Online health communities collect vast amounts of information and opinions in regards to health and wellness management. However, these opinions are usually stored within lengthy and loosely structured discussion threads; synthesizing information in these threads can be challenging. In this mixed-methods study, grounded in the theoretical perspective of collective sensemaking, we examined patterns of communication within an online diabetes community TuDiabetes. The results of the study suggest that members of TuDiabetes often construct shared meaning through deep discussions, back and forth negotiation of perspectives, and resolution of conflicts in opinions. However, unlike participants of other sensemaking communities, members of TuDiabetes often value multiplicity of opinions rather than consensus. We use study results to draw implications for the design of computing platforms for facilitating collective sensemaking that promote construction of shared knowledge yet embrace diversity of opinions.
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Background In the United States, patients can access their electronic health record (EHR) data through patient portals. However, current patient portals are mainly focused on a single provider, with very limited data sharing capabilities and put low emphasis on independent sensemaking of the EHR data. This makes it very challenging for patients to switch between different portals and aggregate the data to obtain a complete picture of their medical history and to make sense of it. Owing to this fragmentation, patients are exposed to numerous inconveniences such as medical errors, repeated tests, and limited self-advocacy. Objective To overcome the limitations of EHR patient portals, we designed and developed Discovery—a web-based application that aggregates EHR data from multiple providers and present them to the patient for efficient exploration and sensemaking. To learn how well Discovery meets the patients’ sensemaking needs and what features should such applications include, we conducted an evaluation study. Methods We conducted a remote study with 14 participants. In a 60-minute session and relying on the think-aloud protocol, participants were asked to complete a variety of sensemaking tasks and provide feedback upon completion. The audio materials were transcribed for analysis and the video recordings of the users’ interactions with Discovery were annotated to provide additional context. These combined textual data were thematically analyzed to surface themes that reflect how participants used Discovery’s features, what sensemaking of their EHR data really entails, and what features are desirable to support that process better. Results We found that Discovery provided much needed features and could be used in a variety of everyday scenarios, especially for preparing and during clinical visits and also for raising awareness, reflection, and planning. According to the study participants, Discovery provided a robust set of features for supporting independent exploration and sensemaking of their EHR data: summary and quick overview of the data, finding prevalence, periodicity, co-occurrence, and pre-post of medical events, as well as comparing medical record types and subtypes across providers. In addition, we extracted important design implications from the user feedback on data exploration with multiple views and nonstandard user interface elements. Conclusions Patient-centered sensemaking tools should have a core set of features that can be learned quickly and support common use cases for a variety of users. The patients should be able to detect time-oriented patterns of medical events and get enough context and explanation on demand in a single exploration view that feels warm and familiar and relies on patient-friendly language. However, this view should have enough plasticity to adjust to the patient’s information needs as the sensemaking unfolds. Future designs should include the physicians in the patient’s sensemaking process and improve the communication in clinical visits and via messaging.
Background There is no consensus on which risks to communicate to a prospective surgical patient during informed consent or how. Complicating the process, patient preferences may diverge from clinical assumptions and are often not considered for discussion. Such discrepancies can lead to confusion and resentment, raising the potential for legal action. To overcome these issues, we propose a visual consent tool that incorporates patient preferences and communicates personalized risks to patients using data visualization. We used this platform to identify key effective visual elements to communicate personalized surgical risks. Objective Our main focus is to understand how to best communicate personalized risks using data visualization. To contextualize patient responses to the main question, we examine how patients perceive risks before surgery (research question 1), how suitably the visual consent tool is able to present personalized surgical risks (research question 2), how well our visualizations convey those personalized surgical risks (research question 3), and how the visual consent tool could improve the informed consent process and how it can be used (research question 4). Methods We designed a visual consent tool to meet the objectives of our study. To calculate and list personalized surgical risks, we used the American College of Surgeons risk calculator. We created multiple visualization mock-ups using visual elements previously determined to be well-received for risk communication. Semistructured interviews were conducted with patients after surgery, and each of the mock-ups was presented and evaluated independently and in the context of our visual consent tool design. The interviews were transcribed, and thematic analysis was performed to identify major themes. We also applied a quantitative approach to the analysis to assess the prevalence of different perceptions of the visualizations presented in our tool. Results In total, 20 patients were interviewed, with a median age of 59 (range 29-87) years. Thematic analysis revealed factors that influenced the perception of risk (the surgical procedure, the cognitive capacity of the patient, and the timing of consent; research question 1); factors that influenced the perceived value of risk visualizations (preference for rare event communication, preference for risk visualization, and usefulness of comparison with the average; research question 3); and perceived usefulness and use cases of the visual consent tool (research questions 2 and 4). Most importantly, we found that patients preferred the visual consent tool to current text-based documents and had no unified preferences for risk visualization. Furthermore, our findings suggest that patient concerns were not often represented in existing risk calculators. Conclusions We identified key elements that influence effective visual risk communication in the perioperative setting and pointed out the limitations of the existing calculators in addressing patient concerns. Patient preference is highly variable and should influence choices regarding risk presentation and visualization.
Background Electronic health record (EHR) data from multiple providers often exhibit important but convoluted and complex patterns that patients find hard and time-consuming to identify and interpret. However, existing patient-facing applications lack the capability to incorporate automatic pattern detection robustly and toward supporting making sense of the patient’s EHR data. In addition, there is no means to organize EHR data in an efficient way that suits the patient’s needs and makes them more actionable in real-life settings. These shortcomings often result in a skewed and incomplete picture of the patient’s health status, which may lead to suboptimal decision-making and actions that put the patient at risk. Objective Our main goal was to investigate patients’ attitudes, needs, and use scenarios with respect to automatic support for surfacing important patterns in their EHR data and providing means for organizing them that best suit patients’ needs. Methods We conducted an inquisitive research-through-design study with 14 participants. Presented in the context of a cutting-edge application with strong emphasis on independent EHR data sensemaking, called Discovery, we used high-level mock-ups for the new features that were supposed to support automatic identification of important data patterns and offer recommendations—Alerts—and means for organizing the medical records based on patients’ needs, much like photos in albums—Collections. The combined audio recording transcripts and in-study notes were analyzed using the reflexive thematic analysis approach. Results The Alerts and Collections can be used for raising awareness, reflection, planning, and especially evidence-based patient-provider communication. Moreover, patients desired carefully designed automatic pattern detection with safe and actionable recommendations, which produced a well-tailored and scoped landscape of alerts for both potential threats and positive progress. Furthermore, patients wanted to contribute their own data (eg, progress notes) and log feelings, daily observations, and measurements to enrich the meaning and enable easier sensemaking of the alerts and collections. On the basis of the findings, we renamed Alerts to Reports for a more neutral tone and offered design implications for contextualizing the reports more deeply for increased actionability; automatically generating the collections for more expedited and exhaustive organization of the EHR data; enabling patient-generated data input in various formats to support coarser organization, richer pattern detection, and learning from experience; and using the reports and collections for efficient, reliable, and common-ground patient-provider communication. Conclusions Patients need to have a flexible and rich way to organize and annotate their EHR data; be introduced to insights from these data—both positive and negative; and share these artifacts with their physicians in clinical visits or via messaging for establishing shared mental models for clear goals, agreed-upon priorities, and feasible actions.
A qualitative user-centered design study on automating the patients' sensemaking process by surfacing interesting patterns in their EHR data from multiple providers (Alerts) and enabling organization and annotation of those data that best suits patients' information needs (Collections).
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