Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care.

Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability.

A computable biomedical knowledge object for calculating in-hospital mortality for patients admitted with acute myocardial infarction.

Introduction: Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data.

Evidence Hub: A place to exchange medical knowledge and form communities.

Introduction: Medical knowledge is complex and constantly evolving, making it challenging to disseminate and retrieve effectively. To address these challenges, researchers are exploring the use of formal knowledge representations that can be easily interpreted by computers.

Methods: Evidence Hub is a new, free, online platform that hosts computable clinical knowledge in the form of "Knowledge Objects".

A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?

Background: Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools.

Discussion: We discuss that the potential reason for the slow adoption of machine learning tools into systematic reviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major barriers to adoption.

Still moving toward automation of the systematic review process: a summary of discussions at the third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR).

The third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 17-18 October 2017 in London, England. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and widespread acceptance of automated techniques for systematic reviews.

Ecological effects of cefepime use during antibiotic cycling on the Gram-negative enteric flora of ICU patients.

This study examines the impact of cefepime and APP-β (antipseudomonal penicillin/ β-lactamase inhibitor combinations) on Gram-negative bacterial colonization and resistance in two Australian ICUs. While resistance did not cumulatively increase, cefepime (but not APP-β treatment) was associated with acquisition of antibiotic resistant Enterobacteriaceae, consistent with an ecological effect. Analysis of the resident gut E.

Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR).

Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply.

Automated screening of research studies for systematic reviews using study characteristics.

Background: Screening candidate studies for inclusion in a systematic review is time-consuming when conducted manually. Automation tools could reduce the human effort devoted to screening. Existing methods use supervised machine learning which train classifiers to identify relevant words in the abstracts of candidate articles that have previously been labelled by a human reviewer for inclusion or exclusion.

Automated annotation of mobile antibiotic resistance in Gram-negative bacteria: the Multiple Antibiotic Resistance Annotator (MARA) and database.

Background: Multiresistance in Gram-negative bacteria is often due to acquisition of several different antibiotic resistance genes, each associated with a different mobile genetic element, that tend to cluster together in complex conglomerations. Accurate, consistent annotation of resistance genes, the boundaries and fragments of mobile elements, and signatures of insertion, such as DR, facilitates comparative analysis of complex multiresistance regions and plasmids to better understand their evolution and how resistance genes spread.

Objectives: To extend the Repository of Antibiotic resistance Cassettes (RAC) web site, which includes a database of 'features', and the Attacca automatic DNA annotation system, to encompass additional resistance genes and all types of associated mobile elements.

Moving toward the automation of the systematic review process: a summary of discussions at the second meeting of International Collaboration for the Automation of Systematic Reviews (ICASR).

The second meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 3-4 October 2016 in Philadelphia, Pennsylvania, USA. ICASR is an interdisciplinary group whose aim is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. Having automated tools for systematic review should enable more transparent and timely review, maximizing the potential for identifying and translating research findings to practical application.

Linking clinical quality indicators to research evidence - a case study in asthma management for children.

Background: Clinical quality indicators are used to monitor the performance of healthcare services and should wherever possible be based on research evidence. Little is known however about the extent to which indicators in common use are based on research. The objective of this study is to measure the extent to which clinical quality indicators used in asthma management in children with outcome measurements can be linked to results in randomised controlled clinical trial (RCT) reports.

Linking quality indicators to clinical trials: an automated approach.

Objective: Quality improvement of health care requires robust measurable indicators to track performance. However identifying which indicators are supported by strong clinical evidence, typically from clinical trials, is often laborious. This study tests a novel method for automatically linking indicators to clinical trial registrations.

Evaluation of a rule-based method for epidemiological document classification towards the automation of systematic reviews.

Introduction: Most data extraction efforts in epidemiology are focused on obtaining targeted information from clinical trials. In contrast, limited research has been conducted on the identification of information from observational studies, a major source for human evidence in many fields, including environmental health. The recognition of key epidemiological information (e.

Measuring the effects of computer downtime on hospital pathology processes.

Objective: To introduce and evaluate a method that uses electronic medical record (EMR) data to measure the effects of computer system downtime on clinical processes associated with pathology testing and results reporting.

Materials And Methods: A matched case-control design was used to examine the effects of five downtime events over 11-months, ranging from 5 to 300min. Four indicator tests representing different laboratory workflows were selected to measure delays and errors: potassium, haemoglobon, troponin and activated partial thromboplastin time.

Comparing clinical quality indicators for asthma management in children with outcome measures used in randomised controlled trials: a protocol.

Introduction: Clinical quality indicators are necessary to monitor the performance of healthcare services. The development of indicators should, wherever possible, be based on research evidence to minimise the risk of bias which may be introduced during their development, because of logistic, ethical or financial constraints alone. The development of automated methods to identify the evidence base for candidate indicators should improve the process of indicator development.

Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter.

The manner in which people preferentially interact with others like themselves suggests that information about social connections may be useful in the surveillance of opinions for public health purposes. We examined if social connection information from tweets about human papillomavirus (HPV) vaccines could be used to train classifiers that identify anti-vaccine opinions. From 42,533 tweets posted between October 2013 and March 2014, 2,098 were sampled at random and two investigators independently identified anti-vaccine opinions.

Citations alone were enough to predict favorable conclusions in reviews of neuraminidase inhibitors.

Objectives: To examine the use of supervised machine learning to identify biases in evidence selection and determine if citation information can predict favorable conclusions in reviews about neuraminidase inhibitors.

Study Design And Setting: Reviews of neuraminidase inhibitors published during January 2005 to May 2013 were identified by searching PubMed. In a blinded evaluation, the reviews were classified as favorable if investigators agreed that they supported the use of neuraminidase inhibitors for prophylaxis or treatment of influenza.

Financial conflicts of interest and conclusions about neuraminidase inhibitors for influenza: an analysis of systematic reviews.

Background: Industry funding and financial conflicts of interest may contribute to bias in the synthesis and interpretation of scientific evidence.

Objective: To examine the association between financial conflicts of interest and characteristics of systematic reviews of neuraminidase inhibitors.

Design: Retrospective analysis.

Automatic evidence retrieval for systematic reviews.

Background: Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing's effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious.

Systematic review automation technologies.

Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects.

Citation networks of related trials are often disconnected: implications for bidirectional citation searches.

Background And Objectives: Reports of randomized controlled trials (RCTs) should set findings within the context of previous research. The resulting network of citations would also provide an alternative search method for clinicians, researchers, and systematic reviewers seeking to base decisions on all available evidence. We sought to determine the connectedness of citation networks of RCTs by examining direct (referenced trials) and indirect (through references of referenced trials, etc) citation of trials to one another.

Gene-disease association with literature based enrichment.

Motivation: Gene set enrichment analysis (GSEA) annotates gene microarray data with functional information from the biomedical literature to improve gene-disease association prediction. We hypothesize that supplementing GSEA with comprehensive gene function catalogs built automatically using information extracted from the scientific literature will significantly enhance GSEA prediction quality.

Methods: Gold standard gene sets for breast cancer (BrCa) and colorectal cancer (CRC) were derived from the literature.

Role of citation tracking in updating of systematic reviews.

We proposed to use automatic citation tracking to enhance the retrieval of new evidence for updating Systematic Reviews (SR). We tested on a Cochrane review from 2003 (updated 2010) and retrieved 12 of the papers to be added (recall 85.7%).

The implications of biomarker evidence for systematic reviews.

Background: In Evidence-Based Medicine, clinical practice guidelines and systematic reviews are crucial devices for medical practitioners in making clinical decision. Clinical practice guidelines are systematically developed statements to support health care decisions for specific circumstances whereas systematic reviews are summaries of evidence on clearly formulated clinical questions. Biomarkers are biological measurements (primarily molecular) that are used to diagnose, predict treatment outcomes and prognosticate disease and are increasingly used in randomized controlled trials (RCT).