We explored the potential of Large Language Models (LLMs) to facilitate laypersons' selection of treatment goals within a complex medical decision-making context.Using ChatGPT-4o, we developed an LLM-enhanced tool to guide users through goal elicitation, clarification, and revision.Our findings demonstrate that LLM features can effectively support these key aspects of decision-making.However, the absence of human interaction, the lack of patientand context-specific treatment information, and the risk of information overload due to unconstrained access to LLM-generated content present significant risks.To balance the benefits and risks, we propose that LLM-enhanced facilitation tools for asynchronous, independent use should be clinician-initiated, constrain broad information search, and focus on creating a safe space for the exploration of laypersons' preferences and goals regarding the difficult challenges in balancing treatment and tradeoffs for quality of life.

In serious illness contexts, caregivers tasked with making decisions for incapacitated patients face unique challenges. Current tools for values elicitation, primarily patient-oriented, may not fully meet caregiver needs. This study, involving 12 caregivers, adopted a research-through-design approach to evaluate caregiver-facing values elicitation tools. The findings reveal caregivers’ need for more support in understanding their own values, discerning patient values, and reconciling these perspectives. While caregivers seek tools for consensus building within the family, they prefer using them individually rather than in a shared manner. The study also highlights a general unawareness of the importance of values in decision-making. This lack of understanding points to the need for tools that educate as well as aid in decision-making. Finally, tool designers must balance top-down directives with bottom-up values in their designs. This research informs the development of more effective caregiver-facing values elicitation tools.

Digital contact tracing can limit the spread of infectious diseases. Nevertheless, barriers remain to attain sufficient adoption. In this study, we investigate how willingness to participate in contact tracing is affected by two critical factors: the modes of data collection and the type of data collected. We conducted a scenario-based survey study among 220 respondents in the United States (U.S.) to understand their perceptions about contact tracing associated with automated and manual contact tracing methods. The findings indicate a promising use of smartphones and a combination of public health officials and medical health records as information sources. Through a quantitative analysis, we describe how different modalities and individual demographic factors may affect user compliance when participants are asked to provide four key information pieces for contact tracing.

Previous work has looked closely at the challenges of using patient-generated data to enable remote assessment and monitoring by healthcare professionals. In this paper, we examine family caregivers who act as proxies for patients who may not have the capacity of capturing the necessary data. We worked with occupational therapists to develop an application for remote assessment of the safety of patients' homes by occupational therapists with the assistance of family caregivers. We evaluated the application with family caregivers and found two features unique to communication between family caregivers and healthcare professionals: Caregivers want to be able to direct healthcare professionals' attention to support problem-solving at home, and they include their perspective on how to best meet the patient's health needs. We discuss the importance of these findings for home systems in the domain of long-term chronic care.

Low patient motivation and compliance has been reported as a main barrier to effective rehabilitation. This study aims to provide an engaging and intuitive tool to increase the motivation of elderly stroke survivors in rehabilitation program. We present a tangible gaming board for the training of upper limb gross motor and fine motor skills. 64 RGB LED rotary encoders are evenly distributed on the board into eight rows. A modular design allows for training of various hand functions with interchangeable sizes of knobs attached on the rotary encoders. Preliminary usability testing of the game "Lights Invader" with eight elderly stroke survivors suggested that the gaming board has a high acceptance rate among all the participants, the game interface is easy to understand and the game is fun to play with. User feedback on the features that need to be improved are discussed in this paper.

Healthcare systems around the world are facing the challenge of information overload in caring for patients in an affordable, safe and high-quality manner in a system with limited healthcare resources and increasing costs. To alleviate this problem, we develop an integrative healthcare analytics system called GEMINI which allows point of care analytics for doctors where real-time usable and relevant information of their patients are required through the questions they asked about the patients they are caring for. GEMINI extracts data of each patient from various data sources and stores them as information in a patient profile graph. The data sources are complex and varied consisting of both structured data (such as, patients' demographic data, laboratory results and medications) and unstructured data (such as, doctors' notes). Hence, the patient profile graph provides a holistic and comprehensive information of patients' healthcare profile, from which GEMINI can infer implicit information useful for administrative and clinical purposes, and extract relevant information for performing predictive analytics. At the core, GEMINI keeps interacting with the healthcare professionals as part of a feedback loop to gather, infer, ascertain and enhance the self-learning knowledge base. We present a case study on using GEMINI to predict the risk of unplanned patient readmissions.

The range of motion (ROM) in stroke patients is often severely affected. Poststroke rehabilitation is guided through the use of clinical assessment scales for the rROM. Unfortunately, these scales are not widely utilized in clinical practice as they are excessively time-consuming. Although commercial motion-capture systems are capable of providing the information required for the assessments, most systems are either too costly or lack the convenience required for assessments to be conducted on a daily basis. This paper presents the design and implementation of a smartphone-based system for automated motor assessment using low-cost off-the-shelf inertial sensors. The system was used to automate a portion of the upper-extremity Fugl-Meyer assessment (FMA), which is widely used to quantify motor deficits in stroke survivors. Twelve out of 33 items were selected, focusing mainly on joint angle measurements of the upper body. The system has the ability to automatically identify the assessment item being conducted, and calculate the maximum respective joint angle achieved. Preliminary results show the ability of this system to achieve comparable results to goniometer measurements, while significantly reducing the time required to conduct the assessments. The portability and ease-of-use of the system would simplify the task of conducting range-of-motion assessments.