Industry
Healthcare
Patient-centered Care: Health and Wellness Redefined
Highlights
- The leading cause of death, cancer, is more likely to be treated successfully if diagnosed at an early stage.
- Early detection—with the help of effective screening protocols—can potentially reduce mortality. However, some of these procedures are complex, invasive, and stressful.
- Many healthcare providers are exploring a new method for high-risk patients called multi-cancer early detection (MCED). These screening tests detect cancer by estimating a small quantity of the circulating tumor DNA or by determining genetic mutations in this DNA. Please note that currently, none of the MCED tests have been approved by the FDA; some of these are available commercially under the Clinical Laboratory Improvement Act.
- As payers feel the pinch due to high treatment costs, they could be integral to accelerating the adoption of MCED tests.
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Early detection and cancer curability
By adopting liquid biopsy for the early detection of multiple cancers, payers can revolutionize cancer care.
Cancer is the leading cause of death, with an estimated ten million deaths worldwide in 2020, according to WHO. It is the second leading cause of mortality worldwide. If the trend thus far is anything to go by, then unfortunately, the number of new cancer patients and deaths will increase in the future. Early detection of cancer will be key to reducing mortality.
Cancer diagnosed at an early stage, especially if localized, is more likely to be treated successfully. It has already been established that screening can reduce the mortality rate of breast, colon, rectum, cervix, lung, and prostate cancers. Overall, patients’ survival rates are higher when cancer is detected at an early stage. Current screening tests, available for a few cancers, are complex, and invasive. Procedures like colonoscopy, CT, and MRI scans are stressful for children, older adults, and persons with disabilities.
Multi-cancer early detection (MCED) screening tests, also called liquid biopsy tests, are gaining momentum for early cancer detection. Multiple types of tests are available commercially, and they are based on different methodologies. Galleri and PanSeer are based on circulating DNA, and CancerSEEK is based on gene mutation and plasma proteins. GRAIL’s Galleri test can detect 50 cancers and the origin of cancer with different sensitivity at different stages of cancer by analyzing DNA methylation patterns. The study showed an overall sensitivity of 51.5%, a specificity of 99.5%, and the accuracy of cancer signal origin prediction was 88.7%.
Early cancer detection will be a focus area for all the players in the healthcare ecosystem in the coming years, including providers, payers, regulators, and policymakers. Healthcare providers in the USA have started evaluating this test for cancer screening. Many large life insurers have started evaluating this test as part of their underwriting process. Insurers may recommend these tests as part of pre-policy medical checkups. This may accelerate the adoption process. Additional primary care clinics will offer the test in the near future.
A payer’s perspective
The cost of treatment is expected to increase, primarily due to factors such as inflation, labor costs, and new types of treatments.
Cancer treatment cost is the second largest in the USA and will be around $209 billion in 2020. Payers are already under financial stress due to higher operational expenses and lower profits and will pay close attention to any new technologies or methodologies that will help reduce costs. They are expected to play a prominent role in driving adoption of this new method.
Payers may consider adopting this as part of preventive care and companion diagnostics as more evidence becomes available on the benefits of this test. Some non-profit payers with associated health maintenance organization (HMO) services could adopt early preventive care for selective populations. Initially, bundled health plans can be rolled out for selective high-risk populations, enabling this test as preventive screening and/or companion diagnostic.
An approach to implementing the new test
Standardization of processes and protocols across the value chain is vital for better outcomes and to avoid systemic bias.
Standardization should address processes including test recommendation, interpretation of test results, and follow-up diagnosis and treatment protocols. One of the challenges will be the clinical interpretation of the results. This interpretation may depend upon various factors including race, ethnicity, age, and comorbidities. Sometimes, clinical interpretation may not be enough for a conclusive diagnosis.
Expert panels from multiple domains may be required to guide complex cases. A comprehensive guideline based on evidence will be necessary, but the usage may be limited due to the complex nature of genetic analysis. A decision support system for augmenting the processes for the entire value chain could help in process standardization. This will help better utilize oncologists and other critical staff by reducing their time in administrative and non-productive work. A decision support system based on evidence could augment the process by minimizing human errors, ensuring consistency in decision-making, and optimizing resource utilization.
The cost factor
Although high, this cost may not be substantial when compared to the overall expenses for cancer treatment.
The cost of diagnosis
Based on the cost trend of molecular diagnostic kits like WES, WGS, and panel-based genetic testing in recent years, the cost may come down in the future, but in the initial years, the test could cost more. Without insurance coverage, a large part of the population will not be able to opt for this test.
Post the test, the diagnosis or scanning cost will increase in the initial stages for patients with cancer-positive results. Further, scanning or other tests will be required to confirm the cancer clinically. This cost would have been higher (assuming inflation) had the patient been diagnosed at a later stage. The average diagnosis cost may dip if it is done early.
The treatment cost
Some of these most prevalent cancers contribute 40% to 50% of total cancer expenses, approximately $120-$130 billion annually, based on estimated cancer burden calculation by the National Cancer Institute in the US. Cancer survivors have higher out-of-pocket costs, even many years after initial diagnosis, and face serious financial debt.
The mean cost of treatment increased two to three times at stage four compared to that at stage one, depending upon the type of cancer. By facilitating early detection (at stage one or two), the MCED test promises to substantially lower the cost of treatment depending upon the cancer type. A conservative average approximation of 20% to 30% of cost savings due to early detection may lead to 10% to 15% of cost savings of total expenses, which will benefit both patients and payers.
Analyzing genetic data
Many healthcare providers already have specimen management facilities.
Genomic testing
Key steps for the MCED test include blood sample collection, epigenetic profiling of the sample using NGS technology, genomic analysis using bioinformatics pipelines, AI-ML algorithms for predicting clinical outcomes (cancer +/-, origin of cancer), and clinical reporting.
In the initial stages, epigenetic profiling, genomic analysis, and clinical reporting can be outsourced to a lab that has a wet lab and genomic analysis capability.
Mining the genetic data
One of the most important tasks for healthcare providers will be the mining of the genetic data obtained through this test. The patient genetic data analysis will be critical in continuously developing or refining diagnosis and treatment guidelines. The results of this test will have many unknowns in the beginning, and genetic data analysis will be critical for addressing these unknowns. Also, the analysis of genetic data could provide critical biomarkers about patients having different comorbidities and could lead to different treatment protocols. A clinical research platform could accelerate the genomic analysis process and automate the integration of new discoveries, updates, or biomarkers into the process.
Integration with electronic medical records (EMR)
Integrating this test into a healthcare platform may require substantial change to the IT infrastructure.
The requests for tests, results, and interpretation from in-house or third-party labs must be integrated into the core IT platform. Seamless integration across internal and external systems may be required to handle different processes. A robust and automated workflow solution with minimal manual intervention may be needed to eliminate processing errors. The workflow solution should provide a scalable architecture for handling future requirements.
Genetic tests for large populations will generate lots of data, including raw and processed data, and report amounts in petabytes, which will require substantial overhead costs for storage. This overhead will increase as the population covered in this test grows. The cost of management of these data and reproducibility requirements will need a dedicated computing environment including data, pipelines for analyzing the raw data, reference data, and configuration data.
