The better way: Self-service access with centralized expertise and support on demand to enable dynamic data exploration
The self-service approach to data access enables health systems to empower clinicians and administrators by allowing them to have a dialogue with their own data. This drives greater volume and speed of quality- and performance-improvement initiatives. Further, when operational improvements require reporting automation and dashboard visualizations, analyst groups are freed from the need to perform initial data exploration, driving savings in time and resources.

This approach also provides significant cost advantages. By using self-service, hospitals empower their existing clinicians and staff to become data-literate. Further, because clinicians throughout the organization can initiate their own projects, health systems gain the ability to dramatically accelerate the number of quality-improvement initiatives that are conducted concurrently. Hospital leaders are better able, using timely data, to select which projects to support, or where in the organization to make changes, when presented with data on the potential outcome or cost benefits. Because interventions cost time and money to implement and evaluate, using a data-driven approach prior to "acting" will lead to cost savings.

A look at self-service in action: The Ottawa Hospital (TOH)
One Canadian hospital that is benefiting from this self-service approach to data exploration is TOH, one of the largest academic and research hospitals in Canada, consisting of three hospital sites, 1,420 beds, and more than 12,000 employees and support staff.

Prior to adopting a self-service approach, the hospital employed a centralized data request process to support data-driven improvement projects. In the centralized process, a researcher, clinician, or healthcare professional would first submit a data request to the analytics team. The analytics team would then meet to determine the potential project’s needs, data plan, and cost estimate. The requestor would then need to obtain the necessary approvals from leadership and regulatory teams, and, if approved, the analytics teams would produce the requested data.

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