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.

'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.

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.

[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.

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.

Data, Big Data, and Metadata in Anesthesiology.

The last decade has seen an explosion in the growth of digital data. Since 2005, the total amount of digital data created or replicated on all platforms and devices has been doubling every 2 years, from an estimated 132 exabytes (132 billion gigabytes) in 2005 to 4.4 zettabytes (4.4 trillion gigabytes) in 2013, and a projected 44 zettabytes (44 trillion gigabytes) in 2020. This growth has been driven in large part by the rise of social media along with more powerful and connected mobile devices, with an estimated 75% of information in the digital universe generated by individuals rather than entities. Transactions and communications including payments, instant messages, Web searches, social media updates, and online posts are all becoming part of a vast pool of data that live "in the cloud" on clusters of servers located in remote data centers. The amount of accumulating data has become so large that it has given rise to the term Big Data. In many ways, Big Data is just a buzzword, a phrase that is often misunderstood and misused to describe any sort of data, no matter the size or complexity. However, there is truth to the assertion that some data sets truly require new management and analysis techniques.

Developing mobile BIM/2D barcode-based automated facility management system.

Facility management (FM) has become an important topic in research on the operation and maintenance phase. Managing the work of FM effectively is extremely difficult owing to the variety of environments. One of the difficulties is the performance of two-dimensional (2D) graphics when depicting facilities. Building information modeling (BIM) uses precise geometry and relevant data to support the facilities depicted in three-dimensional (3D) object-oriented computer-aided design (CAD). This paper proposes a new and practical methodology with application to FM that uses an integrated 2D barcode and the BIM approach. Using 2D barcode and BIM technologies, this study proposes a mobile automated BIM-based facility management (BIMFM) system for FM staff in the operation and maintenance phase. The mobile automated BIMFM system is then applied in a selected case study of a commercial building project in Taiwan to verify the proposed methodology and demonstrate its effectiveness in FM practice. The combined results demonstrate that a BIMFM-like system can be an effective mobile automated FM tool. The advantage of the mobile automated BIMFM system lies not only in improving FM work efficiency for the FM staff but also in facilitating FM updates and transfers in the BIM environment.

Automated surveillance for healthcare-associated infections: opportunities for improvement.

Surveillance of healthcare-associated infections is a cornerstone of infection prevention programs, and reporting of infection rates is increasingly required. Traditionally, surveillance is based on manual medical records review; however, this is very labor intensive and vulnerable to misclassification. Existing electronic surveillance systems based on classification algorithms using microbiology results, antibiotic use data, and/or discharge codes have increased the efficiency and completeness of surveillance by preselecting high-risk patients for manual review. However, shifting to electronic surveillance using multivariable prediction models based on available clinical patient data will allow for even more efficient detection of infection. With ongoing developments in healthcare information technology, implementation of the latter surveillance systems will become increasingly feasible. As with current predominantly manual methods, several challenges remain, such as completeness of postdischarge surveillance and adequate adjustment for underlying patient characteristics, especially for comparison of healthcare-associated infection rates across institutions.

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.

Applying a presentation content manifest for signing clinical documents.

In this paper, we demonstrate how to digitally sign a content manifest of a presentable clinical document that contains multiple clinical data with presentations. Only one signature is needed for an entire clinical document with multiple data resources, which can reduce the computation time during signing and verifying processes. In the radiology field, a report may contain text descriptions, images, and annotations that are stored separately in different data resources. The manifest signature would be a proper means for integrity checking for all the clinical data within the manifest. The manifest signature can be extended with a trusted third party to add a digital time signature for long-term verifiability. The performance of the manifest signing compared with that of a traditional digital signing was evaluated. The new manifest signature can be used for signing different types of presentable clinical documents, such HL7 CDA documents and DICOM image reports.

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.

Use of target-date funds in 401(k) plans, 2007.

WHAT THEY ARE: Target-date funds (also called "life-cycle" funds) are a type of mutual fund that automatically rebalances its asset allocation following a predetermined pattern over time. They typically rebalance to more conservative and income-producing assets as the participant's target date of retirement approaches. WHY THEY'RE IMPORTANT AND GROWING: Of the 401(k) plan participants in the EBRI/ICI 401(k) database who were found to be in plans that offeredtarget-date funds, 37 percent had at least some fraction of their account in target-date funds in 2007. Target-date funds held about 7 percent of total assets in 401(k) plans and the use of these funds is expected to increase in the future. The Pension Protection Act of 2006 made it easier for plan sponsors to automatically enroll new workers in a 401(k) plan, and target-date funds were one of the types of approved funds specified for a "default" investment if the participant does not elect a choice. BRI/ICI 401(K) DATABASE: This study uses the unique richness of the data in the EBRI/ICI Participant-Directed Retirement Plan Data Collection Project, which has almost 22 million participants, to examine the choices and characteristics of participants whose plans offer target-date funds. EFFECT OF AGE, SALARY, JOB TENURE, AND ACCOUNT BALANCE: Younger workers are significantly more likely to invest in target-date funds than are older workers: Almost 44 percent of participants under age 30 had assets in a target-date fund, compared with 27 percent of those 60 or older. Target-date funds appeal to those with lower incomes, little time on the job, and with few assets. On average, target-date fund investors are about 2.5 years younger than those who do not invest in target-date funds, have about 3.5 years less tenure, make about $11,000 less in salary, have $25,000 less in their account, and are in smaller plans. EFFECT OF AUTOMATIC ENROLLMENT: While the EBRI/ICI database does not contain specific information on whether a 401(k) plan had automatic enrollment, this analysis was able to proxy for those who could be identified as automatically enrolled. The data show that workers who were considered to be automatically enrolled in their employer's 401(k) plan are significantly more likely to invest all their assets in a target-date fund than those who voluntarily joined, and were also less likely to have extreme all-or-nothing asset allocations to equities. EQUITY ALLOCATIONS AND FUND FAMILIES: One of the major questions surrounding target-date funds is the equity allocations that these funds use over time (the so-called "glide path") as a participant's retirement target date approaches. The glide paths of different target-date funds have significantly different shapes and starting/ending equity allocations. As of 2007, the equity allocation ranges from about 80-90 percent for 2040 funds (for workers about 30 years away from retirement), and from 26-66 percent for 2010 funds (for workers one year away from retirement)--a 40 percentage-point difference. Moreover, the fund families change their relative rank in equity allocation within the different fund years. This analysis finds that the relative rank of the equity allocation within a target-date fund does not appear to affect the percentage of participants investing all their account into that fund. Nevertheless, investors in specific fund families are more likely to invest all their assets in a single target-date fund from that family.

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.

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.

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.