OBJECTIVES Previous studies regarding nursing documentation focused primarily on documentation quality, for instance, in terms of the accuracy of the documentation. The combination between accuracy measurements and the quality and frequencies of outcome variables such as the length of the hospital stay were only minimally addressed. METHOD An audit of 300 randomly selected digital nursing records of patients (age of >70 years) admitted between 2013-2014 for hip surgery in two orthopaedic wards of a general Dutch hospital was conducted. RESULTS Nursing diagnoses: Impaired tissue perfusion (wound), Pressure ulcer, and Deficient fluid volume had significant influence on the length of the hospital stay. CONCLUSION Nursing process documentation can be used for outcome calculations. Nevertheless, in the first generation of electronic health records, nursing diagnoses were not documented in a standardized manner (First generation 2010-2015; the first generation of electronic records implemented in clinical practice in the Netherlands).
In the last decade, organizations have re-engineered their business processes and started using standard software solutions. Integration of structured data in relational databases has improved documentation of business transactions and increased data quality. But almost 90% of the information cannot be integrated in relational data bases. This amount of ‘unstructured’ information is exploding within the Enterprise 2.0. The use of social media tools to enhance collaboration, creates corporate blogs, wikis, forums, and other types of unstructured information. Structured and unstructured information are records, meant and used as evidence for policies, decisions, products, actions and transactions. Most stakeholders are making increasing demands for the trustworthiness of records for accountability reasons. In this age of evolving social media use, organizational chains, inter-organizational data warehouses and cloud computing, it is crucial for the Enterprise 2.0. that its policies, decisions, products, actions and transactions can be reliably reconstructed in context. Digital Archiving is a necessity for the Enterprise 2.0.: the reconstruction of the past depends on records and their meta data. Blogs, wikis, forums, etc., used for collaboration within the business processes of the organization, need to be documented for reconstruction in the future. Digital Archiving is a combination of three mechanisms: enterprise records management, organizational memory and records auditing. These mechanisms ensure that a digitized organization as the Enterprise 2.0. has a documented understanding of its past. In that way, it improves organizational accountability.
The purpose of this study was to analyse knowledge management research trends to understand the development of the field using a combination of scientometric, bibliometric, and visualisation techniques, subsequently developing a normative framework of knowledge management from the results.282 articles between the years 2010–2015 were retrieved, analysed, and visualised to produce the state of knowledge management during the selected timeframe. The results of this study provide a visualisation of the current research trends to understand the development of the knowledge management discipline. There are signals that the literature about knowledge management is progressing towards academic maturity. This study is one of the first studies to combine bibliometric and scientometric methods to assess productivity along with visualisation, and subsequently provide a knowledge management framework drawing from the results of these methods.
MULTIFILE
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
