The Design of a Digital Scribe for Orthopedic Consultations

In the context of design education, understanding how to approach complex problems in healthcare settings, such as the development of a digital scribe for orthopedic consultations, is essential. Clinical documentation is a critical but often tedious part of healthcare, and with the rise of digital technologies, solutions like digital scribes are emerging to automate these tasks. This case study highlights the importance of a user-centered design approach, as well as the value of interdisciplinary collaboration between user experience (UX) designers and machine learning (ML) engineers, within the context of design education.

Orthopedic consultations, like many other healthcare interactions, involve a significant amount of documentation. Clinicians often spend a large portion of their time on administrative tasks, such as note-taking, which detracts from the time spent on direct patient care. This not only increases clinician burnout but also impacts the overall quality of care. The introduction of digital scribes aims to alleviate this burden by automating much of the documentation process, allowing clinicians to focus more on patient interactions.

In designing the digital scribe for orthopedic consultations, a user-centered design methodology was employed, aligned with established approaches for human-centric AI innovations in healthcare. This methodology followed several key stages: user research, conceptual design, and iterative testing. By integrating principles from interaction design and service design, the project focused on both the functionality of the digital scribe and the experience it would deliver to end users.

A critical aspect of the design process involved in-depth user research. This research was conducted through interviews and job shadowing, providing valuable insights into the daily challenges faced by clinicians during consultations. This qualitative data was used to create user personas and journey maps, which in turn informed the development of a comprehensive set of design requirements for the digital scribe. The goal was to ensure the tool was aligned with the needs, preferences, and workflows of its users.

Following the research phase, the conceptual design of the digital scribe focused on user flows, the creation of a service blueprint, and the design of the screen interface. The digital scribe was designed to automate a large portion of the clinicians’ note-taking tasks, while still allowing for flexibility in editing the automated documentation. Service blueprinting played a significant role in visualizing the service process, showing the interactions between the clinician, the AI system, and other touchpoints within the workflow. This technique provided a structured framework for addressing potential design challenges and highlighted where AI could be most effectively integrated into the process.

One of the key lessons from this project is the importance of collaboration between UX designers and machine learning engineers. Designing a tool like the digital scribe requires balancing user needs with the capabilities of AI technology. Service blueprinting facilitated this collaboration by offering a common language and a visual tool that both designers and engineers could use to better understand each other's challenges and perspectives. This collaboration ensured that the final design was both practical and innovative, meeting the functional needs of clinicians while also incorporating advanced AI capabilities.

The design of a digital scribe for orthopedic consultations is a prime example of how a user-centered approach can enhance the development of AI-enabled tools in healthcare. Through service blueprinting and close collaboration between UX designers and machine learning engineers, a digital scribe can be created that addresses the unique needs of clinicians, reduces administrative burdens, and ultimately improves patient care. For design education, this case underscores the importance of interdisciplinary learning and the integration of human-centric design principles when working with emerging technologies like AI.