When the pandemic hit at the beginning of 2020, the entire world went into an era of remote everything. Tech giants like Microsoft achieved two years' worth of digitization in just two months. The biggest reason: maintaining business continuity. C-suite leaders, in every industry, turned to allocate significant budgets for acquiring relevant technology to drive innovation. In the midst of all of this, healthcare was struggling with outdated systems embedded in legacy IT infrastructures.
For healthcare, investing in digital solutions is more than just maintaining business continuity but also transforming the way care is delivered. The global healthcare IT market will grow to USD 974.5 billion by 2027 at a CAGR of 19.8% indicating a significant shift towards digital solutions and innovation in healthcare. As per a recent McKinsey report, these investments will have a significant business impact. Digital technologies will likely intensify the shift to lower-cost sites of care, and in many cases may replace in-person consultations with virtual modalities. As a result, inpatient and outpatient healthcare spending could decline by as much as $175 billion to $220 billion, reducing readmissions and enhancing care quality.
But in the ever-evolving healthcare industry, ensuring that investments are future-proof and sustainable can be challenging. According to research by Bain & Company, more than 50% of providers are struggling with the flood of offerings in the market. They cite concerns about missing high-impact new solutions or simply feeling overwhelmed by the number of offerings to evaluate. Additionally, since the pandemic, many healthcare organizations adopted new technology solutions across a broad set of clinical and operational areas. As a result, many are emerging from 2022 with greater complexity in their tech stacks than ever before. But leveraging these technologies to keep up with innovation in healthcare is hard.&
Data silos are the biggest hurdle. Healthcare data sources such as EHRs, claims systems, financial systems, administrative systems, etc. are siloed. This siloed infrastructure results in data analysts and engineers often struggling to create and analyze complete longitudinal records of a given patient, curbing the speed of analysis, increasing the cost of generating insights, and severely impacting decision-making capabilities and ultimately outcomes. On top of this, health systems that have multiple EHRs face an added complexity that makes it near-impossible to generate comprehensive patient insights through their native systems.&
The lack of data standardization in healthcare is another obstacle limiting organizations to leverage data-driven technologies. Existing healthcare IT (HIT) systems lack the capabilities to harmonize data coming from multiple clinical and non-clinical sources such as EHRs, claims systems, pharma, and lab, with each having unique data attributes. These data are often analyzed in silos or brought together for point solutions to solve targeted use cases through significant effort.&
Additionally, the local nature of provider analytics and the lack of a centralized data warehouse results in significant redundancy and a lack of true enterprise insights. Local teams focus on their targeted use cases, build their own “fit for purpose” data warehouses, implement a unique data model, and generate their version of the truth through custom analytics. These analytics aren’t scalable, add significant cost, and have the potential to create confusion or lack of trust in the data when numbers don’t align between different parts of the business. Finally, the lack of data standardization through a centralized data warehouse and enterprise data model prevents true interoperability, which is crucial for future success.&
The fact is that end users can only get a view of their total patient population and generate true enterprise insights if they integrate and unify all critical data into a single enterprise data model in a centralized data warehouse. Achieving this requires significant effort to standardize and normalize data inputs, monitor, constantly improve data quality, and harmonize into a unified longitudinal record. Only then can analysts and downstream clinical/business users derive real value from the wealth of data within the organization to achieve their desired improvements on outcomes. The significant effort required to address these pain points ends up curbing the health system’s speed to innovate efficiently and grow overall.&&
An enterprise data model with a data platform as its engine serves as the fundamental building block for innovation at scale in any data-driven healthcare organization. Without a unified data model, healthcare organizations are forced to start from scratch each time they want to generate enterprise-level insights to drive innovation. Scaling data processes becomes an operational nightmare. For each new application developed or licensed, new analysis/report/dashboard, or any other use of data, the process of sourcing, normalizing, standardizing, and cleansing the data is restarted from scratch. In addition to this massive effort, there’s a huge risk associated with not having standardized analytics (due to the siloed nature of analytics discussed above), leading to the dearth of comprehensive actionable insights.
Beyond the increased cost due to duplication, redundancies, and inefficiencies in the system, and a lack of reusability, business outcomes could be significantly impacted because leadership doesn’t have the insights required to lead the organization in a highly complex market, where new business threats and pressures crop up almost daily. Decision makers tend to overlook these potential long-term “hidden business costs” during IT investment planning. Unfortunately, by the time that’s recognized, it's often too late for the organization to take corrective action, as they’re being bogged down with the multiple issues discussed above. Any further efforts to change course are complicated and end up wasting resources, time, and money, adding to the challenges already faced in this tough economy.
Recognizing the need for an enterprise data model and centralized data warehouse is important, and so is deciding how to approach it. The first question organizations face once they decide to go in this direction is build vs. buy. Many organizations venture into building out the architecture and infrastructure themselves without realizing the full cost until they’ve invested significant capital and years into the effort. By then it’s too late. As organizations move forward with a build they often encounter delays, cost overruns, and reduced value delivery, ultimately finding themselves in a position where they’re locked into legacy infrastructure and tooling that they don’t have the time, staff, or capital to maintain—much less refresh to more current solutions. All of these challenges slow down the speed at which organizations can innovate and ultimately impact their overall business performance.&
A solid foundation for your innovation into the future
An enterprise data model built on a centralized data warehouse with data architecture best practices is a sure way to enable innovation at scale. An enterprise data model drives efficiency, and enables healthcare organizations to connect and seamlessly interchange data across applications, implement machine learning (ML) algorithms to drive forward-looking insights, and& route those insights into applications to drive improved outcomes at the point of care. On this solid foundation, data scientists become more efficient and can quickly solve a variety of healthcare use cases at scale across the organization, driving innovation. Furthermore, analysts can quickly answer the simple questions that, when left unanswered, prevent business leaders from making the best decisions or decisions at all. Building and powering these capabilities at scale is only possible on the contemporary foundation of an enterprise data model used by a robust centralized data infrastructure.&
Unlocking the true value of healthcare data with Innovaccer’s unified data model (UDM)
Innovaccer approached healthcare’s data challenges with a unique and hybrid approach of leveraging modern distributed data technologies with data model foundations based on data architecture best practice principles. Innovaccer’s Unified Data Model (UDM), the foundational component of Innovaccer’s FHIR®-enabled Data Activation Platform (DAP) is a highly scalable and efficient enterprise data model that supports providers, payers, and life sciences organizations in different business and analytics use cases to deliver value. With Innovaccer’s DAP and UDM, stakeholders also have the freedom to extend their data model to meet their dynamic requirements. This solution empowers organizations to build customized dashboards and business-specific solutions with a rich set of APIs. The quality rules of the UDM aren’t restricted to predefined constraints, letting organizations add value to their core product and extend it to solve different real-world issues.
Let's explore some customer challenges where they leveraged an enterprise data model powered by a healthcare data platform to drive value.
Banner Health—one of the top health systems in the country—identified redundancies in its IT backbone, refined its data archival strategy, and streamlined applications with intelligence layers to reduce technology costs. Using an integrated, comprehensive, interoperable data platform, Banner deployed a single-enterprise data lake to close care gaps and access actionable, data-driven insights aimed at improving care delivery. The network gained an enterprise data fabric within its budget and achieved a 70% reduction in IT infrastructure costs by moving to a SaaS-based model. It condensed more than 10,000 ad-hoc reports by converting them into around 800 user-friendly enterprise metrics, which resulted in easier access to actionable insights. As a result, the network saved $5.7 million on annual IT operational expenses.
Innovaccer’s #1 KLAS-rated Data Activation Platform allowed Banner Health to:
The Banner Health system achieved: &
Another example: Central Maine Health Care (CMHC), an integrated healthcare delivery system that serves over 400,000 people in the central, western, and mid-coast regions of the state. They deployed a comprehensive, data-driven TCM (transitional care management) solution to improve patient outcomes, risk-stratification, automate value-based care delivery,& reduce readmission rates, and deliver actionable insights at the point of care. This solution is enabling rapid innovation with expansion of data-driven strategies for emergency visit follow-up.
Using Innovaccer’s unified data model on the #1 KLAS-rated Data Activation Platform allowed the CMHC to:
The CMHC health system achieved: &
The pace of change in healthcare is increasing, and healthcare stakeholders simply can’t keep up using yesterday’s technology today. The industry is at an inflection point, where we finally have the technology to bring patient data together and make it useful—from inside our own organizations, public data sources, and increasingly from patient-generated device data—to deliver the longitudinal patient view that’s long been sought but never fully realized.
People are starting to realize they need a robust enterprise healthcare data model on a centralized data platform to drive effective digital transformation. Innovaccer’s customers are leading the way to equitable care with the most complete patient insights in the industry, made possible by the transformative power of our Data Activation Platform.
Innovaccer’s robust unified data model on the #1 Best in KLAS Data & Analytics platform helps organizations rapidly innovate to improve patient care and outcomes, reduce IT spending and workforce burnout, and provide significant improvement with millions in value generated for the communities they serve, their stakeholders, and their partners.
The big winners in healthcare will be those healthcare organizations that understand their data well and can drive transformation by unlocking their data’s value. And to do that, a data platform is essential, because it makes it possible to activate your data assets to drive rapid innovation.
To learn more about Innovaccer’s unified data model and its #1-ranked Data Activation Platform, click here for a quick and free demo.