Patient locus of control (LOC) is a strong determinant of health outcomes, yet healthcare systems rarely use patient LOC to devise treatment plans. Our clinical research applies action design research methods to design and evaluate healthcare information systems to improve paths and outcomes of patient care. Socio-technical synergies between the interior (technical) and exterior (socio) designs support effective human-computer interfaces and interactions. Rigorous focus group evaluations of the systems for capturing LOC information and using that information in treatment plans are performed and system refinements are implemented. The practitioner organisation has future plans for full implementation of the designed systems.

Worldwide epidemics, such as corona virus disease 2019 (COVID-19), cause unprecedented challenges for society and its healthcare systems. Governments attempt to mitigate those challenges by either reducing healthcare demand (“flattening the curve” by imposing restrictions, e.g., on travel or social gatherings) or by increasing healthcare capacity, for example, by canceling elective procedures or setting up field hospitals. To implement these mitigation procedures efficiently, accurate and timely forecasts of the epidemic’s progression are necessary. In this paper, we develop an innovative forecasting methodology based on the ideas of long short-term memory (LSTM) recurrent neural networks. LSTM models are shown to outperform traditional forecasting models, especially when the relationship between input and output is complex and not available in closed form. However, whereas LSTM models perform well for data that changes dynamically over time, one shortcoming is that they are not directly applicable when the data also includes static, nontemporal components. In this work, we propose an [Formula: see text] model that overcomes this limitation. Our model leverages a private partnership with a mobile data company in order to capture population mobility (using mobility indices derived from mobile device data), which allows us to anticipate an epidemic’s spread early and accurately. In addition, we also leverage a public partnership with a consortium of hospitals. Using hospital admissions (rather than, say, positive caseload) results in an unbiased measure of the severity of an epidemic because patients seek and are admitted to hospital care only when symptoms worsen beyond a critical point. We illustrate the effectiveness of our method on forecasting COVID-19 for a major U.S. metropolitan area where it has aided decision makers of the emergency policy group. Our model improves the predictive accuracy of hospital admission by a factor of 2.5× as compared with competing models in the same analytical space. History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: This research was funded by a monetary gift from Hillsborough County to establish the Pandemic Response Research Fund at University of South Florida. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1269 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0027 ) at ( http://dx.doi.org/10.5281/zenodo.7112004 ).

This research essay proposes an elaborated process model for applying the action design research (ADR) approach to immersive industry-based projects. Building on the original ADR concepts, we identify four distinct types of ADR cycles for diagnosis, design, implementation, and evolution of the growing artefact-based solution. Each ADR cycle moves through activities of problem formulation, artefact creation, evaluation, reflection, and learning. Rapid iterations of ADR cycles provide a well-defined process map for managing and performing an emergent ADR project. The proposed model supports multiple entry points based on the current state of the problem environment and the goals of the ADR project. The elaborated ADR process model provides a more flexible yet disciplined inquiry into the initiation, conduct, reflection, and presentation of rigorous and relevant ADR projects.

This position paper proposes a new stream of research targeted at technical debt as a source of information asymmetry between finance and IT professionals involved in information technology investment decisions. Finance teams interact with technology teams in several ways, predominantly when business cases require review and during the annual budgeting process. During these discrete interactions, finance teams are required to digest large amounts of technical strategy and architectural information chock-full of technical terminology and diagrams. Typically, the estimates for effort are soft and risk is difficult to measure. It is within this context that finance approves budgets and projects that inevitably result in the accumulation of technical debt. This paper discusses some of the dynamics at work between finance and IT teams within large complex organizations when they meet to make technology investment decisions. In addition, future research is proposed aimed at reducing information asymmetry, thereby leading to improved IT investment decisions and better management of technical debt.1