Browsing by Author "Karimi, Pegah"

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    Designing Conversational Assistants to Support Older Adults’ Personal Health Record Access
    (Springer, 2022) Karimi, Pegah; Ballard, Kallista; Vazirani, Pooja; Jorigay, Ravi Teja Narasimha; Martin-Hammond, Aqueasha; Human-Centered Computing, Luddy School of Informatics, Computing, and Engineering
    Older adults often rely on information provided during doctors’ visits or online to manage their health but can experience challenges accessing this information at home. Recently, conversational assistants are being explored to aid navigation of health information included in online portals, but we still know little about users’ perceptions of using these tools for managing personal health information. In this paper, we conducted a wizard-of-oz study to better understand older adults’ perceptions of a conversational assistant, MIHA, to help with navigating personal health information. Participants saw value in using a tool such as MIHA to help facilitate access to their personal health information and to help them become more engaged in their health. Participants believed MIHA’s features helped build confidence in the responses returned, but made suggestions for improving the interactions. We share insights of potential uses and design implications for conversational assistants that help older adults navigate personal health information.
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    Scribe: improving older adults' access to medical instructions from patient-physician conversations
    (ACM, 2021-04) Karimi, Pegah; Bora, Parishmita; Martin-Hammond, Aqueasha; Human-Centered Computing, School of Informatics and Computing
    During doctor's visits, the medical conversations shared often contain essential instructions and tailored advice necessary for daily care, particularly among older adults who manage chronic illnesses. However, some older adult patients face barriers and accessibility challenges that limits their access to shared information. Current research to improve access to instructions provided during patient-physician conversations focuses on methods such as one-to-one sessions that can be applied during the visit but are not available after the session ends. Electronic health portals provide access to some information after the visit, but many older patients find it challenging to navigate and access information through portals. To address the limitations of existing methods, this paper introduces a prototype called Scribe that provides older patients with access to the transcripts of medical conversations associated with multiple doctors' visits. Scribe automatically creates notes from doctors' visits that contain key information to assist individuals in navigating through information. In a study with 10 older adults, we examined Scribe's perceived usefulness for improving access to medical conversations. Findings suggest Scribe's potential to help older patients better access shared information from doctors' visits as well as support their relationships with caregivers. We contribute considerations for improving older adults future access to medical instructions using similar technologies.
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    Textflow: Screenless Access to Non-Visual Smart Messaging
    (ACM, 2021-04) Karimi, Pegah; Plebani, Emanuele; Bolchini, Davide; Human-Centered Computing, School of Informatics and Computing
    Texting relies on screen-centric prompts designed for sighted users, still posing significant barriers to people who are blind and visually impaired (BVI). Can we re-imagine texting untethered from a visual display? In an interview study, 20 BVI adults shared situations surrounding their texting practices, recurrent topics of conversations, and challenges. Informed by these insights, we introduce TextFlow: a mixed-initiative context-aware system that generates entirely auditory message options relevant to the users’ location, activity, and time of the day. Users can browse and select suggested aural messages using finger-taps supported by an off-the-shelf finger-worn device, without having to hold or attend to a mobile screen. In an evaluative study, 10 BVI participants successfully interacted with TextFlow to browse and send messages in screen-free mode. The experiential response of the users shed light on the importance of bypassing the phone and accessing rapidly controllable messages at their fingertips while preserving privacy and accuracy with respect to speech or screen-based input. We discuss how non-visual access to proactive, contextual messaging can support the blind in a variety of daily scenarios.
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    Understanding Barriers to Medical Instruction Access for Older Adults: Implications for AI-Assisted Tools
    (ACM, 2020-09) Karimi, Pegah; Martin-Hammond, Aqueasha; Human-Centered Computing, School of Informatics and Computing
    Recalling medical instructions provided during a doctor's visit can be difficult due to access barriers, primarily for older adults who visit doctors multiple times per year and rely on their memory to act on doctor's recommendations. There are several interventions that aid patients in recalling information after doctors' visits; however, some have been proven ineffective, and those that are effective can present additional challenges for older adults. In this paper, we explore the challenges that older adults with chronic illnesses face when collecting and recalling medical instructions from multiple doctors' visits and discuss implications for AI-assisted tools to enable older adults better access medical instructions. We interviewed 12 older adults to understand their strategies for gathering and recalling information, the challenges they face, and their opinions about automatic transcription of their conversations with doctors to help them recall information after a visit. We found that participants face accessibility challenges such as hearing information and recalling medical instructions that require additional time or follow-up with the doctor. Therefore, patients saw potential value for a tool that automatically transcribes and helps with recall of medical instructions, but desired additional features to summarize, categorize, and highlight critical information from the conversations with their doctors.