Complex adaptive systems are usually difficult to design and control. The design of complex adaptive systems often, if not always, occurs in a context that is uncertain. In addition to the complexity of the system, the systems both affect and are influenced by a social environment containing a large variety of actors. Methods and processes that study the design of such types of systems can help scholars and practitioners to better understand, design and manage complex adaptive systems. The aim in this paper is to provide a methodology useful for designing and controlling systems developed to solve complex adaptive problems. In this paper, the complex systems life cycle understanding and design framework are introduced as an approach for satisficing of systems at conceptual design. To illustrate the methodology, a financial systemic risks infrastructure systems architecture development case study is presented. Copyright © 2013 John Wiley & Sons, Ltd.

(1) Background: Cameroonians are exposed to poor health services, more so citizens with cardiovascular-related diseases. The global high cost of acquiring healthcare-related technologies has prompted the government and individuals to promote the need for local research and the development of the health system. (2) Objectives: The main goal of this study is to design and develop a low-cost cardiovascular patient monitoring system (RPM) with wireless capabilities that could be used in any region of Cameroon, accessible, and very inexpensive, that are able to capture important factors, well reflecting the patient’s condition and provide alerting mechanisms. (3) Method: Using the lean UX process from the Gothelf and Seiden framework, the implemented IoT-based application measures the patients’ systolic, diastolic, and heart rates using various sensors, that are automated to record directly to the application database for analysis. The validity of the heuristic evaluation was examined in an ethnographic study of paramedics using a prototype of the system in their work environment. (4) Results: We obtained a system that was pre-tested on demo patients and later deployed and tested on seven real human test subjects. The users’ task performances partially verified the heuristic evaluation results. (5) Conclusions: The data acquired by the sensors have a high level of accuracy and effectively help specialists to properly monitor their patients at a low cost. The proposed system maintains a user-friendliness as no expertise is required for its effective utilization.

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