|ACCESS Health International hosted the eighth session of the virtual Special Interest Group (SIG) sessions for India as part of the Fintech for Health (F4H) project funded by MetLife Foundation on July 19, 2022. The Fintech for Health team in India continues to engage fintech and healthtech players to share knowledge, dialogue with each other, and catalyze the creation of new partnerships and models to support health financing for low- and moderate-income populations. ACCESS Health International created the Fintech for Health platform for innovative solutions that enable low- to moderate-income people in Asia to pay for and access high-quality care using digital financial services and a financial inclusion approach.
This session focused on claims management for health insurance using innovative technologies and was attended by thirty-seven representatives from leading healthtech and fintech start-ups, payment banks, non-profit organizations, insurance companies, government officials, healthcare experts, and pharmaceutical companies. The session was aimed at understanding time and effort efficient claims management and processing platforms in the industry that could potentially result in reduction of insurance premiums and costs. The stakeholders identified that insurance companies today face three major challenges – outreach to prospective customers at the right time; provide the right set of products that suit the requirements; and fast claim support to customers and rejection of fraudulent claims.
Innovative technologies such as artificial intelligence (AI), machine learning (ML), and robotics have been instrumental in conceptualizing solutions to improve the claims management processes and subsequently improve the demand for insurance products through faster and secure processes. Faster and more secure claims processing and settlement improve the health insurance coverage by attracting both the providers and the patients to adopt and promote health insurance, especially to facilitate cashless visits to the hospitals. While some insurance companies thrive using AI to enhance their business operations, the AI implementation in the industry looks poor. According to the Gartner CIO Report (2018 -2020), only 19% of insurance companies deployed AI-enabled applications to production. Insurance companies also need to ensure data privacy, deter security challenges, and solve ethical problems with use of AI for health insurance. To further elaborate on this pertinent discussion, the SIG welcomed presentations from two innovative startups in the field – Artivatic from India and Aceso Health, from New Zealand. Both of these startups have developed innovative models that have helped the insurance ecosystem through improved claims management by using starkly different approaches using technologies.
Aceso Health – A transformative and transparent claims management system and insurance information system for patients and providers
Aceso Health shared pertinent details on New Zealand’s capitation based health financing system that results in funding of providers after the treatment has satisfied certain criteria – individual and system level. Aceso Health shared various elements of this complex health system that they help streamline by automating and integrating the data across systems. Their product Pinga is coded with the rules or guidelines that inform and help the providers claim the cost for treatment in the capitation model. It also helps them understand in real time the claims and reimbursements that the patient is eligible for through their respective insurance policy. Aceso Health provides the system in multiple ways such as an enterprise version where the platform is leased by a company or a transaction version where a part of every transaction is claimed by Aceso Health.
The discussion involved questions and insights on the challenges and implementation of an outcome based system through use of community engagement, emphasis on preventive health, increasing self- and home-based care, and fastening the long wait time for specialist care through free government services. The challenges of the capitation model such as low payments for care providers were discussed and further exchange of learnings and models such as contracted private doctors in Turkey and co-payments was undertaken by the stakeholders. The discussion was pivot towards achieving improved health outcomes and health seeking behavior through innovative platforms such as these.
Artivatic Health Labs – AI-based product to improve the claims processing and communication between the stakeholders
The discussion focused on the elaborate application of the Alfred platform across the ecosystem in India especially through integration with the ABDM. This integration allows the platform to process the claims through generation and use of the Ayushman Bharat Health Account (ABHA) ID (Health ID) provided by the National Health Authority (NHA). The comprehensive structure of Alfred allows the patients to also utilize this platform for multiple health services such as delivery of medicines, booking doctor consultations online, and patient analysis for the providers. Alfred also provided various data-driven healthtech features of Alfred that creates the required analytics to assist the claims processing and patient experience through various features such as provider pool among multiple others. The discussion was concluded with stakeholders engaging with Artivatic on the feasibility of implementation of the platform for various applications including fintech avenues such as digital lending for health services.
According to a report, Insurance companies lose over US$6.25 billion to frauds which results in higher premiums for genuine consumers . Driven by Artificial Intelligence, the touchless insurance claim process can report the claim, capture damage, update the system, communicate with the customer, and reject fraudulent claims all by itself. Traditionally, insurers manually check the relevance of claims to determine whether they are fraudulent or not. According to McKinsey, for every 10 health insurance claims submitted, insurers classify up to 7 as unusual, meaning potentially false or fraudulent, based on company policy. Re-investigating 70% of claims is an unrealistic suspicion rate that shows that people are not effective fraud detectors. By combining monitored and unsupervised ML models and using behavioral analytics, insurance companies can reduce the cost of fraud. These technologies reduce the time taken to process claims, efficiently detect fraudulent claims using advanced analytics and eliminate human error and lag time. Conclusively, the large-scale innovation being undertaken in the claims processing and management area would need attention from the regulators and users. Making these mainstream could potentially reduce the costs involved in the insurance industry and make it affordable for the lower and lower middle-income population in the years to come.
Some of the questions which still need attention to understand and implement innovative claims processing models are as follows:
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