Smart Acute Stroke Quality Registry Design-Data Elements Identification

Stroke is one of the most important health problems around the world. Care quality improvement in the acute phase is significantly influential on stroke prognosis. An acute stroke quality registry that is integrated with a guideline-based support tool is a powerful system for measuring and improving care quality. As the first step in registry system design, the goal of this study was to identify relative data elements.

A list of common data elements taken from the National Institute of Neurological Disorders and Stroke and a list of data elements for paper-based medical records were first evaluated, then compared with each other. In parallel, a literature review was conducted to explore the main data elements in acute stroke registries. Considering quality improvement as the main purpose, a second study was undertaken to identify the measures of acute stroke care quality. For guideline-based smart diagnosis of patients' eligibility for thrombolytic treatment (as a clinical support tool), clinical guidelines of the American Heart Association were assessed, and appropriate eligibility criteria were identified. Finally, a questionnaire was prepared based on the identified data elements. The questionnaire was distributed among 17 neurology physicians for identification of essential data elements (minimum data sets).

Patient-centric data elements were identified and classified into 3 categories: (1) data elements identified based on acute stroke care quality measures; (2) data elements for diagnosis of patients' eligibility for tissue plasminogen activator treatment based on clinical guidelines; and (3) essential data elements based on paper medical records. After duplication removal, the 3 categories of data elements were integrated. Finally, essential data elements were identified using the neurology clinical experts' survey.

Compared to traditional disease registries, quality improvement registries cover much more detailed data elements. Integration of medical record data elements with care quality measures, as well as guideline-based criteria, results in a powerful source of data for more exact studies and analysis by clinical support tools. Identifying essential data elements as a mandatory part of the system helps with more accurate data entry, and can also be considered a ready-to-use item for other relative systems.