Supporting Patient-Provider Collaboration to Identify Individual Triggers using Food and Symptom Journals

Schroeder, Jessica; Hoffswell, Jane; Chung, Chia-Fang; Fogarty, James; Munson, Sean A.; Zia, Jasmine

doi:10.1145/2998181.2998276

Cited by 90 publications

(73 citation statements)

References 51 publications

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“…Patients and clinicians may also approach the analysis of data in different ways [32]. However, successful collaboration can be very rewarding: with the proper tools, discussing tracked data with clinician experts can increase patients’ understanding of their health data, lead to more productive reflection, improve the clinician-patient relationship, and ultimately increase patients’ ability to self-manage [34,44,48]. Clinicians and patients can collaborate over data to jointly interpret it in and engage in shared decision-making [37].…”

Section: Related Workmentioning

confidence: 99%

“…Chung et al suggest that tracking tools be designed to more flexibly support collaboration and provide better visualizations [8], while West et al suggest that tracking tools make it easier for clinicians to reason about the data they collect [55]. Schroeder et al suggest that tools support exploration through flexibility and simplicity, and emphasize the generation of actionable insights [48]. …”

Section: Related Workmentioning

confidence: 99%

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Supporting Collaborative Health Tracking in the Hospital

Mishra

Miller

Haldar

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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The hospital setting creates a high-stakes environment where patients’ lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients’ engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients’ tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients’ needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Supporting Collaborative Health Tracking in the Hospital

Mishra

Miller

Haldar

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…Moreover, advances in information technology enable people to use various technological tools for tracking their condition, but limited evidence supports the efficacy of using these newer tools to accomplish successful management [20]. Barriers to successful self-tracking identified in prior research include: (1) insufficient support for patient-or tracker-provider collaboration [21], (2) lapses in use of tracking tools due to barriers in making sense of accumulated data [22], and (3) difficulty in remembering to track or deciding what to track [23].…”

Section: Self-trackingmentioning

confidence: 99%

“…For an in-depth discussion of coordinated reflection using one tool, in one disease context, see Schroeder et al [21] for a description of person and healthcare provider collaboration in the context of Irritable Bowel Syndrome.…”

Section: The Pharmacist Got Involved In My Cholesterol Medication Shmentioning

confidence: 99%

“It’s Not Just Technology, It’s People”: Constructing a Conceptual Model of Shared Health Informatics for Tracking in Chronic Illness Management (Preprint)

Vizer¹,

Eschler²,

Koo³

et al. 2018

Preprint

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Background: Chronic illness is a substantial public health burden and the day-to-day work of a person with chronic illness is central to illness management. However, although management programs are available to train people to manage their chronic illnesses, those programs rarely provide people with sufficient guidance on implementing fine-grained management strategies. This lack of guidance transfers much of the burden of day-to-day management practices to people with chronic illness. Objective: To decrease the management burden many people have created technological solutions, but we claim that to be maximally effective and tailored to people's needs, those solutions need to be informed by a model that integrate patient work and self-management frameworks with personal health informatics models. We see an opportunity to improve personal health informatics models to (1) incorporate the ongoing nature of management work, and (2) append a social dimension to more accurately depict the context of illness management. Methods:We used qualitative methods to analyze the chronic illness management practices of 63 people through the lenses of self-management and informatics frameworks. Results: Analysis results are distilled into a new Conceptual Model of Shared Health Informatics. We describe the people and work involved in chronic illness management and contribute a Conceptual Model of Shared Health Informatics depicting the process of chronic illness management in a social context. Conclusions: Through insight into management work provided by our Conceptual Model of Shared Health Informatics, technology designers and implementers can improve the quality of chronic illness management tools to ensure confident and capable management of chronic illness.

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“…DOI: https://doi.org/10.1145/3173574.3173743 Previous research on pain logging has focused on the use of diaries that allow patients to engage in self-scoring of their experienced pain. Such diaries are intended to be used away from the clinic, as a journal which can later support discussions between patients and clinicians [24,34]. However, in the context of monitoring patient pain during hospital stays, the standard clinical practice is for nurses to collect subjective pain data from patients [7,8,11].…”

Section: Introductionmentioning

confidence: 99%

Feel My Pain

Price

Kelly

Mehta

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Monitoring patients' pain is a critical issue for clinical caregivers, particularly among staff responsible for providing analgesic relief. However, collecting regularly scheduled pain readings from patients can be difficult and time-consuming for clinicians. In this paper we present Painpad, a tangible device that was developed to allow patients to engage in self-logging of their pain. We report findings from two hospital-based field studies in which Painpad was deployed to a total of 78 inpatients recovering from ambulatory surgery. We find that Painpad results in improved frequency and compliance with pain logging, and that self-logged scores may be more faithful to patients' experienced pain than corresponding scores reported to nurses. We also show that older adults may prefer tangible interfaces over tablet-based alternatives for reporting their pain, and we contribute design lessons for pain logging devices intended for use in hospital settings.

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Supporting Patient-Provider Collaboration to Identify Individual Triggers using Food and Symptom Journals

Cited by 90 publications

References 51 publications

Supporting Collaborative Health Tracking in the Hospital

Supporting Collaborative Health Tracking in the Hospital

“It’s Not Just Technology, It’s People”: Constructing a Conceptual Model of Shared Health Informatics for Tracking in Chronic Illness Management (Preprint)

Feel My Pain

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