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

Patient-Peer Support to Improve Quality and Safety in the Hospital

Thesis (Ph.D.)--University of Washington, 2019Patient safety is a critical and persistent problem impacting health care systems around the world. Despite major financial and technological investments to improve this problem, medical errors remain a leading cause of death in the United States. As experts in the care they receive, patients offer unique insights about the source of these problems and have key roles in their prevention. However, most interventions have not included patients as equal partners in safeguarding their own care. Peer support is one type of intervention that recognizes the valuable insights patients could provide for each other to improve the quality and safety of their care. In many other health care settings, digital peer interventions have been implemented, and have demonstrated benefits such as increased knowledge, empowerment, and self-efficacy—many factors that also influence patient involvement in safety. Yet, we know little about how peer support might translate into the context of patient safety, particularly in a hospital setting. In this thesis, I investigate how peer support technologies can improve the quality and safety of a patient’s hospital stay. I first examine what opportunities exist for peer support in the hospital and articulate design recommendations for technologies to enable this support. I then describe my design, implementation, and deployment of a fully-functioning patient-peer support technology for the hospital setting. Finally, I show how patients used this technology and how it impacted their hospitalization. My findings reveal that peer support can be a powerful tool that equips patients with the support they need to navigate their hospital stay and can help patients take proactive steps toward improving the quality and safety of their care

The Supportive Accountability Inventory: Psychometric properties of a measure of supportive accountability in coached digital interventions

Background: One of the most widely used coaching models is Supportive Accountability (SA) which aims to provide intervention users with clear expectations for intervention use, regular monitoring, and a sense that coaches are trustworthy, benevolent, and have domain expertise. However, few measures exist to study the role of the SA model on coached digital interventions. We developed the Supportive Accountability Inventory (SAI) and evaluated the underlying factor structure and psychometric properties of this brief self-report measure. Method: Using data from a two-arm randomized trial of a remote intervention for major depressive disorder (telephone CBT [tCBT] or a stepped care model of web-based CBT [iCBT] and tCBT), we conducted an Exploratory Factor Analysis on the SAI item pool and explored the final SAI's relationship to iCBT engagement as well as to depression outcomes. Participants in our analyses (n = 52) included those randomized to a receive iCBT, but were not stepped up to tCBT due to insufficient response to iCBT, had not remitted prior to the 10-week assessment point, and completed the pool of 8 potential SAI items. Results: The best fitting EFA model included only 6 items from the original pool of 8 and contained two factors: Monitoring and Expectation. Final model fit was mixed, but acceptable (χ2(4) = 5.24, p = 0.26; RMSR = 0.03; RMSEA = 0.091; TLI = 0.967). Internal consistency was acceptable at α = 0.68. The SAI demonstrated good convergent and divergent validity. The SAI at the 10-week/mid-treatment mark was significantly associated with the number of days of iCBT use (r = 0.29, p = .037), but, contrary to expectations, was not predictive of either PHQ-9 scores (F(2,46) = 0.14, p = .89) or QIDS-C scores (F(2,46) = 0.84, p = .44) at post-treatment. Conclusion: The SAI is a brief measure of the SA framework constructs. Continued development to improve the SAI and expand the constructs it assesses is necessary, but the SAI represents the first step towards a measure of a coaching protocol that can support both coached digital mental health intervention adherence and improved outcomes

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