Gauging the Purported Costs of Public Data Archiving for Long-Term Population Studies.

It was recently proposed that long-term population studies be exempted from the expectation that authors publicly archive the primary data underlying published articles. Such studies are valuable to many areas of ecological and evolutionary biological research, and multiple risks to their viability were anticipated as a result of public data archiving (PDA), ultimately all stemming from independent reuse of archived data. However, empirical assessment was missing, making it difficult to determine whether such fears are realistic. I addressed this by surveying data packages from long-term population studies archived in the Dryad Digital Repository. I found no evidence that PDA results in reuse of data by independent parties, suggesting the purported costs of PDA for long-term population studies have been overstated.

Increasing the Impact of JMIR Journals in the Attention Economy.

The Journal of Medical Internet Research (JMIR) has attained remarkable achievements in the past twenty years. By depth, JMIR has published the most impactful research in medical informatics and is top ranked in the field. By width, JMIR has spun off to about thirty sister journals to cover topics such as serious games, mobile health, public health, surveillance, and other medical areas. With ever-increasing data and research findings, academic publishers need to be competitive to win readers' attention. While JMIR is well-positioned in the field, the journal will need more creative strategies to increase its attention base and maintain its leading position. Viable strategies include the creation of online collaborative spaces, the engagement of more diverse audience from less traditional channels, and partnerships with other publishers and academic institutes. Doing so could also enable JMIR researchers to turn research insights into practical strategies to improve personal health and medical services.

The collective impact of rare diseases in Western Australia: an estimate using a population-based cohort.

PURPOSE: It has been argued that rare diseases should be recognized as a public health priority. However, there is a shortage of epidemiological data describing the true burden of rare diseases. This study investigated hospital service use to provide a better understanding of the collective health and economic impacts of rare diseases. METHODS: Novel methodology was developed using a carefully constructed set of diagnostic codes, a selection of rare disease cohorts from hospital administrative data, and advanced data-linkage technologies. Outcomes included health-service use and hospital admission costs. RESULTS: In 2010, cohort members who were alive represented approximately 2.0% of the Western Australian population. The cohort accounted for 4.6% of people discharged from hospital and 9.9% of hospital discharges, and it had a greater average length of stay than the general population. The total cost of hospital discharges for the cohort represented 10.5% of 2010 state inpatient hospital costs. CONCLUSIONS: This population-based cohort study provides strong new evidence of a marked disparity between the proportion of the population with rare diseases and their combined health-system costs. The methodology will inform future rare-disease studies, and the evidence will guide government strategies for managing the service needs of people living with rare diseases.Genet Med advance online publication 22 September 2016.

Feasibility of extracting data from electronic medical records for research: an international comparative study.

BACKGROUND: Electronic medical records (EMR) offer a major potential for secondary use of data for research which can improve the safety, quality and efficiency of healthcare. They also enable the measurement of disease burden at the population level. However, the extent to which this is feasible in different countries is not well known. This study aimed to: 1) assess information governance procedures for extracting data from EMR in 16 countries; and 2) explore the extent of EMR adoption and the quality and consistency of EMR data in 7 countries, using management of diabetes type 2 patients as an exemplar. METHODS: We included 16 countries from Australia, Asia, the Middle East, and Europe to the Americas. We undertook a multi-method approach including both an online literature review and structured interviews with 59 stakeholders, including 25 physicians, 23 academics, 7 EMR providers, and 4 information commissioners. Data were analysed and synthesised thematically considering the most relevant issues. RESULTS: We found that procedures for information governance, levels of adoption and data quality varied across the countries studied. The required time and ease of obtaining approval also varies widely. While some countries seem ready for secondary uses of data from EMR, in other countries several barriers were found, including limited experience with using EMR data for research, lack of standard policies and procedures, bureaucracy, confidentiality, data security concerns, technical issues and costs. CONCLUSIONS: This is the first international comparative study to shed light on the feasibility of extracting EMR data across a number of countries. The study will inform future discussions and development of policies that aim to accelerate the adoption of EMR systems in high and middle income countries and seize the rich potential for secondary use of data arising from the use of EMR solutions.

Big Data: the challenge for small research groups in the era of cancer genomics.

In the past decade, cancer research has seen an increasing trend towards high-throughput techniques and translational approaches. The increasing availability of assays that utilise smaller quantities of source material and produce higher volumes of data output have resulted in the necessity for data storage solutions beyond those previously used. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective and secure manner. This requires technical solutions and administrative support not normally financially accounted for in small- to moderate-sized research groups. In this review, we highlight the Big Data challenges faced by translational research groups in the precision medicine era; an era in which the genomes of over 75,000 patients will be sequenced by the National Health Service over the next 3 years to advance healthcare. In particular, we have looked at three main themes of data management in relation to cancer research, namely (1) cancer ontology management, (2) IT infrastructures that have been developed to support data management and (3) the unique ethical challenges introduced by utilising Big Data in research.

Data's shameful neglect.

Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly.

NOR-DMARD data management: implementation of data capture from electronic health records.

OBJECTIVES: The use of electronic health records (EHR) is an essential part of modern health care, and electronic data capture (EDC) has become essential for managing clinical trials. Usually, these two entities are independent of each other, and transfer from one system to another is done manually. Our aim was to develop a method to capture data directly from the EHR system and transfer them into an EDC system for the NORwegian Disease-Modifying Anti-Rheumatic Drugs (NOR-DMARD) registry. METHODS: All rheumatology departments contributing to NOR-DMARD had implemented a structured EHR system. Data are extracted locally and securely transferred to the study data management once a month. The study data management then parse the data into a readable format for the EDC and import the data. Once the data is in the EDC, they are available to all authorized researchers and downloadable in a preferred format. RESULTS: From May 2012 to August 2014 almost 6400 visits in 3400 patients treated with biologics have been successfully registered in the EDC system. Previously, NOR-DMARD used standard paper-based case report forms (CRFs), with a substantial cost for data entry. Setting up and maintaining the EDC system required some investments, but the amount saved from avoiding paper handling has made the shift into EDC profitable. In addition to this, gains have been made in administration and data quality. CONCLUSIONS: The transition from paper and pencil format to a fully electronic data management system in NOR-DMARD has had obvious advantages regarding feasibility, cost, data quality and accessibility of the data.

A reliable, low-cost picture archiving and communications system for small and medium veterinary practices built using open-source technology.

Picture Archiving and Communications Systems (PACS) are the most needed system in a modern hospital. As an integral part of the Digital Imaging and Communications in Medicine (DICOM) standard, they are charged with the responsibility for secure storage and accessibility of the diagnostic imaging data. These machines need to offer high performance, stability, and security while proving reliable and ergonomic in the day-to-day and long-term storage and retrieval of the data they safeguard. This paper reports the experience of the authors in developing and installing a compact and low-cost solution based on open-source technologies in the Veterinary Teaching Hospital for the University of Torino, Italy, during the course of the summer of 2012. The PACS server was built on low-cost x86-based hardware and uses an open source operating system derived from Oracle OpenSolaris (Oracle Corporation, Redwood City, CA, USA) to host the DCM4CHEE PACS DICOM server (DCM4CHEE, http://www.dcm4che.org ). This solution features very high data security and an ergonomic interface to provide easy access to a large amount of imaging data. The system has been in active use for almost 2 years now and has proven to be a scalable, cost-effective solution for practices ranging from small to very large, where the use of different hardware combinations allows scaling to the different deployments, while the use of paravirtualization allows increased security and easy migrations and upgrades.

Biomedical event extraction from abstracts and full papers using search-based structured prediction.

BACKGROUND: Biomedical event extraction has attracted substantial attention as it can assist researchers in understanding the plethora of interactions among genes that are described in publications in molecular biology. While most recent work has focused on abstracts, the BioNLP 2011 shared task evaluated the submitted systems on both abstracts and full papers. In this article, we describe our submission to the shared task which decomposes event extraction into a set of classification tasks that can be learned either independently or jointly using the search-based structured prediction framework. Our intention is to explore how these two learning paradigms compare in the context of the shared task. RESULTS: We report that models learned using search-based structured prediction exceed the accuracy of independently learned classifiers by 8.3 points in F-score, with the gains being more pronounced on the more complex Regulation events (13.23 points). Furthermore, we show how the trade-off between recall and precision can be adjusted in both learning paradigms and that search-based structured prediction achieves better recall at all precision points. Finally, we report on experiments with a simple domain-adaptation method, resulting in the second-best performance achieved by a single system. CONCLUSIONS: We demonstrate that joint inference using the search-based structured prediction framework can achieve better performance than independently learned classifiers, thus demonstrating the potential of this learning paradigm for event extraction and other similarly complex information-extraction tasks.

Biomedical cloud computing with Amazon Web Services.

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.