The role of innovative global institutions in linking knowledge and action

It is becoming increasingly recognized that our collective ability to tackle complex problems will require the development of new, adaptive, and innovative institutional arrangements that can deal with rapidly changing knowledge and have effective learning capabilities. In this paper, we applied a knowledge-systems perspective to examine how institutional innovations can affect the generation, sharing, and application of scientific and technical knowledge. We report on a case study that examined the effects that one large innovative organization, The Global Fund to Fight AIDS, Tuberculosis, and Malaria, is having on the knowledge dimensions of decision-making in global health. The case study shows that the organization created demand for new knowledge from a range of actors, but it did not incorporate strategies for meeting this demand into their own rules, incentives, or procedures. This made it difficult for some applicants to meet the organization's dual aims of scientific soundness and national ownership of projects. It also highlighted that scientific knowledge needed to be integrated with managerial and situational knowledge for success. More generally, the study illustrates that institutional change targeting implementation can also significantly affect the dynamics of knowledge creation (learning), access, distribution, and use. Recognizing how action-oriented institutions can affect these dynamics across their knowledge system can help institutional designers build more efficient and effective institutions for sustainable development.

The Role of Diverse Strategies in Sustainable Knowledge Production

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.

Ergonomics for the inclusion of older workers in the knowledge workforce and a guidance tool for designers

The ageing of the population and the inverted population pyramid is bringing important changes to society as a whole. These changes are associated with the inclusion of an older workforce in knowledge work and the challenge they represent in adapting the work environment accordingly. In order to approach a more universal design of the work environment, industrial designers need support from user-sensitive inclusive design studies. While there are plenty of guidelines and tools containing relevant information, there is a need to develop more appropriate tools for Industrial Designers that cover the initial phase of the design process. This study provides a review of the available tools and guidelines and proposes a theoretical framework intended for developing a design guidance tool for inclusive workstation design.

Brokering the Research-Practice Gap: A typology

Despite widespread recognition of a research-practice gap in multiple service sectors, less is known about how pre-existing communication channels facilitate the flow of information between researchers and practitioners. In the current study, we applied an existing typology of brokerage developed by Gould and Fernandez (Sociol Methodol 19:89-126, 1989) to examine what types of brokerage facilitate information spread between researchers and educational practitioners. Specifically, we conducted semi-structured interviews with 19 school administrators and staff in two public school districts regarding their experiences searching for information about instructional, health, and social skills programs. Using deductive content analysis, we found evidence of all five types of brokerage identified by Gould and Fernandez (1989). However, only three types of brokerage-gatekeepers, representatives, and liaisons-were involved in the flow of information between school administrators and researchers. Moreover, information transfer often occurred in longer chains that involved multiple, distinct types of brokerage. We conclude with the broad implications of our findings for narrowing the research-practice gap by improving researchers' dissemination efforts and practitioners' search for information.

Towards a better management of complex emergencies through crisis management meta-modelling

Managing complex emergency situations is a challenging task, mainly due to the heterogeneity of the partners involved and the critical nature of such events. Whatever approach is adopted to support this objective, one unavoidable issue is knowledge management. In the context of our research project, gathering, formalising and exploiting all the knowledge and information about a given crisis situation is a critical requirement. This paper presents some research results concerning this specific topic: from a theoretical point of view, the generic dimensions of crisis characterisation are defined, while from a technical point of view, we describe a software solution able to collect that knowledge (based on meta-models and ontologies). This is used to confront the characteristics of the situation (context) with characteristics of the resources (relief system) in order to design a suitable response. Finally, an illustrative example concerning a crash between a tanker truck and a train is described.

The effectiveness of toolkits as knowledge translation strategies for integrating evidence into clinical care: a systematic review

OBJECTIVES

The aim of this systematic review was to evaluate the effectiveness of toolkits as a knowledge translation (KT) strategy for facilitating the implementation of evidence into clinical care. Toolkits include multiple resources for educating and/or facilitating behaviour change.

DESIGN

Systematic review of the literature on toolkits.

METHODS

A search was conducted on MEDLINE, EMBASE, PsycINFO and CINAHL. Studies were included if they evaluated the effectiveness of a toolkit to support the integration of evidence into clinical care, and if the KT goal(s) of the study were to inform, share knowledge, build awareness, change practice, change behaviour, and/or clinical outcomes in healthcare settings, inform policy, or to commercialise an innovation. Screening of studies, assessment of methodological quality and data extraction for the included studies were conducted by at least two reviewers.

RESULTS

39 relevant studies were included for full review; 8 were rated as moderate to strong methodologically with clinical outcomes that could be somewhat attributed to the toolkit. Three of the eight studies evaluated the toolkit as a single KT intervention, while five embedded the toolkit into a multistrategy intervention. Six of the eight toolkits were partially or mostly effective in changing clinical outcomes and six studies reported on implementation outcomes. The types of resources embedded within toolkits varied but included predominantly educational materials.

CONCLUSIONS

Future toolkits should be informed by high-quality evidence and theory, and should be evaluated using rigorous study designs to explain the factors underlying their effectiveness and successful implementation.

The Self-Assessment Process and Impacts on the Health Information Management Program Performance: A Case Study

This study examined how health information management (HIM) educational programs can use the Malcolm Baldrige National Quality Award Model (MBNQAM) educational criteria to meet the self-assessment requirement for Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM) accreditation. An existing instrument, Quantum Performance Group's Organizational Assessment Survey authored by Dr. Mark Blazey, was used in this study. The instrument was designed to self-assess the entire organization. Results of the study demonstrate how the MBNQAM can be used to successfully self-assess HIM programs. This research adds to the body of literature surrounding the application of the MBNQAM for HIM programs and provides new information to deans, administrators, and educators that may be useful, as an added component, when self-assessing HIM programs. The results of this study will help to establish a foundation for HIM programs to strengthen the self-assessment process, providing a strong starting point for strategic planning prioritization for HIM program improvement initiatives. The improved process will help in maturing the HIM program while fulfilling accreditation requirements for self-assessment. As additional HIM programs formalize the self-assessment process, benchmarking opportunities with other HIM programs will be created.

Managing OMICS-Data: Considerations for the Design of a Clinical Research IT-Infrastructure

Biomarker-based translational research enables deep insight into cellular processes and human diseases. As a result, high-throughput technologies promulgating a fast and cost-effective generation of data are widely used to advance our understanding in the molecular background of individuals. However, the increasing volume and complexity of data increases the need for sustainable infrastructures and state-of-the-art tools allowing management, analysis, and integration of OMICS data. To address these challenges, we have performed site visits of core facilities with a focus on high-throughput technologies to explore their (IT) infrastructure, organizational aspects, and data management strategies. Different stakeholders were interviewed regarding requirements and needs for dealing with high-throughput data. We have identified four different fields of action: (1) the interface from biorepositories to service providers of high-throughput technologies, (2) aspects within services providers, (3) the interface from service providers to bioinformatical analysis, and (4) organizational and other aspects. For each field, recommendations and strategies were developed for implementation of a seamless pipeline from biorepositories to highly specialized high-throughput laboratories including the sustainable management and integration of OMICS data.

Clinical Decision Support Knowledge Management: Strategies for Success

Clinical Decision Support Systems have been shown to increase quality of care, patient safety, improve adherence to guidelines for prevention and treatment, and avoid medication errors. Such systems depend mainly on two types of content; the clinical information related to patients and the medical knowledge related to the specialty that informs the system rules and alerts. At King Faisal Specialist Hospital and Research Center, Saudi Arabia, the Health Information Technology Affairs worked on identifying best strategies and recommendations for successful CDSS knowledge management. A review of literature was conducted to identify main areas of challenges and factors of success. A qualitative survey was used over six months' duration to collect opinions, experiences and suggestions from both IT and healthcare professionals. Recommendations were categorized into ten main topics that should be addressed during the development and implementation of CDSS knowledge management tools in the hospital.

Knowledge Management within the Medical University

The aim of the work is studying the possibilities of ontological engineering in managing of medical knowledge. And also practical implementation of knowledge management system (KMS) in medical university. The educational process model is established that allows analyzing learning results within time scale. Glossary sub-system has been developed; ontologies of educational disciplines are constructed; environment for setup and solution of situational cases is established; ontological approach to assess competencies is developed. The possibilities of the system for solving situation tasks have been described. The approach to the evaluation of competence has been developed.

A Decision Fusion Framework for Treatment Recommendation Systems

Treatment recommendation is a nontrivial task--it requires not only domain knowledge from evidence-based medicine, but also data insights from descriptive, predictive and prescriptive analysis. A single treatment recommendation system is usually trained or modeled with a limited (size or quality) source. This paper proposes a decision fusion framework, combining both knowledge-driven and data-driven decision engines for treatment recommendation. End users (e.g. using the clinician workstation or mobile apps) could have a comprehensive view of various engines' opinions, as well as the final decision after fusion. For implementation, we leverage several well-known fusion algorithms, such as decision templates and meta classifiers (of logistic and SVM, etc.). Using an outcome-driven evaluation metric, we compare the fusion engine with base engines, and our experimental results show that decision fusion is a promising way towards a more valuable treatment recommendation.

Evaluation of need for ontologies to manage domain content for the Reportable Conditions Knowledge Management System

The Reportable Condition Knowledge Management System (RCKMS) is envisioned to be a single, comprehensive, authoritative, real-time portal to author, view and access computable information about reportable conditions. The system is designed for use by hospitals, laboratories, health information exchanges, and providers to meet public health reporting requirements. The RCKMS Knowledge Representation Workgroup was tasked to explore the need for ontologies to support RCKMS functionality. The workgroup reviewed relevant projects and defined criteria to evaluate candidate knowledge domain areas for ontology development. The use of ontologies is justified for this project to unify the semantics used to describe similar reportable events and concepts between different jurisdictions and over time, to aid data integration, and to manage large, unwieldy datasets that evolve, and are sometimes externally managed.

Managing free text for secondary use of health data

OBJECTIVE

To summarize the best papers in the field of Knowledge Representation and Management (KRM).

METHODS

A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM and natural language processing (NLP) published in 2013.

RESULTS

Four articles were selected, one focuses on Electronic Health Record (EHR) interoperability for clinical pathway personalization based on structured data. The other three focus on NLP (corpus creation, de-identification, and co-reference resolution) and highlight the increase in NLP tools performances.

CONCLUSION

NLP tools are close to being seriously concurrent to humans in some annotation tasks. Their use could increase drastically the amount of data usable for meaningful use of EHR.

Representing Human Expertise by the OWL Web Ontology Language to Support Knowledge Engineering in Decision Support Systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

Data management in the modern structural biology and biomedical research environment

Modern high-throughput structural biology laboratories produce vast amounts of raw experimental data. The traditional method of data reduction is very simple-results are summarized in peer-reviewed publications, which are hopefully published in high-impact journals. By their nature, publications include only the most important results derived from experiments that may have been performed over the course of many years. The main content of the published paper is a concise compilation of these data, an interpretation of the experimental results, and a comparison of these results with those obtained by other scientists.Due to an avalanche of structural biology manuscripts submitted to scientific journals, in many recent cases descriptions of experimental methodology (and sometimes even experimental results) are pushed to supplementary materials that are only published online and sometimes may not be reviewed as thoroughly as the main body of a manuscript. Trouble may arise when experimental results are contradicting the results obtained by other scientists, which requires (in the best case) the reexamination of the original raw data or independent repetition of the experiment according to the published description of the experiment. There are reports that a significant fraction of experiments obtained in academic laboratories cannot be repeated in an industrial environment (Begley CG & Ellis LM, Nature 483(7391):531-3, 2012). This is not an indication of scientific fraud but rather reflects the inadequate description of experiments performed on different equipment and on biological samples that were produced with disparate methods. For that reason the goal of a modern data management system is not only the simple replacement of the laboratory notebook by an electronic one but also the creation of a sophisticated, internally consistent, scalable data management system that will combine data obtained by a variety of experiments performed by various individuals on diverse equipment. All data should be stored in a core database that can be used by custom applications to prepare internal reports, statistics, and perform other functions that are specific to the research that is pursued in a particular laboratory.This chapter presents a general overview of the methods of data management and analysis used by structural genomics (SG) programs. In addition to a review of the existing literature on the subject, also presented is experience in the development of two SG data management systems, UniTrack and LabDB. The description is targeted to a general audience, as some technical details have been (or will be) published elsewhere. The focus is on "data management," meaning the process of gathering, organizing, and storing data, but also briefly discussed is "data mining," the process of analysis ideally leading to an understanding of the data. In other words, data mining is the conversion of data into information. Clearly, effective data management is a precondition for any useful data mining. If done properly, gathering details on millions of experiments on thousands of proteins and making them publicly available for analysis-even after the projects themselves have ended-may turn out to be one of the most important benefits of SG programs.

Machine Learning for Knowledge Extraction from PHR Big Data

Cloud computing, Internet of things (IOT) and NoSQL database technologies can support a new generation of cloud-based PHR services that contain heterogeneous (unstructured, semi-structured and structured) patient data (health, social and lifestyle) from various sources, including automatically transmitted data from Internet connected devices of patient living space (e.g. medical devices connected to patients at home care). The patient data stored in such PHR systems constitute big data whose analysis with the use of appropriate machine learning algorithms is expected to improve diagnosis and treatment accuracy, to cut healthcare costs and, hence, to improve the overall quality and efficiency of healthcare provided. This paper describes a health data analytics engine which uses machine learning algorithms for analyzing cloud based PHR big health data towards knowledge extraction to support better healthcare delivery as regards disease diagnosis and prognosis. This engine comprises of the data preparation, the model generation and the data analysis modules and runs on the cloud taking advantage from the map/reduce paradigm provided by Apache Hadoop.

Predictors of attitude and intention to use knowledge management system among Korean nurses

Knowledge sharing using Knowledge Management (KM) systems helps nurses to understand and acquire appropriate knowledge that influences the quality of healthcare service. The purpose of this study was to identify organizational and individual factors influencing attitude and intention to use KM systems among Korean nurses. A cross-sectional survey design was used to study a sample of 245 nurses employed at five hospitals in Seoul. A multiple hierarchical regression was used to examine predictors of nurses' attitude and intention to use. From an individual perspective, nurse's informatics competency was identified as a significant factor influencing attitudes toward knowledge management usage within adhocracy and clan cultures. However, from an organizational perspective, level of hospital information system was identified as a significant factor influencing KM system usage within adhocracy cultures. The findings of this study will be helpful in better understanding and assessing the impact of the factors affecting the implementation of nursing knowledge management systems and in further developing successful managerial strategies using knowledge resources in healthcare settings.

Analysis and deliberation as a mechanism to assess changes in preferences for indicators of sustainable forest management: a case study in Puebla, Mexico

The purpose of this study was to assess changes in forest owners' preferences regarding indicators of sustainable forest management. The analysis and deliberation framework served as a platform upon which to explore these changes in a rural community in Puebla, Mexico. Sixty-two indicators were selected from existing sets to design a five-point Likert survey instrument. The instrument was administered three times: early in the study to capture the forest owners' preferences before intervention (baseline); following an educational meeting in which the participants learned of three alternative forest management plans (analysis); and following a community meeting in which the forest owners deliberated to choose one of the alternatives (deliberation). As forest owners were exposed to knowledge (analysis) and deliberation, their preferences for the indicators changed significantly. An examination of the instrument demonstrated how the indicators increased or decreased in importance. Social and economic indicators tended to be ranked differently following analysis and again following deliberation because of the commitment to pursue a forest management plan that would benefit the community without jeopardizing stakeholder values. The ecological indicators directly associated with forest structure gained relevance following the analysis meeting. The deliberation process elucidated the importance of forest administration and professional help. Forest owners' preferences were reflected by the selection of the management plan that included good science and accommodated community values. The forest owners changed their preferences in response to new knowledge, management objectives, and their commitment to safeguarding the future condition of the forest. Analysis and deliberation is a participatory forum that facilities communication and learning and allows stakeholders to share values; thus, it serves as a mechanism for forest planning.