Healthcare analytics in action

As healthcare organizations transition to value-based care, there is an increasing need for actionable information. Many organizations do not know where to start in building an information framework that assists with decision-making and drives actions. Provider organizations, particularly large, complicated health systems, have incredible amounts of data spread over several hundred disparate systems that do not easily talk to one another. They can be described as being “data-rich” but “information poor.” While an abundance of new and emerging analytics-based decision support systems are available, many organizations that have invested in data aggregation and analytics technologies are struggling to realize value and achieve their end goals.

The key questions organizations are asking include:

• How do we ensure we are aggregating the right data that in turn generates actionable information?
• Where do we start in working with our population health analytics vendors to create organizational capabilities rather than being driven (and limited) by product functionality?
• How long will this take? How much will it cost, and how do we measure value of our investment?
• Given competing priorities, how do we create a data strategy and an information roadmap for the organization?

While challenges are varied, the biggest reason for unrealized success is that organizations miss out on an opportunity to create a well-defined digital health and analytics strategy, establish a multidisciplinary data governance structure, and create a problem-backed information framework that serves as a blueprint for technology vendors to deliver results.

Best Practices

High performing healthcare organizations that have developed result-oriented analytics have incorporated best practices that focus on building a strong information management framework, establishing analytic-driven needs and requirements, and creating actionable information that drive knowledge enablement and strategic success. Below are some of these best practices.

Begin with the end in mind. The key to establishing an analytics framework that drives decision-making and actions within the organization is to ensure that you begin with the end in mind, and develop a clear understanding of the problems to be solved. Some of the overarching questions could the following: What are some of the key objectives that your organization wants to achieve? Is facilitating transition of care within the integrated care network an immediate need? Is your organization aiming to capture market share by offering competitive services and offerings? Is referral management and reducing flow outside the network an area of concern? Is entering into value-based contracts one of the long-term goals for your organization?

Succinctly outlining end goals guides what problems need to be solved and helps conceptualize a knowledge framework that will assist with the decision-making. It also ensures that the information analytic framework appropriately aligns with the organization’s strategic priorities.

Champion for an integrated solution that creates operational capabilities versus being led (or being limited) by technology product functionality. Consider the following comments from Gary Weiner, chief information officer at Community Health Systems in northwest Indiana: “Exchange of information should be as easy as withdrawing cash from an ATM. Unfortunately, lack of standards and misaligned vendors prohibits organizations from achieving speed to value. Population health analytics vendors are overpromising and under delivering value and still developing their go-to market strategy. They need to come together and break barriers to interoperability so organizations can realize the promise of analytics and enable value-based care.”

As organizations select and design their analytics solutions, it’s easy to fall victim to the glitzy functionality and reports an application can provide. The true success of any technology application is how well it is used by the end-user, which is often dictated by its ability to integrate into the operational workflow. Building action-oriented information through an analytics tool must begin with understanding the operational needs and translating these into requirements and capabilities. If an analytic product aligns with the organization’s capability requirements and needs, the results will be a successful tool supporting the organization. Absent this alignment, it will create fragmentation in use and incomplete outcomes. Utilize and champion the information blueprint to align technology vendors to break down barriers and build bridges with desired standards of interoperability.

Create multi-disciplinary data governance. It is important to identify key consumers of analytics within your organization early on. Understanding the information needs of the users, their level of data literacy (ability to understand and interpret data) and the ability to act on information offered via analytics platform determines the pace at which your organization can adopt a knowledge-based, decision-making system.

Setting up a multidisciplinary data governance structure is an important element of success that includes the following:
• guidelines for management of the quality of data being leveraged across the continuum,
• a road map to incrementally building data literacy within consumers of analytics, and
• an operational framework that allows for maximizing of data exploitation for organization’s benefit.

Multidisciplinary data governance ensures that the analytic direction supports the strategic initiatives of the organization. It becomes an important mechanism in establishing data integrity, alignment of technology platform criteria, and producing actionable information.
Start small and build for scalability. Often analytics product implementations become too complex, too fast, and they lack the ability to produce meaningful information that can be acted upon. Information produced from an analytics platform might identify a problem, but an established operational framework is needed to solve the problem. The usability of the analytics model is closely tied to the best practices, methodologies, organizational structures and workflows that surround it. Organizations that have successfully built action-oriented analytics have started small, often around a “proof-of-concept,” and gradually expanded based on the strategic and operational requirements of the organization. A pragmatic, results-based approach allows organizations to ensure that their information needs are addressed around the capabilities and results, and not focusing on functionality.

Build for adaptability to evolving strategic needs. Quick wins via structured proof of concepts allows for increased engagement with stakeholders. It will also highlight the gaps in collection of data at the front lines. Consider establishing a change management framework that helps manage the culture change in how the organization collects data, uses actionable information and makes outcome-based decisions. Ensure that the information framework factors in data sets that you would want to tap into in the future (community data, social, etc.).

Build for sustainability by focusing on organizational  literacy. Create a road map that allows you to gradually build the required skills within your organization. This minimizes risk and dependency on outside resources and creates ownership. As organizational maturity to consume complex analytical information increases, plan for future resource needs such as data scientists to be brought into the organization. Champion using the power for actionable information to create analytic consumers vs. application users within your organization.

Transitioning to an action-oriented analytics framework drives the ability to leverage meaningful insights and generate results. Taken over an evolving landscape, this journey is filled with many lessons learned and challenges. Healthcare organizations can leverage best practice recommendations to avoid common pitfalls and ensure sustained success.