A minimal set of prompts to surface reasoning in digital health

Digital health technologies can reduce workloads, enhance information retention, and minimize errors. However, when poorly integrated or misaligned with clinical practice, they often introduce new risks and allow unprocessed information to accumulate, amplifying existing challenges. The core problem is that reasoning often stays implicit and unstructured when translating needs into digital solutions. Without a shared way to make it explicit, critical inputs are missed, unprocessed information builds up, and reasoning faults spread through the system. We present a minimal, non-redundant set of eight plain-language prompts that spans the full reasoning process, guards against common reasoning faults, and provides a shared structure for aligning perspectives across stakeholders. We tested the set in a clinical case study on the initial management of septic shock, where the prompts helped to turn unstructured clinical reasoning into clear inputs for system design and workflow adjustments. To interpret its impact, we built a mathematical model that captures how unprocessed information changes over time. Compared with standard practice, the prompts reduced peak backlog by 20.3 %, cumulative backlog by 22.2 %, and improved recovery time from not reached to five time steps. We share an open-source code to model backlog dynamics, enabling easy adaptation to other clinical settings through parameter calibration.