One of the most fundamental questions facing any health care system is whether the resources invested are achieving the maximum result. Another way of phrasing this is whether patients, the ultimate benefactors of health care, are seeing the highest available health outcomes.
This question is at the center of the value-based health care movement, a movement that seeks to measure value in health care outcomes as related to the cost of the initial investment.
In order to do this, health care systems must rely on real-world data analytics which makes use and sense of the mass of available data in the system.
Oncology and cancer immunotherapy based on real-world data analytics
One medical branch where these questions are particularly relevant is oncology, and specifically cancer immunotherapy. This treatment has emerged as an alternative to many conventional ones and has the potential to bring a high level of value to health care systems. Just how much value is still to be determined, as value assessments of immunotherapy are in the early stages.
This means that in order to truly advance this field, health care providers, researchers and payers need to invest in big data analytics in cancer treatment focused on measuring the value of immunotherapy. This will require developing and popularizing the right tools, something that is luckily happening through innovative partnerships.
Sqilline as a value-based healthcare technology solution provider
Sqilline is a leading business in providing tools for value-based health care in partnership with SAP.
Their latest product which can drive the value-based measurements of cancer immunotherapy is DANNY, a tool that allows providers to leverage big data in order to help provide personalized oncology to their patients. By utilizing big data analytics in cancer treatments and accessing data from across borders and systems, providers will be able to more accurately predict patient survival rates, thus more accurately measuring the value of each treatment.
In order to more fully take advantage of advances in cancer immunotherapy, more money and time must be invested in further research. While initial results are promising, these therapies still have the potential to provide even more value to patients. This becomes particularly important as health care systems rightfully focus on the evaluation of immunotherapy treatment.
Payers should choose the best value metric for immunotherapy treatments
Payers will ultimately look at a variety of metrics when determining the ultimate value of immunotherapy treatments, including statistical analysis to evaluate the innovative value-based approaches. New metrics will be applied to completely measure the achieved values and outcomes.
The value of cancer immunotherapy treatments
When evaluating the value of immunotherapy treatments, it is important to utilize the appropriate criteria for assessing how patients respond during treatment and identify the relevant clinical endpoints.
Since 2012, EMA accepts Progression-Free Survival (PFS) and Disease-Free Survival (DFS) as surrogate endpoints in oncology trials, in Europe. In addition, the Response Evaluation Criteria in Solid Tumors (RECIST) has been the common criteria used for solid tumors in evaluating how the tumor is responding during treatment (e.g. complete response, partial response, stable disease, or progressive disease).
The challenge in choosing the right metrics
However, immunotherapy presents a challenge as standard metrics and criteria may not fully capture the range of response patterns and potential outcomes in immunotherapies. For instance, patients may initially respond to immunotherapy with pseudoprogression, which may cause early discontinuation of treatment when in fact such treatment would provide long-term survival benefits.
The often-used endpoints and endpoint surrogates include,
– Overall Survival (OS) – the time from initial treatment to death from any cause. OS is considered the gold standard and the most reliable endpoint in solid tumor oncology clinical trials. For situations with large cohorts with prolonged survival, using OS may be impractical or at least require long follow-up times. Hence, other surrogate metrics and/or endpoints have been used. OS can be measured as OS rate (percentage of patients alive at a specified time) or as median OS (time duration since initial treatment at which 50% of patients are still alive).
Although the median OS allows for survival estimation before complete reporting for all patients with events, it does not capture the benefits of treatment if there are small proportion of patients with long-term benefits. This can especially mask the benefits of immunotherapy, which exhibit this characteristic.
– Progression-Free Survival (PFS) – the time from initial treatment to disease progression or death from any cause. PFS has been used as a surrogate endpoint of overall survival, and in some situations as the primary endpoint. While RECIST is the standard criteria for assessing tumor response in solid tumors, for immunotherapy, new criteria are being developed and used. In addition, PFS has not always been found to be a good surrogate for OS, especially for immunotherapy.
– Time to Progression (TTP) – related to PFS, but excludes deaths as events.
– Disease-Free Survival (DFS) – the time from initial treatment until disease recurrence or death from any cause. DFS is most frequently used in the adjuvant setting.
– Objective Response Rate (ORR) – the proportion of patients with tumor reduction to a predefined amount in a defined time period. The criteria for assessing tumor response is based on for example RECIST, or newer criteria specifically for immunotherapy.
Each separate endpoint provides a different evaluation of the value of treatment. DANNY allows healthcare providers, researchers, and payers to measure and compare a range of endpoints, including OS/PFS/DFS and derived novel metrics (response rate at specific time periods, area under the curve, etc). This enables a more robust view of the overall value of immunotherapy.
Given that the FDA and the European Medicines Agency (EMA) have begun to show flexibility in which endpoint or endpoint surrogates they accept, users of DANNY can take a more flexible approach in their research.
The payers’ benefits
Payers will also see benefits to this approach, as they will more effectively be able to determine which drugs they should sponsor based on the ultimate value of the treatment. The technological advances that DANNY represents will allow payers to track drugs from the moment of prescription to the ultimate outcomes. This will ensure that payers are choosing the highest value drugs based on more than just a limited view of value based on costs alone. Payers ultimately will want to invest not in the cheapest treatments, but in the ones that have the highest value as determined by a wide range of indicators.
The best approach using big data in oncology
In order to truly put the patient at the center of the health care system, it’s necessary to continuously strive to provide higher value interventions. The process of identifying these interventions and treatments will require making the most use of the big data in oncology and other medical branches. A tool such as Sqilline’s DANNY is the best approach to doing this not only due to its usefulness but also because it represents a partnership between companies (Sqilline and SAP through their SAP HANA DB) in service of the same ultimate outcome: more efficient and ppatient-centeredhealth care systems.