Healing Architecture in Healthcare: A Scoping Review

Objectives: The purpose of this scoping review is to identify evidence on how characteristics of healing architecture in clinical contexts impact clinical practice and patient experiences. Based on these insights, we advance a more practice-based approach to the study of how healing architectures work. Background: The notion of “healing architecture” has recently emerged in discussions of the spatial organization of healthcare settings, particularly in the Nordic countries. This scoping review summarizes findings from seven articles which specifically describe how patients and staff experience characteristics of healing architecture. Methods: This scoping review was conducted using the framework developed by Arksey and O’Malley. We referred to the decision tool developed by Pollock et al. to confirm that this approach was the most appropriate evidence synthesis type to identify characteristics related to healing architecture and practice. To ensure the rigor of this review, we referred to the methodological guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews. Results: There are two main findings of the review. First, there is no common or operative definition of healing architecture used in the selected articles. Secondly, there is limited knowledge of how healing architecture shapes clinical and patient outcomes. Conclusions: We conclude that further research is needed into how healing architectures make a difference in everyday clinical practices, both to better inform the development of evidence-based designs in the future and to further elaborate criteria to guide postoccupancy evaluations of purpose-built sites.

PARAFOG v2.0: a near-real-time decision tool to support nowcasting fog formation events at local scales

An improved version of the near-real-time decision tool PARAFOG (PFG2) is presented to retrieve pre-fog alert levels and to discriminate between radiation (RAD) and stratus lowering (STL) fog situations. PFG2 has two distinct modules to monitor the physical processes involved in RAD and STL fog formation and is evaluated at European sites. The modules are based on innovative fuzzy logic algorithms to retrieve fog alert levels (low, moderate, high) specific to RAD/STL conditions, minutes to hours prior to fog onset. The PFG2-RAD module assesses also the thickness of the fog. Both the PFG2-RAD and PFG2-STL modules rely on the combination of visibility observations and automatic lidar and ceilometer (ALC) measurements. The overall performance of the PFG2-RAD and PFG2-STL modules is evaluated based on 9 years of measurements at the SIRTA (Instrumented Site for Atmospheric Remote Sensing Research) observatory near Paris and up to two fog seasons at the Paris-Roissy, Vienna, Munich, and Zurich airports. At all sites, pre-fog alert levels retrieved by PFG2 are found to be consistent with the local weather analysis. The advanced PFG2 algorithm performs with a hit rate of about 100 % for both considered fog types and presents a false alarm ratio on the order of 10 % (30 %) for RAD (STL) fog situations. Finally, the first high alerts that result in a subsequent fog event are found to occur for periods of time ranging from −120 min to fog onset, with the first high alerts occurring earlier for RAD than STL cases.

I-DECIDED® – A decision tool for assessment and management of invasive devices in the hospital setting

Indwelling medical devices, including vascular access and urinary catheters, pose a risk for infection, and therefore daily assessment and consideration of their continued need is a patient safety priority. The I-DECIDED® device assessment and decision tool is an evidence-based checklist, designed to improve the assessment, care, and timely removal of invasive devices in acute hospitalized patients. This paper explains each step of the tool, with rationale for inclusion.

Development of a Model-Informed Dosing Tool to Optimise Initial Antibiotic Dosing—A Translational Example for Intensive Care Units

The prevalence and mortality rates of severe infections are high in intensive care units (ICUs). At the same time, the high pharmacokinetic variability observed in ICU patients increases the risk of inadequate antibiotic drug exposure. Therefore, dosing tailored to specific patient characteristics has a high potential to improve outcomes in this vulnerable patient population. This study aimed to develop a tabular dosing decision tool for initial therapy of meropenem integrating hospital-specific, thus far unexploited pathogen susceptibility information. An appropriate meropenem pharmacokinetic model was selected from the literature and evaluated using clinical data. Probability of target attainment (PTA) analysis was conducted for clinically interesting dosing regimens. To inform dosing prior to pathogen identification, the local pathogen-independent mean fraction of response (LPIFR) was calculated based on the observed minimum inhibitory concentrations distribution in the hospital. A simple, tabular, model-informed dosing decision tool was developed for initial meropenem therapy. Dosing recommendations achieving PTA > 90% or LPIFR > 90% for patients with different creatinine clearances were integrated. Based on the experiences during the development process, a generalised workflow for the development of tabular dosing decision tools was derived. The proposed workflow can support the development of model-informed dosing tools for initial therapy of various drugs and hospital-specific conditions.

An exploratory design science study on theory testing using crowdsourcing

<p>Theory in Information Systems (IS) is very important to the development of the field. Theory building, and theory testing seeks to accumulate knowledge about the relationships between people and technology. Testing theory can be difficult to accomplish, especially when it involves humans, a diversity of methods and sources, multiple experiments, large data sets, and careful tuning of conditions and instruments. Crowdsourcing is a strategy supporting the distribution of activities to crowd workers, which suggests that it may be used to support theory testing. This exploratory study seeks to analyse the adoption of crowdsourcing in theory testing, and to develop guidance for researchers to instantiate the strategy in their research projects. The study adopts the design science research paradigm to explore incorporating the crowdsourcing strategy in theory testing, and to evaluate its viability and utility. According to the principles of design science research, the study is structured around the construction of several interconnected IS artefacts: 1) a conceptual framework articulating the main principles of theory testing; 2) a pattern model of theory testing, which codifies existing research approaches to theory testing; and 3) a decision tool, which codifies guidelines for researchers making decisions on which research activities to crowdsource. In order to build the conceptual framework and pattern model, the study conducts a systematic review of theory testing in the IS domain. Both the conceptual framework and pattern model are then operationalized in the decision tool. The utility of the various artefacts is then assessed with the participation of research practitioners. This study is relevant because it synthesizes knowledge about theory testing, builds innovative artefacts supporting the adoption of crowdsourcing in theory testing, helps academic researchers understanding the theory testing process, and enables them to adopt crowdsourcing for theory testing.</p>

An exploratory design science study on theory testing using crowdsourcing

<p>Theory in Information Systems (IS) is very important to the development of the field. Theory building, and theory testing seeks to accumulate knowledge about the relationships between people and technology. Testing theory can be difficult to accomplish, especially when it involves humans, a diversity of methods and sources, multiple experiments, large data sets, and careful tuning of conditions and instruments. Crowdsourcing is a strategy supporting the distribution of activities to crowd workers, which suggests that it may be used to support theory testing. This exploratory study seeks to analyse the adoption of crowdsourcing in theory testing, and to develop guidance for researchers to instantiate the strategy in their research projects. The study adopts the design science research paradigm to explore incorporating the crowdsourcing strategy in theory testing, and to evaluate its viability and utility. According to the principles of design science research, the study is structured around the construction of several interconnected IS artefacts: 1) a conceptual framework articulating the main principles of theory testing; 2) a pattern model of theory testing, which codifies existing research approaches to theory testing; and 3) a decision tool, which codifies guidelines for researchers making decisions on which research activities to crowdsource. In order to build the conceptual framework and pattern model, the study conducts a systematic review of theory testing in the IS domain. Both the conceptual framework and pattern model are then operationalized in the decision tool. The utility of the various artefacts is then assessed with the participation of research practitioners. This study is relevant because it synthesizes knowledge about theory testing, builds innovative artefacts supporting the adoption of crowdsourcing in theory testing, helps academic researchers understanding the theory testing process, and enables them to adopt crowdsourcing for theory testing.</p>