Enterprise radiology dashboard integrates with academic and community practices
A large academic health system has developed an enterprise radiology dashboard with the aim of streamlining and improving data sharing for all radiology operations across the organization. This has become increasingly difficult for healthcare institutions that need to integrate with academic facilities and community radiology practices, according to a case study published in the Journal of the American College of Radiology. [1]
In the paper, researchers from Penn State Health acknowledge a serious lack of literature pertaining to the development of radiology dashboards that facilitate integration with community health practices and academic organizations, particularly those that operate within a larger network like Penn State.
By embarking on this task, the organization was effectively roadmapping a solution to a novel problem. A custom enterprise radiology dashboard was developed at Penn State Health to provide clinicians and leadership with a more complete picture of radiology operations. This dashboard collected and processed data, including radiology reports, from various sources such as hospital radiology reporting systems, radiologist scheduling data, EMRs, and a variety of exports from the enterprise reporting system, including financial info.
“To create meaningful data that reflects the current state of radiology operations—or to be used in analyzing retrospective data, assessing trends, or monitoring progress—data from these multiple sources were combined, linked and or manipulated,” lead author Michael Moore, MD, with the Department of Radiology at Penn State Health, and his colleagues wrote.
Gathering and processing this data facilitated the creation of multiple smaller dashboards that can monitor the radiology service line from across the health system, including all academic and community practice divisions. This provided a more complete picture of radiology operations, effectively providing a central location to zoom in and look at everything happening with radiology scans and workflows at Penn State Health.
The dashboards facilitated improvements in radiology operations, including:
- Improved data analytics: The dashboards allowed for the calculation of productivity benchmarks within the academic practice division using two methods. These benchmarks provided insights into the performance of those divisions, offering more real-time insights and data that can be used in predictive analytics.
- Visualization of workloads: The dashboards helped redistribute overnight workloads between the academic and community practice divisions by providing visualized data on metrics such as exam completion, final signing, and emergency department turnaround times.
Despite the improvements, several challenges remain that may stop the dashboards from being as useful as they otherwise could be. For starters, Penn State Health was required to maintain custom infrastructure to support the dashboards, meaning most of the data was housed and sourced on site. This was found to be time consuming, potentially diverting IT resources that could be used for other data-based improvements across the organization. Additionally, the inaccessibility of certain data streams limited the full utilization of the dashboards, hindering the ability to properly assign radiologists to balance workloads in some cases.
“Proper longitudinal automated tracking of radiologists, even with direct data input of scheduling data from radiologist scheduling software, initially was difficult, as faculty may change assignments or cover multiple practice areas, or transition from trainee to faculty,” the authors wrote.
They also expressed concerns about ongoing acquisitions by Penn State, citing the limitation of the dashboards to add additional data streams. “This leads to operational questions as to whether to wait for these sites to obtain standard application as used throughout the health system, or to explore data extraction and storage from the existing databases,” the authors added.
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