Maturity model helps guide PACS deployment, development

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A PACS maturity model describing five levels of PACS evolution and corresponding process focus may be able to be applied for organizational assessments, monitoring and benchmarking purposes.

Developed by Rogier van de Wetering and Ronald Batenburg from the department of information and computing sciences at Utrecht University in the Netherlands, the model—which also identifies three development trends of PACS maturity and evolvability—was published online before print in the International Journal of Medical Informatics.

The authors initiated the idea to review PACS literature on maturity and evolvability, and develop a PACS maturity model based on a qualitative meta-analysis. They selected 34 papers that set out the theoretical and empirical contributions of PACS in developing their model.

In their overview, Batenburg and van de Wetering identified three trends of PACS maturity and evolvability:

1. Radiological and hospital-wide process improvements;
2. Integration optimization and innovation;
3. Enterprise PACS and the electronic patient record (EPR).

In the first trend, PACS technologies are introduced, which allows for the distribution of imaging data via internal and external networks. Workflow changes also occur as a facility moves from analog to digital processes, which requires a high level of integration of the various imaging information systems such as a RIS or hospital information system (HIS).

The second trend identified by Batenburg and van de Wetering--integration optimization and innovation--permits the recovery of statistical information allowing for quantitative control mechanisms. This will help detect critical points in a hospital service workflow allowing for the diagnosis process to be optimized

Leading-edge technologies, such as advanced visualization tools and computer-assisted detection software, expand the capabilities of a PACS and increase its reach into clinical specialties beyond radiology. For example, cardiac CT angiography for cardiology and perfusion MR imaging for stroke detection in neurology are exams dependent on image post-processing tools and PACS for distribution across a healthcare enterprise.

The third trend Batenburg and van de Wetering recognized is the integration of PACS with and EPR. All three trends helped the pair construct their five-level PACS maturity model.

In level 1, PACS infrastructure, Batenburg and van de Wetering describe PACS “as the basic and unstructured implementation and usage of image acquisition, storage, distribution and display.”

The second maturity level, PACS process, is one of effective process redesign, optimizing manual processes in radiology and initiating transparent PACS processes outside radiology, according to the authors.

“The focus, however, at this maturity level, is still only on medical images and is therefore restrictive in managing (hospital) workflows,” they noted.

The third level, clinical process capability, is when PACS has evolved toward a system that can hand workflow and patient management as well as hospital-wide distribution of imaging data, communication and image-based clinical workflow.

“The evolution to this level requires important alterations in terms of PACS processes, extending the scope beyond imaging data and the level of integration of health information systems like HIS, RIS and PACS,” the authors wrote.

Level 4, integrated managed innovation, is characterized by the initial integration of a PACS into an EPR and cross-enterprise exchange of digital imaging data and supporting documentation, according to Batenburg and van de Wetering.

“At this maturity level,” they wrote, “integrated PACS solutions are also applied for statistical information, intelligent data mining purposes and quantitative control mechanisms.”

The fifth level of their PACS maturity model is the optimized enterprise PACS chain. This stage is marked by a fully integrated PACS and EPR.

“Moreover, at this level the adoption within the wider EPR and healthcare facility integration is continually optimized and the operational improvements yield process innovations and overall efficiencies in the continuum of the patient-care delivery process,” the authors noted.

The duo observed that the levels described in their model may not be fully exclusive or deterministic. However, they believe that by identifying these five levels of PACS maturity a healthcare organization can use the model as a tool for developing the capabilities of its deployed PACS technology.