Overcoming Challenges of Developing a 360-Degree Patient View

The healthcare industry is evolving rapidly to adjust to new care models that support the increased utilization of innovative health information technology. Through the continual advancements of these interventions, significant improvements are apparent, such as the progression of electronic health records, patient engagement and care team communication. Yet the emergence of new digital solutions has yielded significant complications, such as the fragmentation of the information landscape. Data integrity also remains an issue, including the prevalence of inefficient and overlapping fragments of patient information existing in various data silos, further complicating the attainability of a singular, accurate and thorough patient record. Healthcare organizations and entities struggle to maximize efficiency of essential operational activities, a challenge exacerbated by attempts to utilize IT infrastructures with separate systems, such as data warehousing, clinical data, provider networks, care management, and member and provider portals. This can often also lead to further complications when engaging with other healthcare organizations that rely on disparate electronic medical record (EMR), practice management, billing and data management systems.

Accessing a comprehensive, transparent view into a patient’s medical history, as well as recommended treatments, yields enhanced decision-making, supports care management, limits preventable errors, and improves care outcomes. The provision of updated, detailed patient information also equips healthcare providers with the resources required to identify compliance-related issues and address care gaps in a timely manner. This proactive care method is essential to reduce costs and avoidable readmissions, provide quality care for all populations, and, ultimately, develop an infrastructure that supports a 360-degree patient view. By initially addressing this issue as a data problem, the Virtual Health team was determined to overcome the challenge faced by a multi-state Blues plan. Although the required data existed, processes had to be developed to find, extract, and file it. We deconstructed the challenge into three basic components: (1) data integration; (2) data normalization; and (3) data binding.

Data Integration
The first step was to effectively aggregate patient data from a myriad of disparate sources maintained by the plan and its delegated entities.“Interoperability”is one of the most frequently used and misleading
terms in health IT. Because the technology landscape consists of a multitude of legacy systems, and because data standards are neither universally adopted nor identically implemented, the only way to effectively integrate with the plan’s existing systems and formats was to create a dedicated health information exchange (HIE). The key issue contributing to failed HIEs was a combination of inability and vendor unwillingness to comply with each HIE’s requirements for data integration. Based on this insight, the only practical path forward was to ensure our system could both consume and generate data in the preferred format of each of the Blues’ other vendors.

Our interfaces were based upon a modular architecture that followed best practice object-oriented patterns to rapidly adjust interfaces for format variations and for new systems

We built or customized our existing interfaces for every standard type of counterparty extract, including enrollment/eligibility, EMR, claims, authorizations, practice management, pharmacy, lab, vendor management, and provider network management, as well as ensured support for both standards and proprietary formats. Our interfaces were based upon a modular architecture that followed best practice object-oriented patterns to rapidly adjust interfaces for format variations and for new systems. This approach enabled unparalleled integration timelines for the plan to connect to state, claims, authorization, and provider management systems, and ensured that the challenging regulatory timetable was achieved.

Data Normalization
Step two was ensuring that all the disparate data being consumed by our system was consistently mapped, indexed, and stored. To achieve this, the data schema had to be sufficiently comprehensive to encompass all major categories of healthcare data and also remain flexible to support variability across vendor systems and the plan’s needs. We then were tasked with developing an approach to accurately define data dictionaries, validation criteria, and business rules that could apply across data domains. A substantial amount of critical information is captured in an unstructured format. To ensure that the system could optimally support all data categories, we designed our data repository to contain the elements of both a data warehouse and a data lake, which we termed as an ‘adaptive data model’.

Data Binding
While aggregating and indexing cross-domain data about a patient is valuable, it is the binding of that data to workflows and analytics that empowers care teams to work more efficiently. In collaboration with the Blues’ clinical operations teams, we built data-driven workflows that alerted to the next best action for each case based on each patient’s unique profile and most current information streaming into the system.Most population health systems focus on calculating quality metrics and care gaps across the entire patient population, but this approach fails to provide actionable feedback to the providers, care managers, and service coordinators on the front lines. By calculating these measures in realtime at the individual patient level and tying them to tasks and dashboards directly leveraged by care teams, it is possible to ensure more optimal and timely interventions at the point of care.

By adhering to the three components, the Virtual Health platform enabled the success of the plan as it overcame the complexities of attaining optimal interoperability, scalability and flexibility while adhering to government regulations and resolving issues associated with complex populations. As the transition to value-based care continues to transform healthcare, attaining a360-degree view of a patient view is critical to ensure quality outcomes, care and operational processes.