Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process

Abstract

Background As the first point of contact for patients with health issues, general practitioners (GPs) are frequently confronted with patients presenting with non-specific symptoms of unclear origin. This can result in delayed, prolonged or false diagnoses. To accelerate and improve the diagnosis of diseases, clinical decision support systems would appear to be an appropriate tool. The objective of the project ‘Smart physician portal for patients with unclear disease’ (SATURN) is to employ a user-centered design process based on the requirements analysis presented in this paper to develop an artificial Intelligence (AI)-based diagnosis support system that specifically addresses the needs of German GPs. Methods Requirements analysis for a GP-specific diagnosis support system was conducted in an iterative process with five GPs. First, interviews were conducted to analyze current workflows and the use of digital applications in cases of diagnostic uncertainty (as-is situation). Second, we focused on collecting and prioritizing tasks to be performed by an ideal smart physician portal (to-be situation) in a workshop. We then developed a task model with corresponding user requirements. Results Numerous GP-specific user requirements were identified concerning the tasks and subtasks: performing data entry (open system, enter patient data), reviewing results (receiving and evaluating results), discussing results (with patients and colleagues), scheduling further diagnostic procedures, referring to specialists (select, contact, make appointments), and case closure. Suggested features particularly concerned the process of screening and assessing results: e.g., the system should focus more on atypical patterns of common diseases than on rare diseases only, display probabilities of differential diagnoses, ensure sources and results are transparent, and mark diagnoses that have already been ruled out. Moreover, establishing a means of using the platform to communicate with colleagues and transferring patient data directly from electronic patient records to the system was strongly recommended. Conclusions Essential user requirements to be considered in the development and design of a diagnosis system for primary care could be derived from the analysis. They form the basis for mockup-development and system engineering.

Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study

“…Previous research on CDSS requirements has primarily relied on methods such as interviews [ 31 , 34 , 35 , 38 , 39 ] and group discussions [ 30 , 34 , 35 ] to elicit users’ subjective needs. Based on recommendations from clinical experts and medical informatics professionals within our research team, it was acknowledged that certain objective requirements might not be articulated by users during interviews.…”

“…Among these barriers, comprehensive user requirements engineering should be performed at the very beginning of development, which should be continued iteratively throughout the CDSS design-development-implementation life cycle [ 25 , 26 , 28 , 29 ]. To address this gap, several recent studies have aimed at elucidating the clinical requirements for an effective and usable CDSS in the context of specific fields or scenarios [ 30 - 34 ] with a variety of methods, including focus groups [ 30 , 35 ], a workshop [ 34 ], expert discussion with a literature review [ 36 , 37 ], semistructured interviews [ 31 , 34 , 35 , 38 ], writing user stories [ 39 ], and system evaluation [ 40 ]. Overall, most studies have adopted a user-centered approach with qualitative analysis.…”