Intelligent Interactive Systems Group at Harvard

Improving Care Coordination in Complex Healthcare

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. Our formative studies revealed that the nature of teamwork in complex care poses challenges to team coordination that extend beyond those identified in prior work and that can be handled by existing coordination systems. We are building on a computational theory of teamwork to create entirely new tools to support complex, loosely-coupled teamwork.

Lab in the Wild

Lab in the Wild is a platform for conducting large scale behavioral experiments with unpaid online volunteers. LabintheWild helps make empirical research in Human-Computer Interaction more reliable (by making it possible to recruit many more participants than would be possible in conventional laboratory studies) and more generalizable (by enabling access to very diverse groups of participants).

LabintheWild experiments typically attract thousands or tens of thousands of participants (with two studies reaching more than 250,000 people). LabintheWild's volunteer participants have also been shown to provide more reliable data and exert themselves more than participants recruited via paid platforms (like Amazon Mechanical Turk). A key characteristic of LabintheWild is its incentive structure: Instead of money, participants are rewarded with information about their performance and an ability to compare themselves to others. This design choice engages curiosity and enables social comparison---both of which motivate participants.

LabintheWild is co-directed by Profs. Katharina Reinecke and Krzysztof Gajos.

AI-Supported Decision Making

AI-powered decision support tools form part of sociotechnical systems, that is human+AI teams tasked with making decisions. Because people and AI-powered systems have complementary strengths, many expected that human+AI teams would perform better on decision-making tasks than either people or AIs alone. However, there is mounting evidence that human+AI teams often perform worse than AIs alone. Building on insights from both machine learning and cognitive science, we are developing new general principles and specific solutions to overcome human overreliance on the AI and to help human+AI teams make higher-quality and more confident decisions than what existing systems enable.

Our results also suggest that the limitations of contemporary explainable AI solutions are not appreciated because the most commonly-used methods for evaluating AI-powered decision support systems are likely to produce misleading (overly optimistic) results.

Quantifying Motor Impairments

Healthcare, clinical trials, and research related to neurological disease all require tools for accurately and objectively measuring motor impairments. Our first tool, called Hevelius, measures motor impairment in the dominant arm based on a person's performance on a simple computer mouse-based task. We are working on other tools as well as on ways to make accurate measurements possible at home without help from clinician. Such at-home measurements can enable granular longitudinal measurements of disease progresson as well as large-scale assessments. This project is done in collaboration with the Laboratory for Deep Neurophenotyping at Massachusetts General Hospital.

DERBI: Communicating Individual Biomonitoring and Personal Exposure Results to Study Participants

Epidemiologic studies and public health biomonitoring rely on chemical exposure measurements in blood, urine, and other tissues, and in personal environments, such as homes. For many chemicals, the health implications of individual results are uncertain, and the sources and strategies to reduce exposure may not be known. Yet, a growing number of researchers consider it their ethical obligation to report the results back to their participants. In a project led by the Silent Spring Institute, we are building scalable online tools to help researchers communicate personalized results to study participants in a manner that appropriately conveys what is and what is not known about the sources and effects of different environmental chemicals.