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 the health of populations, and increasing competition. But also the 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 down 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 providing 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 placebat all. 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 in-house intelligence.

For a large part, a centralized analytics delivery model is important because it offers an improvement to the fragmented, incomplete data governance models that too many providers still use. 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 and osteoarthritis among 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 clinical profile with a 360-degree view of each patient that accounts for the same factors.

This results in health system decision makers being 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 aim is the creation of 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 this way, the organization can eliminate the fragmentation and cacophony of multiple systems that offer conflicting insights and prevent leadership from understanding the organization’s full analytics spend.

Generalization in practice

Already, prominent health systems like University of Michigan Health System (UMHS) and Beth Israel Deaconess Medical Center (BIDMC) have taken the leap to centralized analytics delivery models. 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.

Source: Insidebigdata