Group decision-making in an unconventional emergency situation using agile Delphi approach

Decision Support for Plan Adaptation in Unforeseen Situations

Handling irregular phenomena might bring great complexity for involved teams. Variables considered for undertaking recommended procedures may yield many decision alternatives, challenging to deal with at planning time. Additionally, expectations regarding the phenomena handling may not match those observed. This means that the existing plan’s application may become inappropriate, and teams must be creative in performing actions and decision-making. An approach for on-the-fly adaptation of plans aims to assist teams in identifying and diagnosing unforeseen situations, besides adjusting previously developed plans at runtime. This approach was evaluated through experiments in the emergency management domain, and the initial results indicate its feasibility in dealing with unforeseen situations while handling irregular phenomena in complex environments.

The design and evaluation of an antimicrobial resistance surveillance system for neonatal intensive care units in Iran

INTRODUCTION

Neonatal intensive care units (NICUs) have complex patients in terms of their diagnoses and required treatments. Antimicrobial treatment is a common therapy for patients in NICUs. To solve problems pertaining to empirical therapy, antimicrobial stewardship programs have recently been introduced. Despite the success of these programs in terms of data collection, there is still inefficiency in terms of analyzing and reporting the data. Thus, to successfully implement these stewardship programs, the design of antimicrobial resistance (AMR) surveillance systems is recommended as a first step. As a result, this study aimed to design an AMR surveillance system for use in the NICUs in northwestern Iranian hospitals to cover these information gaps.

METHODS

The recommended system is compatible with the World Health Organization (WHO) guidelines. The business intelligence (BI) requirements were extracted in an interview with a product owner (PO) using a valid and reliable checklist. Following this, an AMR surveillance system was designed and evaluated in relation to user experiences via a user experience questionnaire (UEQ). Finally, an association analysis was performed on the database, and the results were reported by identifying the important multidrug resistances in the database.

RESULTS

A customized software development methodology was proposed. The three major modules of the AMR surveillance are the data registry, dashboard, and decision support modules. The data registry module was implemented based on a three-tier architecture, and the Clinical Decision Support System (CDSS) and dashboard modules were designed based on the BI requirements of the Scrum product owner (PO). The mean values of UEQ measures were in a good range. This measures showed the suitable usability of the AMR surveillance system.

CONCLUSION

Applying efficient software development methodologies allows for the systems' compatibility with users' opinions and requirements. In addition, the construction of interdisciplinary communication models for research and software engineering allows for research and development concepts to be used in operational environments.

A Lightweight Social Computing Approach to Emergency Management Policy Selection

In order to select effective policies for emergency management in a timely manner, this paper proposes an agile and lightweight social computing approach to facilitating policy selection, evaluation, and adjustment relative to emergency management in both quantitative and qualitative ways. The approach consists of three components represented as PZE: 1) (P) emergency management policy selecting; 2) (Z) modeling artificial societies with the zombie-city model (a general and formal artificial society model); and 3) (E) policy evaluation. The formal specification of the zombie-city model and rigorous expressions of scenarios enable rigorous description and formal reasoning of an artificial society. A feedback loop of this approach supports the iterative adjustment of emergency management policies and the creation of more effective policies. This approach is verified by applying it to a case of an infectious disease transmission with quantitative evaluations, qualitative reasoning and analysis, and iterative adjustments. Results indicate effective emergency management policies can be established with the approach in an iterative way. In contrast with existing research, our proposed approach offers the benefits of being simple, general, rapidly adaptive to changes, and low cost.