Sensor, Signal, and Imaging Informatics in 2017.

Objective To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.Methods PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.Results The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics

Outcomes of specialist discharge coordination and intermediate care schemes for patients who are homeless: analysis protocol for a population-based historical cohort

Introduction People who are homeless often experience poor hospital discharge arrangements, reflecting ongoing care and housing needs. Specialist integrated homeless health and care provision (SIHHC) schemes have been developed and implemented to facilitate the safe and timely discharge of homeless patients from hospital. Our study aims to investigate the health outcomes of patients who were homeless and seen by a selection of SIHHC services. Methods and analysis Our study will employ a historical population-based cohort in England. We will examine health outcomes among three groups of adults: (1) homeless patients seen by specialist discharge schemes during their hospital admission; (2) homeless patients not seen by a specialist scheme and (3)admitted patients who live in deprived neighbourhoods and were not recorded as being homeless. Primary outcomes will be: time from discharge to next hospital inpatient admission; time from discharge to next accident and emergency attendance and 28-day emergency readmission. Outcome data will be generated through linkage to hospital admissions data (Hospital Episode Statistics) and mortality data for November 2013 to November 2016. Multivariable regression will be used to model the relationship between the study comparison groups and each of the outcomes. Ethics and dissemination Approval has been obtained from the National Health Service (NHS) Confidentiality Advisory Group (reference 16/CAG/0021) to undertake this work using unconsented identifiable data. Health Research Authority Research Ethics approval (REC 16/EE/0018) has been obtained in addition to local research and development approvals for data collection at NHS sites. We will feedback the results of our study to our advisory group of people who have lived experience of homelessness and seek their suggestions on ways to improve or take this work further for their benefit. We will disseminate our findings to SIHHC schemes through a series of regional workshops

RevManHAL: towards automatic text generation in systematic reviews

Background: Systematic reviews are a key part of healthcare evaluation. They involve important painstaking but repetitive work. A major producer of systematic reviews, the Cochrane Collaboration, employs Review Manager (RevMan) programme—a software which assists reviewers and produces XML-structured files. This paper describes an add-on programme (RevManHAL) which helps auto-generate the abstract, results and discussion sections of RevMan-generated reviews in multiple languages. The paper also describes future developments for RevManHAL. Methods: RevManHAL was created in Java using NetBeans by a programmer working full time for 2 months. Results: The resulting open-source programme uses editable phrase banks to envelop text/numbers from within the prepared RevMan file in formatted readable text of a chosen language. In this way, considerable parts of the review’s ‘abstract’, ‘results’ and ‘discussion’ sections are created and a phrase added to ‘acknowledgements’. Conclusion: RevManHAL’s output needs to be checked by reviewers, but already, from our experience within the Cochrane Schizophrenia Group (200 maintained reviews, 900 reviewers), RevManHAL has saved much time which is better employed thinking about the meaning of the data rather than restating them. Many more functions will become possible as review writing becomes increasingly automated

A scoping review of diabetes telemedicine research in Norway

The recent pandemic highlighted telemedicine’s potential for continuity of remote diabetes patients’ care. The study objective was to identify diabetes telemedicine services, benefits, and challenges in Norway. We searched for publications on the topic in PubMed, ScienceDirect, CINAHL, and Nora. Most of the included studies (7/15) focused on telemedicine for type 2 diabetes. Telemedicine benefits include improved self-management and cost and time effectiveness. Challenges include organizational and technical issues. To optimize the health system, telemedicine can be used for highly engaged diabetes patients. Creating clear and practical national and organizational telemedicine guidelines for diabetes management could solve the identified challenges

An Automated Text Mining Approach for Classifying Mental-Ill Health Incidents from Police Incident Logs for Data-Driven Intelligence

Data-driven intelligence can play a pivotal role in enhancing the effectiveness and efficiency of police service provision. Despite of police organizations being a rich source of qualitative data (present in less formally structured formats, such as the text logs), little work has been done in automating steps to allow this data to feed into intelligence-led policing tasks, such as demand analysis/prediction. This paper examines the use of police incident logs to better estimate the demand of officers across all incidents, with particular respect to the cases where mental-ill health played a primary part. Persons suffering from mental-ill health are significantly more likely to come into contact with the police, but statistics relating to how much actual police time is spent dealing with this type of incident are highly variable and often subjective. We present a novel deep learning based text mining approach, which allows accurate extraction of mental-ill health related incidents from police incident logs. The data gained from these automated analyses can enable both strategic and operational planning within police forces, allowing policy makers to develop long term strategies to tackle this issue, and to better plan for day-today demand on services. The proposed model has demonstrated the cross-validated classification accuracy of 89.5% on the real dataset

Using the MRC Framework for Complex Interventions to Develop Clinical Decision Support: A Case Study.

The Medical Research Council (MRC) framework for complex interventions provides useful guidance to assist with the development and evaluation of health technology interventions such as decision support. In this paper we briefly summarise a project that focused on designing a decision support intervention to assist with the recognition, assessment and management of pain in patients with dementia in an acute hospital setting. We reflect on our experience of using the MRC framework to guide our study design, and highlight the importance of considering decision support interventions as complex interventions