Enterprise Clinical Data Strategy: How AI is Leading the Way
Implementing an enterprise clinical data strategy is a complex undertaking, often fraught with challenges. However, for health plans in particular, the need for clinical data is only accelerating as the industry seeks to streamline administrative processes from prior authorization to digital quality. Advancements in artificial intelligence (AI) presents a great opportunity to overcome these hurdles and meeting these objectives requires a foundation build upon data. This blog discusses the key pillars of implementing an enterprise clinical data strategy and addressing challenges that still persist today.
Data Acquisition and Interoperability
Health plans typically acquire data from multiple systems, including electronic health records (EHRs) and claims management systems, among others. These systems often use different formats and standards, making it difficult to achieve a unified view of patient information. While claims data is ubiquitous, clinical data acquisition requires a patchwork agreements and connections with provider systems, EHRs, HIEs and other types of collection vendors.
Enterprise Solution
An effective clinical data strategy should leverage a multi-channel interoperability strategy, establishing connections with multiple sources systems and networks that can provide structured clinical data through real-time automated integrations.
Centralized Processing and Intake
With the proliferation of EHR systems and health information networks, clinical data is now represented in a variety of formats and structures. Legacy systems were optimized for unstructured data such as PDF and images. As interoperability grew, newer systems capable of exchanging structured formats emerged, introducing a myriad of structured formats from CDA to FHIR. This complexity leads to increased costs, technical debt and perpetuates data-siloes working against the implementation of an enterprise approach.
Enterprise Solution
An enterprise approach should seek to simplify this complex infrastructure, in favor of modern systems that can process the entire spectrum of clinical sources, from structured and unstructured data simultaneously.
AI Powered Data Extraction
Not only are clinical data standards growing more complex and fragmented, the volume of data is rapidly expanding. Health plans can no longer rely on manual review programs to extract insights often buried in unstructured text. From Prior Authorization to Digital Quality, modern systems powered by AI are capable of extracting data in an automated way that can be leveraged for multiple programs, enabling a foundation of rich clinical data to power any enterprise strategy.
Enterprise Solution
Modern data processing systems powered by AI, automates the extraction of clinical evidence from structured and unstructured datasets.
App Ecosystem Enablement
Across healthcare, data and insights extracted from EHRs are integrated into a variety of applications that support downstream use-cases. These range from certified quality reporting platforms to provider facing apps integrated within the EHR. While third party EHR apps have standardized on FHIR APIs, the rest of healthcare is undefined. An enterprise strategy should not only focus on the ingestion of data, but ensuring insights can easily integrate into platforms of any kind to deliver enterprise value.
Enterprise Solution
Enterprise clinical platforms should support industry standards (e.g. FHIR APIs), allowing insights to efficiently power an ecosystem of 3rd party applications.
Change Management and Adoption
Implementing a new clinical data strategy requires buy-in from various stakeholders and seamless integration into existing workflows. Resistance to change and the complexity of new systems can hinder successful adoption.
Enterprise Solution
Developing a collaborative partnership between IT and the business is critical towards securing buy in, eliminating data silos and ensuring the value of clinical data is extended across the enterprise.
Conclusion
Implementing an enterprise clinical data strategy presents a range of challenges, from data integration and quality to scalability and compliance. This transformation requires a multi-pronged approach to address various business and technical challenges common in health plan operations today.
As health plans continue to evolve and expand their use cases, transformative technologies like AI will be crucial in overcoming these challenges and unlocking the full potential of clinical data. Embracing these technologies not only addresses current hurdles but also positions health plans for future success in an increasingly data-driven healthcare landscape.