Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Inas S. Khayal, Sarah DeLozier, Sarah Masud Preum. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Healthcare technology significantly improved human health for historically predominant acute conditions of infection and trauma. Today, over 78% of healthcare expenditures address chronic conditions. Healthcare services organically emerged to address acute conditions and formed decentrally by specialty as different departments across many different geographic locations. However, today's chronic conditions require the utilization of decentralized services with no centralized source. The patient is left to coordinate their care, introducing safety concerns. The effect of technology on patient health outcomes is mediated by how these tools are integrated into the healthcare delivery system. Thus, patients experience poorly coordinated care, while clinicians experience increasing operational burden. Therefore, suboptimal care experiences suggest a need to evaluate healthcare technology and system design, developments, and implementations. To address the issue of technological integration across a decentralized socio-technical system, this paper presents a design methodology that combines principles from engineering systems, axiomatic design, healthcare systems architecture, coproduction, and user-centered design to develop healthcare delivery systems designed to enhance clinician and patient care experiences. This multi-stakeholder co-design process uses a systems thinking framework to incorporate technology that addresses stakeholder human values, universal access to technology and services, and designs for rather than reacts to the impact of technology.

The traditional monolithic hospital focused healthcare system organically developed to address acute conditions. In recent years, healthcare needs have shifted from treating acute conditions to meeting an unprecedented chronic disease burden. Chronic disease healthcare is based on continued delivery outside healthcare facilities, deep understanding of individual health state, managing individualized health needs and coordination between many medical specialities. Classic healthcare dynamic models based on production systems and applicable to acute healthcare inadequately address chronic disease. A new dynamic model paradigm is needed to capture the dynamics of providing healthcare for individuals with chronic disease based on clinical need rather than patient throughput. This paper develops a healthcare dynamic model for personalized healthcare delivery and managed individual health outcomes. It utilizes a hetero-functional graph theory rooted in Axiomatic Design for Large Flexible Engineering Systems and Petri nets. The dynamics of the model builds upon a recently developed systems architecture for healthcare delivery which bears several analogies to the architecture of mass-customized production systems. At its essence, the model consists of two synchronized Petri nets; one for the healthcare delivery system and another for individuals' health state evolution. Such a model allows for the transparent description of health outcomes and links them to the evolution of the healthcare delivery system and its associated costs.

A key challenge of data-driven social science is the gathering of high quality multi-dimensional datasets. A second challenge relates to design and execution of structured experimental interventions in-situ, in a way comparable to the reliability and intentionality of ex-situ laboratory experiments. In this paper we introduce the Friends and Family study, in which a young-family residential community is transformed into a living laboratory. We employ a ubiquitous computing approach that combines extremely rich data collection in terms of signals, dimensionality, and throughput, together with the ability to conduct targeted experimental interventions with study populations. We present our mobile-phone-based social and behavioral sensing system, which has been deployed for over a year now. Finally, we describe a novel tailored intervention aimed at increasing physical activity in the subject population. Results demonstrate the value of social factors for motivation and adherence, and allow us to quantify the contribution of different incentive mechanisms.