The purpose of this chapter is to review progress in our understanding of human behavior and decision-making relevant to future earth research agenda, and propose Decision Science as a hub of knowledge networks connecting disciplinary and interdisciplinary sciences with the practice of problem-solving. This review is composed of four sections. First, we describe the conceptual framework of “decision science for a sustainable society” and argue that evolutionary biology of the human nature is key to construct this framework. Second, we review how our group decision-making often fails due to various cognitive biases and argue that participatory approaches of co-design and co-production do not guarantee reasonable decision-making. Third, we review success stories of problem-solving in local communities and consider how we can connect those successes in local communities to successful national and global decision-making. Fourth, learning from both failures and successes, we argue that the adaptive learning of society is a process enabling us to transform our society toward a sustainable future. We review some positive global trends toward sustainability and consider the cognitive processes and behavioral mechanisms behind those trends that would provide clues for finding successful ways to transform our society.

Medical staff carry an inordinate risk of infection from patients, and many doctors, nurses, and other healthcare workers are affected by COVID-19 worldwide. The unreached communities with noncommunicable diseases (NCDs) such as chronic cardiovascular, respiratory, endocrine, digestive, or renal diseases became more vulnerable during this pandemic situation. In both cases, Remote Healthcare Systems (RHS) may help minimize the risk of SARS-CoV-2 transmission. This study used the WHO guidelines and Design Science Research (DSR) framework to redesign the Portable Health Clinic (PHC), an RHS, for the containment of the spread of COVID-19 as well as proposed corona logic (C-Logic) for the main symptoms of COVID-19. Using the distributed service platform of PHC, a trained healthcare worker with appropriate testing kits can screen high-risk individuals and can help optimize triage to medical services. PHC with its new triage algorithm (C-Logic) classifies the patients according to whether the patient needs to move to a clinic for a PCR test. Through modified PHC service, we can help people to boost their knowledge, attitude (feelings/beliefs), and self-efficacy to execute preventing measures. Our initial examination of the suitability of the PHC and its associated technologies as a key contributor to public health responses is designed to “flatten the curve”, particularly among unreached high-risk NCD populations in developing countries. Theoretically, this study contributes to design science research by introducing a modified healthcare providing model.

The advancement of ICT and affordability of medical sensors enable healthcare data to be obtained remotely. Remote healthcare data is erroneous in nature. Detection of errors for remote healthcare data has not been significantly studied. This research aims to design and develop a software system to detect and reduce such healthcare data errors. Enormous research efforts produced error detection algorithms, however, the detection is done at the server side after a substantial amount of data is archived. Errors can be efficiently reduced if the suspicious data can be detected at the source. We took the approach to predict acceptable range of anthropometric data of each patient. We analyzed 40,391 records to monitor the growth patterns. We plotted the anthropometric items e.g., Height, Weight, BMI, Waist and Hip size for males and females. The plots show some patterns based on different age groups. This paper reports one parameter, height of males. We found three groups that can be classified with similar growth patterns: Age group 20–49, no significant change; Age group 50–64, slightly decremented pattern; and Age group 65–100, a drastic height loss. The acceptable range can change over time. The system estimates the updated trend from new health records.

Co-design and co-production with non-academic stakeholders has been recognized as a key approach in transdisciplinary sustainability research. The majority of transdisciplinary studies have been conducted in Europe and North America, with a marked lack of such research in the Asian context—particularly with regard to healthcare. Utilizing a case study involving mobile health check-ups performed using a portable health clinic system in Jaipur, India, from March 2016 to March 2018, this study identifies key factors in co-design and co-production that should be considered to ensure the project’s sustainability. Thoroughly reviewing all of the documents and materials related to the case study’s co-design and co-production, this study identifies the following key factors: (1) mutual stakeholder agreement on a long-term research plan, protocol, and budget; (2) harmonizing research objectives, frames, and the scale of stakeholder expectations; (3) stakeholders’ commitment and a sense of ownership derived from their needs and priorities; (4) stakeholder trust; (5) effective coordinators; (6) personality type and characteristics of stakeholder leaders; (7) capacity building and the empowerment of local research staff and participants; and (8) continuous efforts to involve stakeholders throughout the co-design and co-production processes. Facilitating effective co-design and co-production, these factors will help ensure the future sustainability of projects.