Person:

Gajos, Krzysztof

Children with complex health conditions require care from a large, diverse team of caregivers that includes multiple types of medical professionals, parents and community support organizations. Coordination of their outpatient care, essential for good outcomes, presents major challenges. Extensive healthcare research has shown that the use of integrated, team-based care plans improves care coordination, but such plans are rarely deployed in practice. This paper reports on a study of care teams treating children with complex conditions at a major university tertiary care center. This study investigated barriers to plan implementation and resultant care coordination problems. It revealed the complex nature of teamwork in complex care, which poses challenges to team coordination that extend beyond those identified in prior work and handled by existing coordination systems. The paper builds on a computational teamwork theory to identify opportunities for technology to support increased plan-based complex-care coordination and to propose design approaches for systems that enable and enhance such coordination.

Children with complex health conditions require care from a large, diverse set of caregivers that includes parents and community support organizations as well as multiple types of medical professionals. Coordination of their care is essential for good outcomes, and extensive research has shown that the use of integrated, team -based care plans improves care coordination. Care plans, however, are rarely deployed in practice. This paper describes barriers to effective implementation of care plans in complex care revealed by a study of care providers treating such children. It draws on teamwork theories, identifying ways AI capabilities could enhance care plan use; describes the design of GoalKeeper, a system to support providers use of care plans; and describes initial work toward information sharing algorithms for such systems.

Congestive heart failure (CHF) is the most common reason for hospitalization in people aged 65 years and older in the United States. Especially, a very high rate of unplanned readmissions within 30 days of discharge due to CHF occurs a heavy associated financial burden and degraded quality of care. To effectively prevent CHF readmission and improve overall care, this paper proposes SwellFit, a novel wearable sensor that helps outpatients to proactively monitor a physical symptom of worsening CHF, ankle swelling. Using a flex sensor, SwellFit monitors changes in ankle curvature as an indication of swelling. A pilot test with 4 adults showed that SwellFit successfully distinguish noise data and motion artifacts from ankle swelling. Collaborating with cardiac experts, we are currently planning to test SwellFit with hospitalized CHF patients who have swelling conditions to demonstrate the feasibility and reliability of SwellFit on detecting swelling from ankle curvature.

Complex collaborative activities such as treating patients, co-authoring documents and developing software are often characterized by teamwork that is loosely coupled and extends in time. To remain coordinated and avoid conflicts, team members need to identify dependencies between their activities — which though loosely coupled may interact — and share information appropriately. The loose-coupling of tasks increases the difficulty of identifying dependencies, with the result that team members often lack important information or are overwhelmed by irrelevant information. This paper formalizes a new multi-agent systems problem, Information Sharing in Loosely-Coupled Extended-Duration Teamwork (ISLET). It defines a new representation, Mutual Influence Potential Networks (MIP-Nets) and an algorithm, MIP-DOI, that uses this representation to determine the information that is most relevant to each team member. Importantly, because the extended duration of the teamwork precludes team members from developing complete plans in advance, the MIP-Nets approach, unlike prior work on information sharing, does not rely on a priori knowledge of a team’s possible plans. Instead, it models collaboration patterns and dependencies among people and their activities based on team-member interactions. Empirical evaluations show that this approach is able to learn collaboration patterns and identify relevant information to share with team members.

Mobile technologies offer the potential for enhanced healthcare, especially by supporting self-management of chronic care. For these technologies to impact chronic care, they need to work for older adults, because the majority of people with chronic conditions are older. A major challenge remains: integrating the appropriate use of such technologies into the lives of older adults. We investigated how older adults would accept mobile technologies by interviewing two groups of older adults (technology adopters and non-adopters who aged 60+) about their experiences and perspectives to mobile technologies. Our preliminary results indicate that there is an additional phase, the intention to learn, and three relating factors, self-efficacy, conversion readiness, and peer support, that significantly influence the acceptance of mobile technologies among the participants, but are not represented in the existing models. With these findings, we propose a tentative theoretical model that extends the existing theories to explain the ways in which our participants came to accept mobile technologies. Future work should investigate the validity of the proposed model by testing our findings against younger people.

As population ages and chronic disease increases, new models of health care delivery are inevitable. We propose to interweave the concept of chronic care management into social infrastructure to better support elderly individuals with chronic diseases. As an exemplar, we chose and investigated Congestive Heart Failure (CHF), since it is one of serious health threats among the elderly population in western society. From a preliminary investigation of literature and prior work, we suggest two implications for social care of chronic patients: increased social awareness, and social knowledge base. This is not an exhaustive list of implications, but a starting point to open up discussions towards social care for chronic diseases.