Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding

Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based not only SQL (NoSQL) model can provide an accessible, extensive, and robust coding data management framework while maintaining certain flexibility. This paper focuses on the design and implementation of a big data-coding warehouse, and it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented and analyzed a big data-coding warehouse via the MongoDB database and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model

Safety-Critical Systems and Agile Development: A Mapping Study

In the last decades, agile methods had a huge impact on how software is developed. In many cases, this has led to significant benefits, such as quality and speed of software deliveries to customers. However, safety-critical systems have widely been dismissed from benefiting from agile methods. Products that include safety critical aspects are therefore faced with a situation in which the development of safety-critical parts can significantly limit the potential speed-up through agile methods, for the full product, but also in the non-safety critical parts. For such products, the ability to develop safety-critical software in an agile way will generate a competitive advantage. In order to enable future research in this important area, we present in this paper a mapping of the current state of practice based on {a mixed method approach}. Starting from a workshop with experts from six large Swedish product development companies we develop a lens for our analysis. We then present a systematic mapping study on safety-critical systems and agile development through this lens in order to map potential benefits, challenges, and solution candidates for guiding future research.Comment: Accepted at Euromicro Conf. on Software Engineering and Advanced Applications 2018, Prague, Czech Republi

Why is it difficult to implement e-health initiatives? A qualitative study

<b>Background</b> The use of information and communication technologies in healthcare is seen as essential for high quality and cost-effective healthcare. However, implementation of e-health initiatives has often been problematic, with many failing to demonstrate predicted benefits. This study aimed to explore and understand the experiences of implementers - the senior managers and other staff charged with implementing e-health initiatives and their assessment of factors which promote or inhibit the successful implementation, embedding, and integration of e-health initiatives.<p></p> <b>Methods</b> We used a case study methodology, using semi-structured interviews with implementers for data collection. Case studies were selected to provide a range of healthcare contexts (primary, secondary, community care), e-health initiatives, and degrees of normalization. The initiatives studied were Picture Archiving and Communication System (PACS) in secondary care, a Community Nurse Information System (CNIS) in community care, and Choose and Book (C&B) across the primary-secondary care interface. Implementers were selected to provide a range of seniority, including chief executive officers, middle managers, and staff with 'on the ground' experience. Interview data were analyzed using a framework derived from Normalization Process Theory (NPT).<p></p> <b>Results</b> Twenty-three interviews were completed across the three case studies. There were wide differences in experiences of implementation and embedding across these case studies; these differences were well explained by collective action components of NPT. New technology was most likely to 'normalize' where implementers perceived that it had a positive impact on interactions between professionals and patients and between different professional groups, and fit well with the organisational goals and skill sets of existing staff. However, where implementers perceived problems in one or more of these areas, they also perceived a lower level of normalization.<p></p> <b>Conclusions</b> Implementers had rich understandings of barriers and facilitators to successful implementation of e-health initiatives, and their views should continue to be sought in future research. NPT can be used to explain observed variations in implementation processes, and may be useful in drawing planners' attention to potential problems with a view to addressing them during implementation planning

Uptake of BIM and IPD within the UK AEC Industry: the evolving role of the architectural technologist

Building Information Modelling is not only a tool, but also the process of creation, maintenance, distribution and co-ordination of an integrated database that collaboratively stores 2D and 3D information, with embedded physical and functional data within a project-building model. The uptake of BIM within the UK Architecture, Engineering and Construction (AEC) industry has been slow since the 1980’s, but over recent years, adoptions have increased. The increased collaborative nature of BIM, external data sharing techniques and progressively complex building design, promotes requirements for design teams to coordinate and communicate more effectively to achieve project goals. To manage this collaboration, new or evolved job roles may emerge. This research examined the current use of BIM, Integrated Project Delivery (IPD) and collaborative working in the UK AEC industry and job roles that have evolved or been created to cater for them. Using semi-structured interviews the interviewees indicated while several of the key enablers of IPD were being used, IPD itself had not been fully adopted. BIM was being used with some success but improvements could be made. New job roles such as the BIM Engineer and BIM Coordinator had been seen in the industry and evidence that the Architectural Technologist (AT) role is evolving into a more multidisciplinary role; this reflects similar findings of recent research

Evidence-based design utilized in hospital architecture and changing the design process: a hospital case study

As a new paradigm in healthcare design in the 21st century, evidence-based design (EBD) has played a critical role in the changing hospital architectural design process and shaping new images of hospital architecture. Evidence-based design is research informed, and its results affect not only patients' clinical outcomes but also medical facility operational efficiency and its staff retention and satisfaction. This research investigated how EBD was implemented in hospital architectural design and how traditional design process was modified to incorporate credible research evidence through a case study at Grand River Hospital in the United States. This study took a qualitative approach with grounded theory methodology. The methods used for this research were multiple sources of data collection through document reviews, observations, and interviews. Findings revealed that the investigation for EBD needs to focus on environment-behavior studies especially in the development of explanatory theory. This study also recommended a modified cyclical design process model for integrating EBD. This redefined design process model requires collaborations with all stakeholders by adding visioning sessions, multiple design charrettes, mock-ups, and the functional performance evaluation to help to implement research evidence and make design decisions to achieve the best possible outcomes

Mitigating Bias in Organizational Development and Use of Artificial Intelligence

We theorize why some artificial intelligence (AI) algorithms unexpectedly treat protected classes unfairly. We hypothesize that mechanisms by which AI assumes agencies, rights, and responsibilities of its stakeholders can affect AI bias by increasing complexity and irreducible uncertainties: e.g., AI’s learning method, anthropomorphism level, stakeholder utility optimization approach, and acquisition mode (make, buy, collaborate). In a sample of 726 agentic AI, we find that unsupervised and hybrid learning methods increase the likelihood of AI bias, whereas “strict” supervised learning reduces it. Highly anthropomorphic AI increases the likelihood of AI bias. Using AI to optimize one stakeholder’s utility increases AI bias risk, whereas jointly optimizing the utilities of multiple stakeholders reduces it. User organizations that co-create AI with developer organizations instead of developing it in-house or acquiring it off-the-shelf reduce AI bias risk. The proposed theory and the findings advance our understanding of responsible development and use of agentic AI