Access arrangements guide: key stage 2 (2011)

The 2011 key stage 2 Access arrangements guide (AAG) provides schools with information on the suitability of access arrangements to support their pupils in taking the key stage 2 tests. The AAG also provides information on how to apply for access arrangements

The digital scribe.

Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Whilst in their infancy, digital scribes are likely to evolve through three broad stages. Human led systems task clinicians with creating documentation, but provide tools to make the task simpler and more effective, for example with dictation support, semantic checking and templates. Mixed-initiative systems are delegated part of the documentation task, converting the conversations in a clinical encounter into summaries suitable for the electronic record. Computer-led systems are delegated full control of documentation and only request human interaction when exceptions are encountered. Intelligent clinical environments permit such augmented clinical encounters to occur in a fully digitised space where the environment becomes the computer. Data from clinical instruments can be automatically transmitted, interpreted using AI and entered directly into the record. Digital scribes raise many issues for clinical practice, including new patient safety risks. Automation bias may see clinicians automatically accept scribe documents without checking. The electronic record also shifts from a human created summary of events to potentially a full audio, video and sensor record of the clinical encounter. Digital scribes promisingly offer a gateway into the clinical workflow for more advanced support for diagnostic, prognostic and therapeutic tasks

Computer-Assisted versus Oral-and-Written History Taking for the Prevention and Management of Cardiovascular Disease: a Systematic Review of the Literature

Background and objectives: CVD is an important global healthcare issue; it is the leading cause of global mortality, with an increasing incidence identified in both developed and developing countries. It is also an extremely costly disease for healthcare systems unless managed effectively. In this review we aimed to: – Assess the effect of computer-assisted versus oral-and-written history taking on the quality of collected information for the prevention and management of CVD. – Assess the effect of computer-assisted versus oral-and-written history taking on the prevention and management of CVD. Methods: Randomised controlled trials that included participants of 16 years or older at the beginning of the study, who were at risk of CVD (prevention) or were either previously diagnosed with CVD (management). We searched all major databases. We assessed risk of bias using the Cochrane Collaboration tool. Results: We identified two studies. One comparing the two methods of history-taking for the prevention of cardiovascular disease n = 75. The study shows that generally the patients in the experimental group underwent more laboratory procedures, had more biomarker readings recorded and/or were given (or had reviewed), more dietary changes than the control group. The other study compares the two methods of history-taking for the management of cardiovascular disease (n = 479). The study showed that the computerized decision aid appears to increase the proportion of patients who responded to invitations to discuss CVD prevention with their doctor. The Computer-Assisted History Taking Systems (CAHTS) increased the proportion of patients who discussed CHD risk reduction with their doctor from 24% to 40% and increased the proportion who had a specific plan to reduce their risk from 24% to 37%. Discussion: With only one study meeting the inclusion criteria, for prevention of CVD and one study for management of CVD we did not gather sufficient evidence to address all of the objectives of the review. We were unable to report on most of the secondary patient outcomes in our protocol. Conclusions: We tentatively conclude that CAHTS can provide individually-tailored information about CVD prevention. However, further primary studies are needed to confirm these findings. We cannot draw any conclusions in relation to any other clinical outcomes at this stage. There is a need to develop an evidence base to support the effective development and use of CAHTS in this area of practice. In the absence of evidence on effectiveness, the implementation of computer-assisted history taking may only rely on the clinicians’ tacit knowledge, published monographs and viewpoint articles

LAS: a software platform to support oncological data management

The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data qualit

Special Libraries, April 1969

Volume 60, Issue 4https://scholarworks.sjsu.edu/sla_sl_1969/1003/thumbnail.jp

The application of aerospace technology to biomedical problems Quarterly report, 15 Jun. - 31 Aug. 1969

Applications of aerospace technology to biomedical problem

Perceptions of Electronic Health Records Effects on Staffing, Workflow, & Productivity in Community Health Centers

Significant Federal investments under the Health Information Technology for Economic and Clinical Health Act of 2009 and the Affordable Care Act have motivated many community health centers (CHCs) to implement electronic health records (EHRs) in the past few years. Evidence suggests that EHR implementation causes significant changes in how primary care clinicians spend their time and may be associated with changes in staff and facility level productivity. However, the mechanisms to explain these changes were mostly speculative. The goals of this project were to understand how, from the perspective of clinicians, support staff, and administrators, CHCs’ implementation of EHRs has changed staffing models, staff roles, and workflow, as well as the mechanisms by which EHRs influence staff productivity, coordination between providers, and quality of care. Key Questions How has EHR implementation changed staffing models in CHCs? How has EHR implementation changed staff roles and workflow in CHCs? How have these changes influenced CHC productivity and quality of care

ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation

Recent immense breakthroughs in generative models such as in GPT4 have precipitated re-imagined ubiquitous usage of these models in all applications. One area that can benefit by improvements in artificial intelligence (AI) is healthcare. The note generation task from doctor-patient encounters, and its associated electronic medical record documentation, is one of the most arduous time-consuming tasks for physicians. It is also a natural prime potential beneficiary to advances in generative models. However with such advances, benchmarking is more critical than ever. Whether studying model weaknesses or developing new evaluation metrics, shared open datasets are an imperative part of understanding the current state-of-the-art. Unfortunately as clinic encounter conversations are not routinely recorded and are difficult to ethically share due to patient confidentiality, there are no sufficiently large clinic dialogue-note datasets to benchmark this task. Here we present the Ambient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset to date tackling the problem of AI-assisted note generation from visit dialogue. We also present the benchmark performances of several common state-of-the-art approaches

Special Libraries, March 1968

Volume 59, Issue 3https://scholarworks.sjsu.edu/sla_sl_1968/1002/thumbnail.jp