Inphile Health: Awakening Dormant Insurance-Medical Big Data to Empower Intelligent Pharma-Insurance-Payer Integration

The commercial health insurance market is experiencing rapid growth, yet insurers are simultaneously facing heightened pressure from risk control and competition. There is an urgent need for insurers to achieve proactive risk and cost management, as well as personalized health services, through health management and managed care. How to build a closed loop of “medical healthcare + insurance payment,” and how “health insurance + medical care” can drive the integration of pharmaceuticals, healthcare, and insurance, has become a focal point of industry attention.

In mid-April, at the “5th Future Healthcare 100” conference hosted by VCBeat, Dr. Jiao Long, COO of Knowledge Vision, was invited to attend the “Medical Insurance Technology and Commercial Health Insurance Forum” and delivered a speech themed “Data Assetization of Health Insurance and Big Data-Driven Integration of Medicine and Insurance,” providing a detailed introduction to Knowledge Vision’s innovations and explorations in insurance and medical big data.

Amid an era characterized by accelerating population aging, advancements in diagnostic and treatment technologies, and consumption upgrading, commercial health insurance has entered a phase of rapid growth, with its market size projected to reach RMB 2 trillion by 2025. However, health insurance products are suffering from severe homogenization, leading to increasingly fierce competition. The maximum coverage amounts, insurance liabilities, and the breadth of covered conditions have nearly reached their limits, while there is minimal room for further premium reductions.. In the future, innovative health insurance products and accompanying health management services tailored for individuals with pre-existing conditions and those in a sub-health state, leveraging medical big data, will become new growth engines for the health insurance market.

Dr. Jiao Long pointed out that health management services possess significant commercial and social value. For insurance companies, health management can enhance the differentiated competitiveness of their products, improve customer satisfaction, and effectively boost premium pricing power. Through health management, insurers can achieve proactive risk management, strengthen risk identification capabilities, reduce loss ratios, and further drive operational value addition. Moreover, health management improves the efficiency of healthcare resource utilization, thereby raising the overall health level of society and enhancing social value.

Health management mainly consists of two parts: 1) the management of healthy lifestyles for healthy individuals, and 2) the management of diseases for patient populations.Establishing a Dual Closed-Loop System of Healthy Living and Disease Management, there are three key points: first, to conductCustomer Segmentation, only through population segmentation can broader coverage be achieved for individuals in a sub-health state, those with chronic diseases, and those with critical illnesses; secondly,Data Analysiscapabilities, which are the core of the entire health management system. In particular, the analysis and utilization of effective clinical data form the foundation for delivering personalized health management services; third is in-depthProfessional CollaborationWith the trend of basic medical insurance covering essential care and commercial insurance covering critical illnesses becoming established, commercial health insurance companies need to engage in deep collaboration with pharmaceutical and medical device enterprises in key areas, particularly in oncology, to jointly build outcome-oriented health management. Data serves as the foundation and bridge of the entire health management system; however, hindered data flow between medical institutions has created isolated data silos. Out-of-hospital data are generally heterogeneous and unstructured, making them difficult to extract, process, and analyze.

Insurance companies have accumulated extensive medical data during underwriting, claims processing, and health management, encompassing more than 40 types of common medical documents such as prescriptions, itemized bills, diagnostic reports, laboratory test results, pathology reports, and discharge summaries. However, these data present three major challenges: First, the vast volume of unstructured data makes it difficult to extract and leverage valuable medical information in a cost-effective manner. Second, poor standardization arises because the data originate from diverse medical institutions across China, leading to inconsistencies in templates and terminology that require standardization before use. Third, the high degree of professional specialization necessitates medical expertise for data processing, resulting in significant technical difficulty and high costs.

Traditional methods for processing medical data include manual entry and general-purpose OCR. Manual processing is characterized by high costs, lengthy data entry times, low accuracy, and poor reliability. General-purpose OCR, on the other hand, only addresses text digitization and fails to achieve data structuring. Furthermore, since the medical data obtained by insurance companies is often imperfect, frequently containing issues such as misaligned lines and overlapping characters, general-purpose OCR is unable to resolve these problems.

Knowledge Vision targets industry pain points and has independently developedFour Intelligent Systems Facilitate Medical Data Processing.

Inphile Intelligent Medical Data Text Processing System (Digitization of Medical Document Images): Based on real-world clinical scenarios, its proprietaryOCR AlgorithmIt enables the extraction of accounting, diagnostic, and medical information, and features an exclusive single-character error alert function, with an error alert rateLess than 10%, which can significantly reduce the workload of manual verification and effectively resolve issues such as misaligned rows and overlapping characters, thereby shortening the process through human-machine collaboration70%entry time, with a comprehensive accuracy rate of up to99%。Inphile Intelligent Medical Data Structuring System (Structured Extraction of Medical Knowledge): Supports structured output of common claim application materials, including ID cards, bank cards, itemized bills, medical records, imaging reports, laboratory test reports, and pathology reports. Such structured medical data can enrich health big data, providing robust support for intelligent risk control, cost management, and health management.Inphile Intelligent Medical Data Standardization Processing System (Data Cleaning, Normalization): Capable ofStandardization of standard terms, alternate names, and aliases for medical institutions, disease diagnoses, pharmaceuticals, medical services, clinical procedures, and consumables, with an automatic coding rate as high as98%; Establishment of Patient Electronic Health Records (Knowledge-Based): By leveraging data such as basic user information, medication history, laboratory and diagnostic test reports, and insurance claim records, personalized tags for user profiling are generated. Standardized and integrated health record information is displayed in a timeline format, and the health records are updated and enriched in real time based on subsequent user behaviors.

Multi-source heterogeneous medical data, processed through Inphile for textualization, structuring, normalization, and knowledge enhancement,This has resulted in the creation of operationalizable big data for insurance and healthcare, thereby achieving the assetization of health insurance data.Standardization of medical data is also the foundation for medical data integration.

Insurance Medical Big Data as an Asset Can Achieve Operational Value-Added Through Effective Operations.

There is significant information asymmetry between healthcare service demanders and providers, leading to widespread misallocation of medical resources. By leveraging big data from insurance and healthcare, it is possible to achieve precise matching of pharmaceuticals, medical services, and insurance products based on accurate user profiling. For instance, medical information such as physical examination reports, laboratory test results, and diagnostic reports provided by customers during the processes of insurance underwriting, claims settlement, and health management are digitized through the Inphile intelligent platform. Based on this medical information, health risk assessments can be conducted, followed by recommendations for relevant tests and health management advice. Meanwhile, insurance product recommendations tailored to customer needs can be made based on patient tagging.Promote innovation in insurance marketing to enable more precise marketing and targeted delivery of insurance products.

For insured individuals who have already been diagnosed with diseases, such as cancer patients, it is possible to identify specific subgroups of tumor patients based on their pathology reports, examination results, and treatment history, thereby providing patientsComparative Analysis Report on the Treatment Outcomes of Patients with Similar Conditions, using clinical outcome data from different treatment regimens as a reference; this innovation alsoFacilitating the Transition from Experience-Driven Healthcare to Data-Driven Healthcare。

As a form of real-world data, insurance-based healthcare big data can empower the entire lifecycle of drug development and holds significant value for new drug research and development. It provides data support for key stages such as the development of new indications, expedited market approval pathways, optimized clinical trial design, and subject recruitment. Furthermore, it enables digital marketing during the post-launch phase and allows pharmaceutical companies to deliver continuous follow-up services.

Under the digital operational management model, insurance companies provide full-lifecycle health management services to policyholders through health services, disease management, treatment management, and rehabilitation management, thereby achieving process management of medical services. Through health management, insurers are no longer merely payers for medical services but also coordinators of medical resources. Driven by big data from insurance and healthcare, insurers can deliver personalized health management tailored to each policyholder, optimize the allocation of medical services, and build differentiated health management capabilities.

Knowledge Vision is dedicated to the integration of medical big data centered on health insurance, leveraging next-generation artificial intelligence technologies to build a big data-driven innovative service platform that facilitates collaboration among healthcare providers, pharmaceutical companies, and insurers. It offers users diagnosis and treatment decision-making recommendations based on medical big data. By analyzing insurance-related medical big data,Textualization, Structuring, Normalization, and Knowledge Integration, helping insurance companies to capitalize on health insurance data and empowering them to build a Data-as-a-Service (DaaS) model.（DaaS）capabilities, thereby enabling the operational management of data assets and personalized health management services, and through this processIntegration of the Pharmaceutical Supply Chain, empowering insurance companiesAchieve the tripartite linkage among medical services, pharmaceuticals, and health insurance.

In the future, the application of big data in insurance and healthcare will gradually unlock the vast market potential of insurance operations. Knowledge Vision will continue to deepen its expertise in this field, leveraging its three core business pillars—“products, services, and operations”—to build a trustworthy health management service platform.