Identification of Design Requirements and User Needs for a Software Application that Collects Acute Stroke Treatment Data During Clinical Workflow (Preprint)

BACKGROUND Clinical registries are critical for monitoring processes of care in diseases and driving quality improvements. However, many smaller hospitals do not have the required resources to gather the necessary data to contribute to registries. This study aims to design and evaluate a data collection tool for acute stroke treatment. The evaluation will identify key design requirements that facilitates prospective data collection and adds value to clinicians. OBJECTIVE The objective of this study was to identify the design requirements for the collection of acute ischemic stroke data during clinical workflow. METHODS A prototype tool was developed for testing. Clinicians were recruited through convenience sampling to observe the prototype’s use in a simulated clinical field experiment, then follow-up interviews were conducted to gain feedback. Qualitative data analysis identified design requirements that were implemented to iterate the design and finalize the design requirements. RESULTS Three participants were recruited from two hospitals in the simulated field experiment. The simulated scenario times took between 10-12 minutes, with 1.2 to 3.7 minutes spent using the prototype. The simple and condensed layout, features such as calculators and timers, and the final pop-up summary received the most positive feedback. Issues identified included the small target sizes leading to higher error rates, the lack of coloring in important features reducing their visibility, and the grouping of mandatory and optional information layouts led to a disjointed flow. The key design requirements include prioritizing simple dynamic layouts, sufficient target sizes to prevent errors, useful features with clear visual cues, and prompt data feedback to facilitate seamless integration. CONCLUSIONS A prospective data collection tool for clinicians to use during stroke treatment can add value for clinicians and, with further testing, can be integrated into workflow.