Why Providers Should Centralize Analytics

In this special guest feature, Hossein Fakhrai-Rad, President & Chief Scientific Officer at BaseHealth, discusses how In the future, the insights derived from centralized analytics delivery models are likely to help hospitals improve quality, lower costs, identify at-risk populations and better understand performance. For that to happen, however, hospitals and health systems must first overcome the fragmented, decentralized approach to analytics that prevents them from realizing the full value of their analytics investments. Hossein is an entrepreneur who has 20+ years of academic and industrial expertise in identifying and characterizing risk factors that contribute to the progression of numerous health conditions. He was one of the founding members of ParAllele Bioscience which was sold to Affymetrix and a fellow at the Stanford Genome Technology Center. Hossein has a PhD in Medical Genetics and holds multiple patents and publications in the field of Genomics, Personalized Medicine, Disease Prevention and Health Risk Assessment.

Hospitals and health systems continue to invest in data analytics, but too often a fragmented, decentralized approach to analytics delivery models results in excessive costs, inefficiency and missed opportunities to improve patient care.

A number of factors have coalesced in recent years to catalyze greater investment in healthcare analytics – the ongoing transition to new payment models under value-based care, a greater emphasis on population health, increasing competition and an explosion in available health data from electronic health records, laboratory test results, and wearable devices – to name a few.

The momentum isn’t expected to slow any time soon. A recent report from Zion Market Research predicts the global healthcare analytics market to grow to $68 billion in 2024 from approximately $20 billion in 2017, a compound annual growth rate of more than 19 percent.

While there’s no question that provider organizations are busy writing checks to healthcare analytics vendors, there is some question about whether they’re getting an adequate bang for their bucks.

For example, a Deloitte survey of U.S. hospitals and health systems with greater than $500 million in revenues found that fewer than half of respondents said their organization had a clear, integrated data analytics strategy, while about one in four didn’t have a data governance model in place. Even more problematic, about one in three reported that they didn’t know their organizations’ total analytics spend.

Multiple Vendors, No Single Source of Truth

A common cause of many of these issues is a decentralized approach to analytics in which data analysis happens in different business units that do not share assumptions, analytics methods or insights broadly.  In contrast, under a centralized delivery model, an experienced team of data analysts report to one function at the enterprise level, even if they are assigned to serve different business units, based on strategic priorities set at the corporate level. This business-oriented team of analysts meets the need of organizational stakeholders while maintaining and developing intelligence in-house.

A centralized analytics delivery model is important, in large part, because it offers an improvement to the fragmented, incomplete data governance models that too many provider groups still operate. For example, it’s not uncommon for large health systems to contract with multiple vendors to analyze population health risk for groups of patients with different conditions, such as diabetes, osteoarthritis and others.

This lack of a single source of truth in analytics can lead to different answers to the same question, such as conflicting guidance on levels of risk, and in turn, on the highest-priority patients to target for interventions. As a result of this fragmented and potentially conflicting information, when prioritizing care plans and interventions, the health system cannot build a consistent, wholistic clinical profile with a 360-degree view of each patient that accounts for the same factors.

Health system decision makers are then left wondering which vendors’ information they should believe.

Delivering Analytics-as-a-Service Across the Organization

In addition to the fragmentation of data, there are a number of common barriers that prevent hospitals from efficiently and cost-effectively deploying analytics across their organizations, including territorial disputes over data, unclear roles and responsibilities and competition for already-scarce resources.

As with virtually all organizational transitions, success in centralizing analytics starts with buy-in at the top. Strong executive leadership must bring together talented people with deep experience in applying analytical expertise to solving pressing clinical and business issues.

A best practice is to place a senior-level executive in charge of analytics, potentially in a chief data officer role, to lead the organization’s centralization initiative. A key function of this role is to establish effective and comprehensive data governance practices, clearly defining what type of data the organization will collect, how the data is structured, who can access it and how it gets reported and presented to different people in the organization, among other steps.

Once the organization establishes a solid foundation for data, it will be ready to adopt a single analytics platform that delivers actionable information to decision makers. Today’s leading analytics platforms often employ machine-learning systems to automatically extract important insights that may not be otherwise apparent to human analysts.

Ultimately, the goal is to create one internal, centralized professional services group within the organization that delivers analytics-as-a-service to other stakeholders in the hospital. By structuring a hospital’s analytics functions in this manner, the organization can eliminate the fragmentation and cacophony of multiple systems that offer conflicting advice and prevent leadership from understanding the organization’s full analytics spend.

Centralization in Practice

Already, a number of prominent health systems have taken the leap to centralized analytics delivery models, including University of Michigan Health System (UMHS) and Beth Israel Deaconess Medical Center (BIDMC). UMHS, for example, has created comprehensive registries for population health and used them to generate predictive analytics that focus predominantly on chronic diseases. BIDMC, through its centralized analytics governance model, provides layers of decision support and analytics for its physicians, with the goal of understanding variations in cost and care to maximize quality, safety, and efficiency.

In the future, the insights derived from centralized analytics delivery models are likely to help hospitals improve quality, lower costs, identify at-risk populations and better understand performance. For that to happen, however, hospitals and health systems must first overcome the fragmented, decentralized approach to analytics that prevents them from realizing the full value of their analytics investments.

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