We were approached by a group of healthcare providers who are involved in the care of chronic patients looking for potential technologies to facilitate the process of reviewing patient-generated data during clinical visits.   Aiming at understanding the healthcare providers' attitudes towards reviewing patient-generated data, we (1) conducted a focus group with a mixed group of healthcare providers. Next, to gain the patients' perspectives, we (2) interviewed eight chronic patients, collected a sample of their data and designed a series of visualizations representing patient data we collected. Last, we (3) sought feedback on the visualization designs from healthcare providers who requested this exploration. We found four factors shaping patient-generated data: data & context, patient's motivation, patient's time commitment, and patient's support circle. Informed by the results of our studies, we discussed the importance of designing patient-generated visualizations for individuals by considering both patient and healthcare provider rather than designing with the purpose of generalization and provided guidelines for designing future patient-generated data visualizations. Collecting patient-generated data is becoming increasingly common in chronic disease management [20] . Patients use technological tracking tools to collect health and lifestyle data in disparate places [3] . Both healthcare providers and patients agree that this data could be used to make smarter decisions to improve patients' quality of life and to aid providers in making decisions about patient ongoing care [36, 44, 57] . There are already technological tools for tracking and visualizing health data such as sleep (e.g., [10] ), physical activity (e.g., [16] ), variations in weight (e.g., [37] ), and blood sugar level (e.g., [6] ). However, most of these tracking tools are not designed to fully meet patients and healthcare providers' expectations [11] and do not support reviewing patient-generated data with healthcare providers during clinical visits. One way to support patients in presenting their data with healthcare providers is to visualize the patient-generated data collections effectively. Yet, we lack an understanding of what type of visualization designs can support chronic patients to present and review their health data with healthcare providers during clinical visits. To answer this question, we explored patients' and healthcare providers' perspectives on presenting and reviewing patient data. To extract healthcare provider requirements when reviewing patientgenerated data during a clinical visit, we conducted a focus group with a mixed group of healthcare providers. To uncover patient stories and their approaches to tracking and presenting their health data, we interviewed eight patients with chronic conditions who actively track their health data. Our findings revealed four factors shaping patient-generated data: data items & data context collected by patients, time commitment invested by patients to track data, patients' motivation for collecting data, and patients' support circle. Considering these four factors, we designed various visualizations representing patient-generated data collections we gathered from our patients. Instead of pursuing a single generalized visualization design, we designed individually tailored visualizations for each patient. Based on our preliminary visualization designs, we proposed a design space of patient-generated data visualizations. Next, using these individually tailored visualization designs as elicitation artifacts, we interviewed the healthcare providers who had initiated the request for this project to reflect on the designs. Healthcare providers pointed to four use cases that they envision these visualizations could support their practice. As a whole, the results of all our studies led to one message: the importance of designing patient-generated data visualizations by considering each patient and healthcare provider rather than designing for generalization. However, it may seem impossible to either design a unique set of visualizations for each patient or expect patients to design their own visualizations. We, as healthcare technology designers, need to provide patients and providers with a set of visualization designs as starting points. This approach would let each patient and provider choose the visualization designs that work the best for them with the capacity to customize the designs based on their lifestyle, conditions, collected data, and patientprovider relationships. Our contributions in this paper are as follow: (1) We identified four factors shaping patient-generated data. (2) We presented a design space of visualizations representing patient-generated data collections. (3) We provided guidelines for designing future patient-generated data visualizations. Effective communication of patient-generated data during clinical visits can help patients feel understood and support healthcare providers get all the necessary data they need to make proper medical decisions [26, 29] . Our objective was to design visualizations to support patients present patient-generated data for reviewing during clinical visits. The focus of our studies was on studying patients with chronic conditions and the healthcare providers who visit chronic patients. The results of our patient interview studies revealed the individualities and the complexities of patient-generated data collections. Each patient has a unique body, a highly individualized lifestyle, a different set of goals, and a personalized patient-provider relationship. All these factors need to be considered while caring or designing for patients [19] . How can we design only one visualization solution that can consider all these differences in patients? Providers also differed in their principle goal of using patientgenerated data. This has major implications on the design of visualization. A solution that works for one provider may not work for another. This may affect the types of visualizations we consider for them and their patients. There are many driving forces for designing effective patient-generated data visualizations. It is still unclear which direction works best for both patients and providers. In software and technology design, research, and businesses, there is often the notion of designing with a generalization mindset, 'one-size-fits-all'. The idea of designing one software or one visualization tool that can address everyone's problem may be appealing and cost-efficient, but it does not always bring validation [5] . We echo the call from recent research [5] for the necessity to design for particulars, individuals. Looking into medical literature and the approaches taken in healthcare services for patient care planning, we often see one-toone interactions between a patient and their healthcare providers in clinical visits [23, 34] . This one-to-one interaction model has been practiced for centuries in medicine and is tailored depending on the individualities of each patient and their healthcare provider. Similarly, for designing visualizations to improve patient-provider communication, we, as visualization and technology designers, should take directions from the medical literature and their practices. We should take steps towards designing individualized tailored visualizations based on both patient and provider preferences to be able to accommodate as many patient-provider communications as possible [52] . Perhaps one solution can be to start designing and developing many patient-generated visualizations tailored based on both the healthcare provider and the patient preferences [8] . There have been attempts in the literature to design visualizations representing patient-generated data for some chronic conditions including, visualizing bipolar patient lived experience [45] , collaborative interactive storyboards design with chronic pediatric patients [24] , photo-based visualization to support patients with irritable bowel syndrome communicate with providers [15] . However, designing visualizations to support chronic patients with their self-collected data is indeterminant, or in other word a wicked problem [7] , meaning there are no definitive solutions or limits to this design problem. Our design study in this paper is a step towards tackling this wicked problem. We followed two criteria required for a rigor design study when addressing a wicked problem [31] : 1) A rigor solution to a wicked problem needs to include multiple perspectives to shape the design problem and consider a broad set of solutions [31] . Thus, in our study, we included the perspectives of both patients and healthcare providers. We explored the healthcare provider perspectives on reviewing patient-generated data during clinical visits and the details of eight patients' approaches to tracking and presenting their health data. Furthermore, we looked into a sample of our patient participants data collections to understand patients' methods of recording their data and their reasoning. 2) A design study is not and should not be reproducible; rather the solutions proposed are one of many possible solutions [31] . However, a rigor solution to a wicked problem needs to report the process of design and analysis in a transparent way [31] . We designed multiple alternative visualizations for each patient. All of our visualizations together shaped a design space of variant patient-generated data representations. We understand that depending on the patient needs, the providers' expectations, and the patient-provider relationship dynamics, a different set of visualization designs could be suitable. Our solutions are one of the many possible solutions to this wicked problem. Furthermore, we explained the process of design and reflection of our design in detail transparently. We hope that the detailed design process we provided supports other researchers and designers to further tackle this wicked problem and to design patient-generated data visualizations. In recent years, we have seen growing interests among patients with chronic conditions to track and analyze their data. However, sharing this data with healthcare providers can be challenging due to limited time in clinical visits and the large and complex nature of patient-generated data. We responded to a group of healthcare providers' call from a local hospital to design potential technological solutions to address the challenges of presenting, reviewing, and analyzing patient-generated data collections. We first gained healthcare providers' perspectives through a focus group. Then, we took an in-depth look at chronically ill patients' perspectives tracking their health data. The individual differences among these patients promoted a design space approach where we used insights from these patients to design a space of possible tailored visualizations. By exploring the possibilities of designing individual tailored visualizations representing patient-generated data, we have added one way that can support patients and healthcare providers when reviewing patient-generated data during clinical visits. We hope our proposed visualizations provide patients and healthcare providers better opportunities to present, review, and gain insights on patientgenerated data. We note that we included the perspectives of a small number of patients and healthcare providers; thus, other perspectives may not be included in our results. However, we envision this study as a stepping stone for the call to focus more on designing technologies in healthcare for individuals. We encourage the human-computer interaction, visualization, and healthcare communities to repeat these studies by including more patients and healthcare providers and explore designing tailored visualizations for each individual. Then, as a community, we can move towards accumulating these perspectives and designs to empower individuals with accessible design variations. We hope that in the long term, the results of this exploration contribute to supporting patients' and healthcare providers' in reviewing patient-generated data collections using visualizations during clinical visits.
We explored the visualization designs that can support chronic patients to present and review their health data with healthcare providers during clinical visits.
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