Collaborative care management is an evidence-based approach to integrated psychosocial care for patients with comorbid cancer and depression. Prior work highlights challenges in patient-provider collaboration in navigating parallel cancer care and psychosocial care journeys of these patients. We design and deploy SCOPE , a platform for technology-enhanced collaborative care combining a patient-facing mobile app with a provider-facing registry. We examine SCOPE through a total of 45 interviews with patients and providers conducted in SCOPE 's 15 months of design and development and 24 Months of SCOPE 's deployment for actual care in 6 cancer clinics. We find that: (1) SCOPE supported patient engagement in its underlying collaborative care and behavioral activation interventions, (2) patient-generated data in SCOPE improved patient-provider collaboration between and within in-person sessions, (3) SCOPE supported providers in delivering care and improved care team collaboration, (4) experience with SCOPE created evolving expectations for collaboration around data, and (5) SCOPE 's deployment in actual care surfaced important implementation barriers. We discuss the implications of our findings in terms of designing for engagement with behavioral health interventions, negotiating patient data sharing and provider responsiveness, supporting personalized self-tracking goals in evidence-based interventions, exploring the role of digital health navigators in technology-enhanced care, and the need for flexibility in aligning technology-supported interventions to patient needs.

Over the past decade, businesses have migrated to the cloud for its simplicity, elasticity, and resilience. Cloud ecosystems offer a variety of computing and storage options, enabling customers to choose configurations that maximize productivity. However, determining the right configuration to minimize cost while maximizing performance is challenging, as workloads vary and cloud offerings constantly evolve. Many businesses are overwhelmed with choice overload and often end up making suboptimal choices that lead to inflated cloud spending and/or poor performance.In this paper, we describe INFA-FinOps, an automated system that helps Informatica customers strike a balance between cost efficiency and meeting SLAs for Informatica Advanced Data Integration (aka CDI-E) workloads. We first describe common workload patterns observed in CDI-E customers and show how INFA-FinOps selects optimal cloud resources and configurations for each workload, adjusting them as workloads and cloud ecosystems change. It also makes recommendations for actions that require user review or input. Finally, we present performance benchmarks on various enterprise use cases and conclude with lessons learned and potential future enhancements.

Self-tracking and personal informatics offer important potential in chronic condition management, but such potential is often undermined by difficulty in aligning self-tracking tools to an individual's goals. Informed by prior proposals of goal-directed tracking, we designed and developed MigraineTracker, a prototype app that emphasizes explicit expression of goals for migraine-related self-tracking. We then examined migraine patient experiences in a deployment study for an average of 12+ months, including a total of 50 interview sessions with 10 patients working with 3 different clinicians. Patients were able to express multiple types of goals, evolve their goals over time, align tracking to their goals, personalize their tracking, reflect in the context of their goals, and gain insights that enabled understanding, communication, and action. We discuss how these results highlight the importance of accounting for distinct and concurrent goals in personal informatics together with implications for the design of future goal-directed personal informatics tools.

Videoconferencing usage has surged in recent years, but current platforms present significant accessibility barriers for the 430 million d/Deaf or hard of hearing people worldwide. Informed by prior work examining accessibility barriers in current videoconferencing platforms, we designed and developed Jod, a videoconferencing platform to facilitate communication in mixed hearing groups. Key features include support for customizing visual layouts and a notification system to request attention and influence behavior. Using Jod, we conducted six mixed hearing group sessions with 34 participants, including 18 d/Deaf or hard of hearing participants, 10 hearing participants, and 6 sign language interpreters. We found participants engaged in visual layout rearrangements based on their hearing ability and dynamically adapted to the changing group communication context, and that notifications were useful but raised a need for designs to cause fewer interruptions. We provide insights for future videoconferencing designs and conclude with recommendations for conducting mixed hearing studies.

We live in the gilded age of data-driven computing. With public clouds offering virtually unlimited amounts of compute and storage, enterprises collecting data about every aspect of their businesses, and advances in analytics and machine learning technologies, data driven decision making is now timely, cost-effective, and therefore, pervasive. Alas, only a handful of power users can wield today's powerful data engineering tools. For one thing, most solutions require knowledge of specific programming interfaces or libraries. Furthermore, running them requires complex configurations and knowledge of the underlying cloud for cost-effectiveness. We decided that a fundamental redesign is in order to democratize data engineering for the masses at cloud scale. The result is Informatica Cloud Data Integration - Elastic (CDI-E). Since the early 1990s, Informatica has been a pioneer and industry leader in building no-code data engineering tools. Non-experts can express complex data engineering tasks using a graphical user interface (GUI). Informatica CDI-E is built to incorporate the simplicity of GUI in the design layer with an elastic and highly scalable run time to handle data in any format without little to no user input using automated optimizations. Users upload their data to the cloud in any format and can immediately use them in conjunction with their data management and analytic tools of choice using CDI-E GUI. Implementation began in the Spring of 2017, and Informatica CDI-E has been generally available since the Summer of 2019. Today, CDI-E is used in production by a growing number of small and large enterprises to make sense of data in arbitrary formats. In this paper, we describe the architecture of Informatica CDI-E and its novel no-code data engineering interface. The paper highlights some of the key features of CDI-E: simplicity without loss in productivity and extreme elasticity. It concludes with lessons we learned and an outlook of the future.

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevitygap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensembleaveraged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stablefamilies within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work.

Effective collaboration in data science can leverage domain expertise from each team member and thus improve the quality and efficiency of the work. Computational notebooks give data scientists a convenient interactive solution for sharing and keeping track of the data exploration process through a combination of code, narrative text, visualizations, and other rich media. In this paper, we report how synchronous editing in computational notebooks changes the way data scientists work together compared to working on individual notebooks. We first conducted a formative survey with 195 data scientists to understand their past experience with collaboration in the context of data science. Next, we carried out an observational study of 24 data scientists working in pairs remotely to solve a typical data science predictive modeling problem, working on either notebooks supported by synchronous groupware or individual notebooks in a collaborative setting. The study showed that working on the synchronous notebooks improves collaboration by creating a shared context, encouraging more exploration, and reducing communication costs. However, the current synchronous editing features may lead to unbalanced participation and activity interference without strategic coordination. The synchronous notebooks may also amplify the tension between quick exploration and clear explanations. Building on these findings, we propose several design implications aimed at better supporting collaborative editing in computational notebooks, and thus improving efficiency in teamwork among data scientists.