The Paradigm of Risk rationalization in healthcare and insurance – Expanding role of Data Science
The concept of risk sharing is fundamental to all insurance products and contracts. At the simplest level, it is about sharing of risk between an insurance company and a policy holder for life insurance, car insurance etc. The concept is also fundamental to all stakeholders who are involved, for e.g., risk sharing between an insurance company and a TPA, and risk sharing between an insurance company & a reinsurer. Traditionally such sharing and rationalization of risks between interested parties has been based on data, probabilities, statistical estimates and such. Limited data were used and not frequently updated. But the scenario has changed, with technological advances, digitization and the focus on facilitating ‘informed’ decision-making for all. Access to Big Data, increased sophistication of statistical and data science methods, and availability of data analytics skillset can be leveraged to define risk sharing mechanisms and models, and to refine and implement them based on real-time availability of updated, comprehensive data. There is growing recognition of the power of using real-world experience (data) to define risk sharing strategy and implement it.
Another area of risk sharing that has gained prominence is healthcare, with the emerging importance of rationalization of risks across all stakeholders in the value chain – the patient, the provider (doctors/hospitals), the employer medical insurance company and of late, also the pharmaceutical company; with HTAs and regulators being strong influencers of how risk sharing is modelled. Several sources of real-world data can be accessed and deployed to optimize risks across all entities in the ecosystem. In addition to claims data from the health insurers, it’s possible to access other data sources such as EHRs, digital health devices and wearables, pharmacy dispensing records, observational studies and registries that are conducted to evaluate safety and effectiveness of the new intervention, genome testing, and more. Then big data analytics and statistical modelling, powered by a robust technology platform, enables: (1) risk-benefit analyses by the HTAs and regulators for approval and reimbursement decisions, (2) determination of outcomes-based reimbursement, risk-sharing models, including stop-loss and reinsurance contracts by payors (insurance cos), reinsurers and pharmaceutical companies, and (3) development of reimbursable rates of interventions, co-pay/deductible plans and insurance contracts for payors, providers, employers and individuals.
Steep price tag of new therapies, especially biologics for oncology and rare diseases, has led to pharmaceutical and insurance companies considering risk-sharing agreements, wherein reimbursement by the insurer is dependent on realization of predefined outcomes for a patient. This concept, when extended beyond reimbursement for a drug or a therapy, to reimbursement for comprehensive clinical care, leads to Value Based Care (VBC) which is now gaining traction with payors and providers. This involves value-based arrangements and hence differential pricing of care. The value of an intervention or a care plan also needs to be assessed on an ongoing basis by analysing all types of experience data that is available.
What does it take to deliver?
Integration of Real-World Data from different sources requires a robust data integration engine, implementation of data standards, and preferably, in-built statistical analysis and modelling functionality. While technology is the foundation, statistical and data science expertise is critical, and a deep understanding of the drug development lifecycle and of actuarial principles is essential for structuring risk-sharing plans.
Well-defined and validated risk metrics, and the ability to dynamically monitor it will facilitate real-time signals that can trigger the required changes to the risk rationalization framework.
The ultimate goal is strategic solutions for insurance and health care delivery to improve access and quality, reduce cost and create a more positive experience for every individual.
President and Founder
30+ years in industry and academia; 24 years in Pharma & CRO in Clinical research, post-marketing and safety; cancer epidemiology; Entrepreneurial experience; Statistics and Actuarial qualification.