Recruiting strategic human capital from MNCs-Does hiring MNC managers enable exporting in domestic firms?

Exporting is a central growth strategy for most firms and managers with international experience are instrumental for export decisions. We suggest that such managers can be hired from Multinational Corporations (MNCs). We integrate theory from strategic human capital research into models explaining export decisions. We theorize that hiring managers from MNCs increases the odds of domestic firms to start exporting and this effect depends on the similarities between hiring firms and MNCs. We hypothesize that young firms will benefit comparatively less from hiring MNC managers. In contrast, firms with internationally diverse workforces and with high degrees of hierarchical specialization will benefit the most from hiring MNC managers. We test and support these hypotheses for 474,926 domestic firms in Sweden, which we observe between 2007 and 2015.

'Big Data' informed drug development: a case for acceptability.

Data, which help inform various stages of drug product development, are increasingly being collected using newer, more novel platforms, such as mobile applications, and analysed computationally as much larger 'Big Data' data sets, revealing patterns relating to human behaviour and interactions. Medicine acceptability gauges the ability and willingness of patients to take their dosage forms. It has become a crucial human component of drug product design. Vouching for the age appropriateness of medicinal products, acceptability related data are now expected by regulatory bodies. Shifting from traditional paper-based to electronic data-gathering platforms will allow the pharmaceutical industry to collect real-world, real-time, clinically relevant data, capable of informing current and future drug product development, reducing time and cost, and setting foundations for patient-centric drug product design.

Improving medication safety practice at a community hospital: a focus on bar code medication administration scanning and pain reassessment.

INTRODUCTION: In the USA over 30% of medication errors occur at the point of administration. Among non-surgical patients in US hospitals exposed to opioids, 0.6% experience a severe opioid-related adverse event. In September 2018, Sierra View Medical Center identified two areas of opportunity for quality improvement: bedside bar code medication administration (BCMA) and pain reassessments. At baseline (April 2018 to September 2018) only 81% of medications were scanned prior to administration with pain reassessments completed only 41% of the time 1 hour postopioid administration. OBJECTIVE: To improve BCMA scanning rates (goal ≥95%) and pain reassessments within 1 hour postopioid administration (goal ≥90%). METHODS: Implementation methods included data transparency, weekly dashboards, education and plan-do-study-act (PDSA) cycles informed by feedback from key stakeholders. RESULTS: Following a series of PDSA cycle implementations, barcode medication administration (BCMA) scanning rates improved by 14% (from 81% to 95%) and pain reassessments improved by 50% (from 41% to 91%), sustained 17 months postproject implementation (October 2018 to February 2019). The number of adverse drug events (ADEs) related to administration errors decreased by 17% (estimated annual cost savings of $120 750-239 725 per year) and opioid-related ADEs decreased by 2.6% (estimated annual cost savings of $72 855-80 928 per year). CONCLUSION: Adopting John Kotter's model for change, developing performance dashboards and sustaining engagement among stakeholders on a weekly basis improved bar code medication scanning rates and pain reassessment compliance. The stakeholders created momentum for change in both practice and culture resulting in improved patient safety with a favourable financial impact.

La maestra de educación física en los libros de texto de primaria / The Teacher of Physical Education in Primary Textbooks

El objetivo del presente estudio es analizar cómo se representa a la maestra de educación física en los libros de texto de esta área, en la etapa de Educación Primaria. Se realiza un estudio cuantitativo que muestra un número elevado de maestras con respecto al maestro de educación física, principalmente en el primer ciclo de Primaria y otro cualitativo, a través del análisis de contenido, identificando un tratamiento estereotipado en cuanto a las características físicas de la docente, al representar a una maestra eminentemente joven, de raza blanca, delgada, con ropa deportiva y sin discapacidad. Pero a la vez, aparece un tratamiento más abierto y participativo en cuanto al rol representado, ya que la maestra de educación física aparece en la zona periférica de la imagen, con actitud activa, en un espacio no necesariamente deportivo, sin complementos docentes, que imparte contenidos relacionados con la gestión del grupo-clase y los juegos

The objective of the present study is to analyse how the physical education teacher is represented in the textbooks of this area, in the Primary Education stage. A quantitative study is carried out showing a high number of teachers with respect to the physical education teacher, mainly in the first cycle of Primary Education and a qualitative one, through content analysis, identifying a stereotyped treatment of the physical characteristics of the teacher, by representing a teacher eminently young, white, thin, wearing sports clothes and without disabilities. But at the same time, a more open and participative treatment appears in relation to the role represented, since the physical education teacher appears in the peripheral area of the image, with an active attitude, in a space not necessarily sports, without teaching complements, that Imparts content related to group-class management and games

Technological Advances in Clinical Definition and Surveillance Methodology for Surgical Site Infection Incorporating Surgical Site Imaging and Patient-Generated Health Data.

Background: Surgical site infection (SSI) continues to be a common and costly complication after surgery. The current commonly used definitions of SSI were devised more than two decades ago and do not take in to account more modern technology that could be used to make diagnosis more consistent and precise. Patient-generated health data (PGHD), including digital imaging, may be able to fulfill this objective. Methods: The published literature was examined to determine the current state of development in terms of using digital imaging as an aide to diagnose SSI. This information was used to devise possible methodology that could be used to integrate digital images to more objectively define SSI, as well as using these data for both surveillance activities and clinical management. Results: Digital imaging is a highly promising means to help define and diagnose SSI, particularly in remote settings. Multiple groups continue to actively study these emerging technologies, however, present methods remain based generally on subjective rather than objective observations. Although current images may be useful on a case-by-case basis, similar to physical examination information, integrating imaging in the definition of SSI to allow more automated diagnosis in the future will require complex image analysis combined with other available quantified data. Conclusions: Digital imaging technology, once adequately evolved, should become a cornerstone of the criteria for both the clinical and surveillance definitions of SSI.

A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images.

Background: Emerging technologies such as smartphones and wearable sensors have enabled the paradigm shift to new patient-centered healthcare, together with recent mobile health (mHealth) app development. One such promising healthcare app is incision monitoring based on patient-taken incision images. In this review, challenges and potential solution strategies are investigated for surgical site infection (SSI) detection and evaluation using surgical site images taken at home. Methods: Potential image quality issues, feature extraction, and surgical site image analysis challenges are discussed. Recent image analysis and machine learning solutions are reviewed to extract meaningful representations as image markers for incision monitoring. Discussions on opportunities and challenges of applying these methods to derive accurate SSI prediction are provided. Conclusions: Interactive image acquisition as well as customized image analysis and machine learning methods for SSI monitoring will play critical roles in developing sustainable mHealth apps to achieve the expected outcomes of patient-taken incision images for effective out-of-clinic patient-centered healthcare with substantially reduced cost.

Automated continuous noninvasive ward monitoring: future directions and challenges.

Automated continuous noninvasive ward monitoring may enable subtle changes in vital signs to be recognized. There is already some evidence that automated ward monitoring can improve patient outcome. Before automated continuous noninvasive ward monitoring can be implemented in clinical routine, several challenges and problems need to be considered and resolved; these include the meticulous validation of the monitoring systems with regard to their measurement performance, minimization of artifacts and false alarms, integration and combined analysis of massive amounts of data including various vital signs, and technical problems regarding the connectivity of the systems.

Data integration of electronic medical record under administrative decentralization of medical insurance and healthcare in China: a case study.

In most regions of China, Electronic Medical Record (EMR) systems in hospitals are developed in an uncoordinated manner. Medical Insurance and Healthcare Administration are localised and organizations gather data from a functional management viewpoint without consideration of wider information sharing. Discontinuity of data resources is serious. Despite the government's repeated emphasis on EMR data integration, little progress has been made, causing inconvenience to patients, but also significantly hindering data mining.This exploratory investigation used a case study to identify bottlenecks of data integration and proposes countermeasures. Interviews were carried out with 27 practitioners from central and provincial governments, hospitals, and related enterprises in China. This research shows that EMR data collection without patients' authorization poses a major hazard to data integration. In addition, non-uniform information standards and hospitals' unwillingness to share data are also significant obstacles to integration. Moreover, friction caused by the administrative decentralization, as well as unsustainability of public finance investment, also hinders the integration of data resources.To solve these problems, first, a protocol should be adopted for multi-stakeholder participation in data collection. Administrative authorities should then co-establish information standards and a data audit mechanism. Finally, measures are proposed for expanding data integration for multiplying effectiveness and adopting the Public-Private Partnerships model.

'Fit-for-purpose?' - challenges and opportunities for applications of blockchain technology in the future of healthcare.

Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin - the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as 'revolutionary' and 'disruptive', is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is 'fit-for-purpose' is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

Statistical analysis of proteomics data: A review on feature selection.

The spread of "-omics" strategies has strongly changed the way of thinking about the scientific method. Indeed, managing huge amounts of data imposes the replacement of the classical deductive approach with a data-driven inductive approach, so to generate mechanistical hypotheses from data. Data reduction is a crucial step in the process of proteomics data analysis, because of the sparsity of significant features in big datasets. Thus, feature selection methods are applied to obtain a set of features based on which a proteomics signature can be drawn, with a functional significance (e.g., classification, diagnosis, prognosis). In this frame, the aim of the present review article is to give an overview of the methods available for proteomics data analysis, with a focus on biomedical translational research. Suggestions for the choice of the most appropriate standard statistical procedures are presented to perform data reduction by feature selection, cross-validation and functional analysis of proteomics profiles. SIGNIFICANCE: The proteome, including all so-called "proteoforms", represents the highest level of complexity of biomolecules when compared to the other "-omes" (i.e., genome, transcriptome). For this reason, the use of proper data reduction strategies is mandatory for proteomics data analysis. However, the strategies to be employed for feature selection must be carefully chosen, since many different approaches exist based on both input data and desired output. So far, a well-established decision-making workflow for proteomics data analysis is lacking, opening up to misleading and incorrect data analysis and interpretation. In this review article many statistical approaches are described and compared for their application in the field of biomedical research, in order to suggest the reader the most suitable analysis pathway and to avoid mistakes.

e-Roster policy: Insights and implications of codifying nurse scheduling.

Following a decade of dissemination, particularly within the British National Health Service, electronic rostering systems were recently endorsed within the Carter Review. However, electronic rostering necessitates the formal codification of the roster process. This research investigates that codification through the lens of the 'Roster Policy', a formal document specifying the rules and procedures used to prepare staff rosters. This study is based upon analysis of 27 publicly available policies, each approved within a 4-year period from January 2010 to July 2014. This research finds that, at an executive level, codified knowledge is used as a proxy for the common language and experience otherwise acquired on a ward through everyday interaction, while at ward level, the nurse rostering problem continues to resist all efforts at simplification. Ultimately, it is imperative that executives recognise that electronic rostering is not a silver bullet and that information from such systems requires careful interpretation and circumspection.

[E-Health and reality - what are we facing in patient care?] / E-Health und die Realität ­ was sehen wir heute schon in der Klinik?

The terms e­Health and digitization are core elements of a change in our time. The main drivers of this change - in addition to a dynamic market - are the serious advantages for the healthcare sector in the processing of tasks and requirements. The large amounts of data, the intensively growing medical knowledge, the rapidly advancing technological developments and the goal of a personalized, customized therapy for the patient, make the application absolutely necessary. While e­Health describes the use of information and communication technologies in healthcare, the concept of digitization is associated with the underlying processes of change and innovation. Digital technologies include software and hardware based developments. The term clinical data intelligence describes the property of workability and also characterizes the collaboration of clinically relevant systems with which the medical user works. The hierarchy in digital processing maps the levels from pure data management through clinical decision support to automated process flows and autonomously operating units. The combination of patient data management and clinical decision support proves its value in terms of error reduction, prevention, quality and safety, especially in drug therapy. The aim of this overview is the presentation of the existing reality in medical centers with perspectives derived from the point of view of the medical user.

Large-scale retrieval for medical image analytics: A comprehensive review.

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis.

Disrupción digital en salud, ¿es este el momento de la rehabilitación médica? / Digital disruption in health: has the time come for medical rehabilitation?

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Assessing the impact of the introduction of an electronic hospital discharge system on the completeness and timeliness of discharge communication: a before and after study.

BACKGROUND: Hospital discharge summaries are a key communication tool ensuring continuity of care between primary and secondary care. Incomplete or untimely communication of information increases risk of hospital readmission and associated complications. The aim of this study was to evaluate whether the introduction of a new electronic discharge system (NewEDS) was associated with improvements in the completeness and timeliness of discharge information, in Nottingham University Hospitals NHS Trust, England. METHODS: A before and after longitudinal study design was used. Data were collected using the gold standard auditing tool from the Royal College of Physicians (RCP). This tool contains a checklist of 57 items grouped into seven categories, 28 of which are classified as mandatory by RCP. Percentage completeness (out of the 28 mandatory items) was considered to be the primary outcome measure. Data from 773 patients discharged directly from the acute medical unit over eight-week long time periods (four before and four after the change to the NewEDS) from August 2010 to May 2012 were extracted and evaluated. Results were summarised by effect size on completeness before and after changeover to NewEDS respectively. The primary outcome variable was represented with percentage of completeness score and a non-parametric technique was used to compare pre-NewEDS and post-NewEDS scores. RESULTS: The changeover to the NewEDS resulted in an increased completeness of discharge summaries from 60.7% to 75.0% (p < 0.001) and the proportion of summaries created under 24 h from discharge increased significantly from 78.0% to 93.0% (p < 0.001). Furthermore, five of the seven grouped checklist categories also showed significant improvements in levels of completeness (p < 0.001), although there were reduced levels of completeness for three items (p < 0.001). CONCLUSION: The introduction of a NewEDS was associated with a significant improvement in the completeness and timeliness of hospital discharge communication.