Challenges associated with the adoption of value-based models The healthcare continuum can be viewed as an aggregation of three distinct domains: • The knowledge domain, including academic centers, scientific institutions and companies that discover and commercialize medical and scientific knowledge; • The care delivery domain, including hospitals, community practices, physicians and other constituents that deliver healthcare to patients; and • The payer domain, including insurers, governments and self-insured employers that administer and provide funding to the healthcare system. The disparate and fragmented nature of these domains and economic incentives under traditional fee-for-service models frequently result in overtreatment, high costs and suboptimal patient outcomes. Fee-for-service models are as a general matter inherently site-centric, volume driven, reactive in nature and uncoordinated. In contrast, value-based models are generally more patient-centric, outcomes-focused, proactive and coordinated across the care continuum. Despite a clear need, the design and implementation of next-generation interoperable systems has been limited due to reliance on legacy, site-specific, fee-for-service technology systems and infrastructure. Since the passage of the Health Information Technology for Economic and Clinical Health (“HITECH”) Act in 2009, providers and payers have made significant investments in EHRs, and other technologies meant to enable the transition to value-based care. Despite extensive investment and coordination, the introduction of value-based models has been limited due to the shortcomings of legacy, proprietary systems and the reliance on unstructured data that hinders interoperability and cannot be sufficiently shared or manipulated to produce actionable findings. Value-based models require collection and analysis of longitudinal treatment, outcomes and financial data at the patient level, regardless of treatment site. Critically, these systems must also securely safeguard patient data in compliance with stringent Health Insurance Portability and Accountability Act of 1996 (“HIPAA”) and other privacy regulations. We believe that there is a significant need for interoperability platforms that dynamically access, normalize, integrate and update information from disparate sources across the healthcare continuum in real time. Secure interoperability platforms can allow for more comprehensive solutions development that proactively connect, deliver business and clinical intelligence and enable enhanced provider and patient engagement. Managing massive volumes of data requires specialized infrastructure, and making data meaningful and actionable requires it to be processed by advanced systems operating in scalable and reliable environments. The network monitoring market is moving away from corporate managed, on-premise solutions towards hybrid and cloud solutions with a focus on platform services expansion and AI/ML-based analytics to provide intelligent alarm/event management. Key trends include: • Expansion of the network edge to include internet of things components and the migration of the network core to the cloud requires that network performance monitoring and diagnostics tools provide visibility in hybrid environments, including edge and cloud network monitoring. • Implementation of cloud native applications and microservices for platform management and orchestration to optimize performance and cost in the cloud. • Increasing appetite for flexible deployment models include SaaS and on-demand pricing, and use of virtual machines, software appliances, and hardware appliances to enable these deployments. • Evolution of traditional network monitoring platforms beyond the basic levels of infrastructure monitoring, and incorporating APIs for easy extension of additional services such as Digital Experience Monitoring, flows/flowlogs, and other applications that support business users and understanding of end-user experience. • Increasing focus on AI/ML advanced analytics to support artificial intelligence for IT operations ("AIOps") (e.g., for anomaly detection and event correlation and RCA). • Focus on network security and alignment between network operations and security operations. With our highly scalable, reliable, and extensible platform solution for network monitoring, OpenNMS is well positioned to address these market trends. Our zero touch appliance solution enables remote monitoring of edge components and enables additional hybrid and cloud integrations. Our traffic analysis (flows) and route monitoring solutions provide deep understanding of network performance. Our Architecture for Learning Enabled Correlation (ALEC) AI/ML solution provides valuable insights and visualizations for interpreting complex faults/alerts. - 9 -
RkJQdWJsaXNoZXIy NTIzOTM0