests more precise.
Software systems help create robust, customised treatment plans for virtually every type of external beam radiotherapy. Plans can be done quickly using models designed by academic institutions and remotely by multi-disciplinary teams.
Finally, treatment and follow-up care can be tailored to meet the patient’s ongoing needs as they progress. Cuttingedge digital solutions allow caregivers in clinics of all sizes to customise their approach to meet patient needs, without sacrificing cost-effectiveness or operational efficiency.
Driving a consistent approach between diagnosis and treatment
For cancer patients, time is critical to survivorship. Waiting is not an option when it comes to effective treatment.
However, patients are treated on different timelines, and the quality of their care depends on several factors outside of their control. This results in significant variability in the effectiveness of each patient’s care.
Connecting imaging and treatment through digital services reduces this variability. Greater connectivity helps improve workflows and shortens the time to treatment, thereby improving outcomes for patients.
New technologies also enable faster and more precise diagnoses, allowing healthcare providers to create an informed, personalised plan for treatment. They can leverage highly targeted treatment options that spare as much healthy tissue as possible by identifying the case earlier. This is where image-guided treatment options can lead to improved long-term quality of life.
Radiation therapy is often delivered over several weeks or longer. As patients undergo treatment, they may experience weight loss or other changes to their anatomy, requiring new treatment plans.
AI-driven adaptive therapy can predict these changes and rapidly personalise the patient’s treatment, reducing clinicians’ time planning. The treatment better targets the tumour, reduces dosage to healthy tissue and can be delivered in a shorter time slot. Console imaging capabilities help create a simple experience for clinicians when treating moving tumours.
By seamlessly combining imaging, treatment, and digital solutions, connected technologies can help healthcare providers ensure patients get the treatments they need exactly where they need it.
Making multi-disciplinary care a reality
Often, the patient’s care is determined by the first point of interaction with the health system. If patients first see a surgeon, they will receive surgery first; if they see a radiation oncologist, the chance is higher they will receive radiation therapy.
Cancer care is a multi-disciplinary and increasingly complex system, often requiring multi-modal treatment. Shared research and insights are becoming more critical in highlighting opportunities along the patient journey to improve outcomes.
However, care providers currently operate in silos, making it challenging to translate individual findings into broader solutions. This lack of connectivity leads to breakdowns in treatment planning and implementation.
The accelerated adoption of software can help to move us away from fragmented care. It enables treatment centres, research institutions and practices to work together and provide more integrated, multi-disciplinary care, creating opportunities to share capacity. The patient can also be an active participant in sharing information and making treatment decisions.
We can democratise access to cancer care by bringing oncology teams closer together to build multi-modal treatment plans more effectively. Specialists working together can use the latest clinical evidence to determine the best course of treatment for the patient.
With remote access, professionals who may be geographically disbursed can also input into the treatment roadmap. Oncology teams can also access data on treatment outcomes from anywhere in the world, allowing them to tailor more effective treatments.
Creating a digitally enabled workforce
According to a 2018 study published by the Journal of Global Oncology, 4 out of 8 LMICs in Asia have a ratio of more than 800 new cancer patients for every clinical oncologist in the country. By contrast, each clinical oncologist in the US must care for just 137 cancer patients.4
Globally, an additional 150,000 skilled clinicians will be required to deliver the care needed worldwide. The gap between cancer care supply and demand increases by 14 per cent every year.5
Local communities require more highly trained technical personnel and specialised clinicians, particularly for radiation treatments. Continuous Medical Education Programmes must embed digital health as part of its curriculum to encourage the adoption of new digitally enabled techniques and technologies.
Training programs can be delivered via virtual live learning using technologies that replicate workplace/in-person training, such as Augmented and Virtual Reality. Trainees can come together to discuss, practice, and collaborate in a virtual environment.
This makes training more economical and efficient for prospective trainees. Virtual training reduces travel costs and has a flexible in-program design.
People have opportunities to interact and network with each other, while spending less time travelling.
Such value-based training reinforces the digital transformation of healthcare and empowers the workforce to stay updated with the most advanced technologies in radiation oncology.
Fostering an environment that favours efficiency and innovation
Despite considerable opportunities to help build access to cancer care, existing care models are not structured to help integrate new solutions and encourage a digital transformation in healthcare.
Challenges range from the lack of a regulatory framework tailored to digital health, complex and opaque reimbursement pathways, and a lack of awareness or familiarity with digital solutions.
Policies covering telemedicine and remote monitoring were developed quickly in response to the pandemic. Reimbursement frameworks for AI, machine learning and software as a service (SaaS) require a similar urgency to address the gap in access to cancer care.
We have seen how digital technologies can support clinical decisions. Regulatory frameworks should encourage clinicians to adopt tools that augment their existing processes, allowing them to be more patient-focused and driving better outcomes.
Proving the cost-effective benefits and improvements in clinical outcomes will be critical to building the case for new care models. Suppliers and caregivers can work together to develop robust clinical evidence highlighting where digital technologies are superior to existing standards of care. Building awareness about the benefits to patients, such as digital therapeutics, will also be vital.
Cloud technologies allow patients to report information about their symptoms and quality of life in real-time. Communicating with healthcare providers helps to keep them more engaged in their care. In turn, the clinical care team can use the technology to provide recommendations to remediate distress and proactively manage the patient’s health before their condition becomes more severe.
More effective reimbursement and co-payment policies that incentivisethe adoption of e-health will encourage the digital transformation to a more cost-effective and beneficial healthcare system—one that expands access to care for more patients.
Experts predict that cancer can become a manageable, chronic disease so that a cancer diagnosis will no longer hold the fear that it does today.6 But the gap in cancer care is growing faster than the infrastructure required to address the need.
Advancing digitisation in healthcare will help ensure that care pathways across Asia will be similar regardless of where the patient lives. To do this, we must encourage digital transformation at every stage, building comprehensive support tools and ensuring caregivers are supported by data-driven insights to enable personalisation at every step – for patients, clinics and networks.
AI and machine learning can improve clinical processes and workflows. Clinicians can collaborate to drive clinical excellence, create personalised treatment plans and continually enhance patient outcomes.
Multi-disciplinary cancer boards and remote access can unlock capacity between organisations. Software and insights can be integrated into the cloud so that caregivers anywhere in the world can access the latest information and deliver more accurate treatments.
Data analytics, AI, and machine learning will enormously impact the cost effectiveness of treatment planning – and improve the quality and utility of data generated. Reimbursement policies should reflect this change.
These technologies should be easy to use by radiologists, medical oncologists, surgeons, and radiation oncologists alike. Together, we will enable a better coordinated, better informed, and better-managed journey for each cancer patient.
1 International Agency for Research on Cancer: Estimated number of incident cases from 2018 to 2040, all cancers, both sexes, all ages
2 Expanding global access to radiotherapy - The LancetOncology
3 Remoteness of residence and survival from cancer in New South Wales | The Medical Journal of Australia (mja. com.au)
4 Global Survey of Clinical Oncology Workforce
6 See ‘Survival Data’ sources at https://www.varian.com/why-varian/intelligent-cancer-care