Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives

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

Interacting With Intelligent Assistants to Predict Consumer Satisfaction

With the rise in popularity of intelligent assistants, there is an increasing need to understand and evaluate both strengths and shortcomings of the technology, in order to define specific areas for improvement and to understand where these interfaces are ideally suited. We describe the current state of personal digital assistants and evaluate performance by testing voice activated queries in four distinct categories including Translation, Current/Real Time Events, How to questions and General Knowledge. Experiments show that Microsoft\u27s Cortana beat the two competitors with an impeccable accuracy of 100% followed by Amazon\u27s Alexa with an average accuracy of 74% and Apple\u27s Siri with only 49.8% accuracy. Siri was fastest to respond on the few questions it correctly answered, with an average speed of 2.09 seconds followed by Cortana with an average speed of 2.35 seconds and Alexa at the average speed of 2.63 seconds. Cortana had the highest accuracy and overall effectiveness. Analysis of these three assistants illustrates the current ability of intelligent assistants to aid consumers. This work also demonstrates tremendous potential of voice activated interfaces in the future. Evaluating which category each assistant performed best (or worst) can be a strong predictor of user satisfaction; essential for the future development of effective intelligent assistants. This research also reinforced concerns about a relatively poor ability of some voice-activated assistants to interpret the accents of non-native English speakers

Scared to go to the Hospital : Inpatient Experiences with Undesirable Events.

Involving patients in healthcare safety practices has long been an area of priority and importance. However, we still need to understand how patients perceive undesirable events during their hospital stay, and what role patients play in the prevention of these events. To address this gap, we surveyed pediatric inpatients and caregivers to understand their perspectives on undesirable events. By giving them an opportunity to use their own words to describe their experiences, we found a diverse array of undesirable events. Our qualitative analysis revealed four major types of events that patients and caregivers experienced: mismanagement, communication, policy, and lack of care coordination. We also examined the information needs that patients and caregivers experienced during these situations, and learned how they would prefer to receive this information. Based on these results, we provide recommendations for inpatient technologies that could enable patients to identify and prevent such undesirable events