Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians’ in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients’ responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.
https://dl.acm.org/doi/abs/10.1145/3491102.3501989
Touchless input could transform clinical activity by allowing health professionals direct control over medical imaging systems in a sterile manner. Currently, users face the issues of being unable to directly manipulate imaging in aseptic environments, as well as needing to touch shared surfaces in other hospital areas. Unintended input is a key challenge for touchless interaction and could be especially disruptive in medical contexts. We evaluated four clutching techniques with 34 health professionals, measuring interaction performance and interviewing them to obtain insight into their views on clutching, and touchless control of medical imaging. As well as exploring the performance of the different clutching techniques, our analysis revealed an appetite for reliable touchless interfaces, a strong desire to reduce shared surface contact, and suggested potential improvements such as combined authentication and touchless control. Our findings can inform the development of novel touchless medical systems and identify challenges for future research.
https://dl.acm.org/doi/abs/10.1145/3491102.3517512
Clinical decision support tools have typically focused on one-time support for diagnosis or prognosis, but have the ability to support providers in longitudinal planning of patient care regimens amidst infrastructural challenges. We explore an opportunity for technology support for discontinuing antidepressants, where clinical guidelines increasingly recommend gradual discontinuation over abruptly stopping to avoid withdrawal symptoms, but providers have varying levels of experience and diverse strategies for supporting patients through discontinuation. We conducted two studies with 12 providers, identifying providers’ needs in developing discontinuation plans and deriving design guidelines. We then iteratively designed and implemented AT Planner, instantiating the guidelines by projecting taper schedules and providing flexibility for adjustment. Provider feedback on AT Planner highlighted that discontinuation plans required balancing interpersonal and infrastructural constraints and surfaced the need for different technological support based on clinical experience. We discuss the benefits and challenges of incorporating flexibility and advice into clinical planning tools.
https://dl.acm.org/doi/abs/10.1145/3491102.3502206
Forgetfulness is a primary factor of medication nonadherence, a problem that contributes to worse health outcomes and increased mortality among people with chronic conditions. Common strategies to address forgetfulness, such as timed reminders, have limited effectiveness. However, there is limited information about why these strategies fail. To address this gap, we conducted interviews with people who take medications daily and miss doses at least twice a month. We contribute a state-based Medication Routine Framework composed of four states (Wellness, New Task, Erratic, and Disruption) in two axes (regularity and time scale). Because most nonadherence due to forgetfulness occurs in nonroutine states (i.e., Erratic and Disruption state), we argue that improving technology for medication adherence requires designing for these states. In this paper, we describe each state in detail and discuss opportunities for adapting medication reminder strategies to overcome the challenges of nonroutine states.
https://dl.acm.org/doi/abs/10.1145/3491102.3517519