Why Healthcare Data Strategy Fails Without Operational Alignment

You can expect to find a document regarding data strategy in nearly every large health system. These documents get a board presentation, and thereafter they collect dust. The main issue lies in the fact that these documents are often well-drafted, but lack a functional understanding of the health system’s daily operations, such as who the key decision makers are, the materials they use to support their decisions, and the obstacles they confront in the process.

Most of these documents also disregard the various staffing models and everyday operational pressures that determine the degree to which a business is open to the adoption of new procedures. Once a strategy is handed down to the employees who are expected to execute it, the gap will be apparent. Strategies constructed by consultants or by a centralized IT group envision an endpoint that is several years away, but lack the intermediate actions that will move people in that direction. The missing element is operational alignment. The gap will be closed when every aspect of a data strategy is tied to a real decision, a real workflow, and a real person who is responsible for that aspect.

What It Means to Achieve Operational Alignment

While conceptualizing a data strategy may require a few change management slides, achieving operational alignment will not. Structural operational alignment describes the purpose that each piece of the strategy entails. The various components of a data strategy will be operationally aligned if they are tied to real decisions that a designated person is empowered to make.

Most strategies do not have traceability. They may include target architectures, governance models, and maturity tiers. All of those may be perfectly valid frameworks, and they may articulate an excellent model, but if none of this is connected to a decision someone actually makes, they will remain untactile. Alignment starts with decisions and is concerned with the data that supports those decisions. It is the opposite of starting with the data platform and hoping that decisions will be made.

Start From The Decisions, Not The Architecture

What healthcare leaders are trying to accomplish what are trying to accomplish is what should drive operationally aligned strategies. What will the Chief Medical Officer be able to see when he or she is able to visualize and explain difference in varying clinical outcomes? What will the Chief Financial Officer be able to see when he or she is able to visualize where the revenue cycle is not meeting expectation? What will the Chief Nursing Officer be able to tell when he or she is able to staff based on expected patient demand versus staffing based on patient demand from the previous year? Each of these decisions identifies the data and analysis that is relevant, and each of these decisions has an owner.

Organizations are often inundated with questions and challenges that cannot all be adequately addressed at the same time, and which far exceed the limitations of any single strategic roadmap. Out of necessity, focus on just three to five of these use cases. This limited set will help determine the direct path to building and enhancing data and analytic capabilities. Prioritize the opportunities that enable key data-driven decisions that will result in significant and positive impact on the business. All other use cases remain on a long-term roadmap. These use cases are acknowledged, but remain unstaffed, prioritized and named. The discipline exists in focus. General strategies, themselves, are clear evidence of a lack of focus.

Misalignment, or lack of focus, manifests itself in a few distinct and recognizable patterns.

The first is the platform trap. Significant investment is made in the architecture of modern, cloud-based data platforms and analytic tools. The assumption is that usage will naturally follow. It will not. Clinicians and staff will develop new analytic habits when embedded in the workflow in the support of the decisions they make in the course of their day-to-day work.

The second is the misunderstanding of data strategy as an IT project. If the strategy is designed and implemented solely by IT or a centralized analytics unit, while the operational side is an end-user, front-line behavior does not change. Decision makers are not going to be present in the room where the strategy is conceived, so it won’t have operational work reflected in it.

The third, and in many ways the most common, is governance and data quality being considered low priority functions. When those making decisions do not have confidence in the data, they revert to using their instincts, side spreadsheets, and the strategy becomes ineffective, regardless of the quality of the underlying platform.

Signs Your Strategy Lacks Alignment

Strategically, the purpose of systems and architectures is to enhance operational decisions. However, operational outcomes are secondary to the phased advancement of technology. Roadmaps, dashboards, and metrics exist; however, the metrics are presented verbally as dashboards are ignored and decisions are made in a meeting. The strategy addresses the technical owners but does not address operational ownership. While there can be isolated issues in the strategy, taken cumulatively, they indicate a lack connection of the strategy to the operational state of the organization.

The Foundations of Sustainable Alignment

For a healthcare data strategy to remain consistently aligned, three things must work in combination.

The first step is to gain your executives’ support for a few specific measurable goals. This ties the strategy to the outcomes the data and analytics will provide, and gives the team metrics for movement.

The second step is the integration of analytics. This must be embedded into existing systems, meetings, workflows, and decision-making processes, rather than creating a process that runs parallel and in competition to existing processes. The data must be embedded into the system at the time and place of the decision; you must not force employees to leave their workflows to source the data.

The last step calls for a governance cadence. This requires a regular review and adjustment of the work to account for the changing needs and shifting priorities of the organization. The data strategy will support the evolving needs of the organization, such as the changes in leadership and the migration of the EHR.

When this is successfully applied, the data strategy is operationalized and integrated into the organization’s daily workflows like quality management and financial management, rather than simply a descriptive document that illustrates a future process.

Authoritative sources

• Agency for Healthcare Research and Quality
• HIMSS
• ONC — HealthIT.gov