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Teens with complex chronic illnesses have difficulty understanding and articulating symptoms such as pain and emotional distress. Yet, symptom communication plays a central role in clinical care and illness management. To understand how design can help overcome these challenges, we created a visual library of 72 sketched illustrations, informed by the Observations of Daily Living framework along with insights from 11 clinician interviews. We utilized our library with storyboarding techniques, free-form sketching, and interviews, in co-design sessions with 13 pairs of chronically-ill teens and their parents. We found that teens depicted symptoms as being interwoven with narratives of personal and social identity. Teens and parents were enthusiastic about collaboratively-generated, interactive storyboards as a tracking and communication mechanism, and suggested three ways in which they could aid in communication and coordination with informal and formal caregivers. In this paper, we detail these findings, to guide the design of tools for symptom-tracking and incorporation of patient-generated data into pediatric care.
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Integrating Patient-Generated Observations of Daily Living into Pediatric Cancer Care: A Formative User Interface Design Study
Patient-generated data, such as recorded Observations of Daily Living (ODL) and Patient-Reported Outcomes (PRO) data, are valued sources of information in oncology care. However, prior work largely focuses on capturing clinician-defined, patient-generated data in adult oncology care. Emerging research at the intersection of human–computer interaction and medical informatics suggests that visual narratives of patients’ observations of daily living (Visual ODLs) could better support multi-party review of patients’ everyday symptoms and quality of life, potentially improving patient–clinician communication. In this paper, we build on a prior study of Visual ODLs by describing a formative, two-phase study with 15 pediatric oncology clinicians. In Phase I, we analyzed data from ethnographic interviews in a pediatric oncology setting to capture the needs of nurses, nurse practitioners, and oncologists. In Phase II, we constructed two low-fidelity dashboard display prototypes, populated with Visual ODLs contributed by actual adolescent oncology patients, and we subsequently interviewed pediatric oncology clinicians who reviewed each dashboard design. Findings from our study contribute four key design objectives for presenting interactive Visual ODL dashboards in pediatric oncology, along with three use cases for using these dashboards for symptom tracking and communication.
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- Award ID(s):
- 1652302
- PAR ID:
- 10084798
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Healthcare Informatics
- Page Range / eLocation ID:
- 265 to 275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Patient-generated data (PGD) show great promise for informing the delivery of personalized and patient-centered care. However, patients' data tracking does not automatically lead to data sharing and discussion with clinicians, which can make it difficult to utilize and derive optimal benefit from PGD. In this paper, we investigate whether and how patients share their PGD with clinicians and the types of challenges that arise within this context. We describe patients' immediate experiences of PGD sharing with clinicians, based on our short onsite interviews with 57 patients who had just met with a clinician at a university health center. Our analyses identified overarching patterns in patients' PGD sharing practices and the associated challenges that arise from the information asymmetry between patients and clinicians and from patients' reliance on their memory to share their PGD. We discuss the implications of our findings for designing PGD-integrated health IT systems in ways to support patients' tracking of relevant PGD, clinicians' effective engagement with patients around PGD, and the efficient sharing and review of PGD within clinical settings.more » « less
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Risk information is increasingly available to health care providers and patients thanks to a growing body of health outcomes research and clinical prediction models. Meanwhile, communicating such information is encouraged for a variety of reasons. Yet clinicians often struggle to communicate risk information—or forego the task altogether due to various challenges. The challenges are real, and this paper briefly discusses six of them: (1) Clinician reliance on verbal risk descriptions, (2) Low patient numeracy; (3) Lack of meaningful numeric evidence; (4) Patient use of heuristics; (5) Uncertain risk information; and (6) The curse of knowledge. Specific strategies exist for clinicians, though, to overcome these complex challenges. In the paper, we present evidence-based best practices with examples of what clinicians can do to effectively communicate risk information to their patients (and what they should not do). The best practices include communicating with numbers, not only words; decreasing cognitive effort for patients; providing the meaning of numeric risk data important to decisions; acknowledging uncertainty; and testing communication with patients through teach-back techniques. We conclude by recommending that clinicians integrate these strategies into their existing scripts for patient encounters.more » « less
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Abstract BackgroundCancer-care complexity heightens communication challenges between health care providers and patients, impacting their treatment adherence. This is especially evident upon hospital discharge in patients undergoing surgical procedures. Digital health tools offer potential solutions to address communication challenges seen in current discharge protocols. We aim to explore the usability and acceptability of an interactive health platform among discharged patients who underwent oncology-related procedures. MethodsA 4-week exploratory cohort study was conducted. Following hospital discharge, a tablet equipped with an integrated Personal Virtual Assistant (PVA) system was provided to patients who underwent oncology-related procedures. The PVA encompasses automated features that provide personalized care plans, developed through collaboration among clinicians, researchers, and engineers from various disciplines. These plans include guidance on daily specific assignments that were divided into 4 categories: medication intake, exercise,symptom surveys, and postprocedural specific tasks. The aim was to explore the acceptability of the PVA by quantification of dropout rate and assessing adherence to each care plan category throughout the study duration. The secondary aim assessed acceptability of the PVA through a technology acceptance model (TAM) questionnaire that examined ease of use, usefulness, attitude toward use, and privacy concerns. ResultsIn total, 17 patients were enrolled. However, 1 (5.8%) patient dropped out from the study after 3 days due to health deterioration, leaving 16/17 (94.2%) completing the study (mean age 54.5, SD 12.7, years; n=9, 52% Caucasian; n=14, 82% with a gynecological disease; n=3, 18% with a hepatobiliary disease). At the study end point, adherence to care plan categories were 78% (SD 25%) for medications, 81% (SD 24%) for exercises, 61% (SD 30%) for surveys, and 58% (SD 44%) for specific tasks such as following step-by step wound care instructions, managing drains, administering injectable medications independently, and performing pelvic baths as instructed. There was an 80% patient endorsement (strongly agree or agree) across all TAM categories. ConclusionThis study suggests the potential acceptability of the PVA among patients discharged after oncology-related procedures, with a dropout rate of less than 6% and fair-to-good adherence to tasks such as medication intake and exercise. However, these findings are preliminary due to the small sample size and highlight the need for further research with larger cohorts to validate and refine the system.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICHI.2018.00037
