dataesr/scientific-paragraphs-categorization huggingface dataset:
Julie Burn and Andrew Sims, Newcastle University, Newcastle-upon-Tyne Introduction Transcatheter aortic valve implantation (TAVI) is a minimally invasive method of replacing the aortic valve in patients with aortic stenosis who are considered inoperable or high risk for surgical aortic valve replacement. The aim of this study was to estimate in-patient NHS resource usage following TAVI procedures using routine hospital administrative data from the Hospital Episode Statistics (HES) database in England. Methods All in-patient finished consultant episodes of care (FCEs) relating to patients undergoing TAVI between 01/04/2009 and 31/03/2014 were extracted for the period from the TAVI admission until 1 year after the TAVI discharge date. FCEs were aggregated into admissions, and for each admission, administrative details, procedure codes, and diagnosis codes were analysed.
This provided information on the dates of admission/discharge, the reason for admission (ICD-10 code), the urgency of admission, whether the admission was due to a complication, length of hospital stay, and the procedures undertaken during the admission (OPCS codes). The pseudonymised patient identifiers were extracted and linked with mortality data from the Office of National Statistics up to 31/03/2015. A Kaplan-Meier survival analysis of time to death was performed. results After exclusions, 4874 patients undergoing TAVI between 01/04/2009 and 31/03/2014 were identified from the HES database. During the TAVI admission, the median [interquartile range] length of stay was 8 [6:14] days, and 216 (4. 43%) patients died. In the 30 day period following TAVI discharge, there were 1602 readmissions by 1205 patients (25.
years). Discussion This is the first study to estimate the in-patient resource usage of a national cohort of TAVI patients in the NHS for the year following the procedure, and may provide useful reference information for commissioners and clinicians involved in decision making. The methods developed provide a technique of tracking patients longitudinally following procedures through the HES database to gather useful clinical information, and are generalisable to other procedures. A limitation is that the data are dependent on the accuracy of clinical coding, which has been shown to vary between providers.
2 Method To collect data and define system requirements a literature review and a field study were conducted which resulted in both quantitative and qualitative data. The data was analyzed to understand the data flow and clinical processes all with a purpose to enable a user keeping in touch with the whole hospital information system. To be able to utilize the users' skills and experiences within their domain, it was important to include them in the participatory design process. To get feedback on the concept, medical staff was given the screens together with explanation of the concept based on several levels of functionality. results Proposed user interface enables entry of data specific for adverse events of the knee and hip implants. Besides the patient data, the system allows entry of the event classification (serious, non-serious) and treatment, as well as the connection of the database maintained within the Helse Bergen hospital system.
Interviews followed, in order to assist the interpretation of the questionnaire's findings. results 35 healthcare professionals answered the survey. Analysis of the questionnaire results offered a proposed dataset of PGHD for MS. The interviews revealed challenges (legislative, organizational) and opportunities (innovative solution) of tracking a disease like MS with the usage of WDs. conclusion Several challenges exist in the usage of PGHD. WDs alone cannot cover the measurements for a disease like MS and applications for patients' reporting are needed as well. Introduction Electronic health records (EHRs) contain vast amount of clinical information, with free text clinical narratives used as a routine medium to record details of patient care. The use of Text Mining (TM) methods in the clinical domain has therefore become increasingly important in order to extract knowledge from large volumes of EHRs.
Discussion The scores are comparable to the best submissions on both the i2b2 2006 and 2014 challenges, demonstrating that the system is robust and applicable to clinical narrative coming from different healthcare disciplines. The main challenge proved to be the identification of the generic class of smoker, given the label is ambiguous (it does not discriminate between current/past smokers) and there are only small number of records with this label in each dataset. Similarly, some expressions (e. g. "He has a smoking history") are ambiguous and it is difficult to differentiate whether such expressions refer to a current or past smoker (or it should be just smoker). conclusion We have demonstrated that a rule-based system can be used to extract social factors from clinical narratives with high performance.
There is a need for rigorous evaluation studies to help inform mHealth implementation designs and practices. mHealth implementation should be funded within the mainstream eHealth strategies and efforts should be directed to overcome the weakness of conventional HIS practices to accommodate mHealth innovations. Similarly, technical integration of mHealth into mainstream HIS strategy should take place concurrently with revisiting of non-technical determinants of HIS performances. Introduction Primary care in the U. K. generates an extraordinary amount of data creating unrivalled opportunities for research. Some of this data however, is underused, unknown and historically only accessed by certain institutions or experienced researchers. The aims of this project were to systematically identify, describe and facilitate access to primary care datasets in England, Scotland, Wales and Northern Ireland and to present the information found in an accessible format for all researchers.
Method We searched for relevant datasets via a variety of methods. Methods included reviewing previously compiled but unpublished lists of datasets, searching the internet using the search terms 'primary care' 'general practice' 'GP' 'data' and 'datasets' and identifying datasets which use primary care electronic record systems e. g. EMIS and Vision to extract data. Metadata were mostly taken from comprehensive website searches. Completed metadata templates were sent out via email to data custodians to verify the information prior to publication. results We identified more than 30 datasets divided into the following categories: electronic medical record data, quality of primary care services, prescribing data, audit, health surveys, special datasets, cohort studies, administrative datasets and screening datasets.
The largest datasets were electronic medical record data linked to primary care electronic medical system e. g. ResearchOne incorporating over 30 million registered patients. The smallest datasets were audits and surveys, e. g. the health survey for Northern Ireland which included around 4000 patients in 2014/5. Metadata included type of data, context of data and method of extraction, coverage, geography, duration, volume, events for data collected, granularity, coding, consent and access. Example publications using the data were included for the majority of datasets listed. The majority of dataset custodians replied to our enquiry and verified the information.
Discussion Our search identified a large number of datasets which may be useful to primary care researchers including major popular datasets, and less well known resources. Identifying peer reviewed publications which have used the datasets allowed us to highlight the variety of research that can be carried out with routinely collected data. We used a template similar to that on the Health Data Finder website to ensure consistency of metadata. conclusion This project fills a gap in the literature regarding the existence and availability of primary health care datasets and is intended to be used as a starting point for researchers. It is a useful resource for both established primary care researchers who have current or historic links to particular datasets, as well as potential users who may not be aware of the existence of such a large variety of datasets or have previously encountered barriers to access.
• To assess the usability of a remote home services, offering services such a personal calendar management, electronic messaging and remote monitoring of the delivery of home services, though the home TC monitor and health care professionals PC. To perform a process evaluation including the usability, feasibility, acceptability and implementation fidelity of the Physiodom HDIM intervention among the elderly and involved care-givers. Introduction Different disciplines in multiple research centres need to collect a large amount of various data, which commonly spends a lot of cost and time. Normative collection of necessary data in every field will increase speed and precision of research. Regard of registry Importance, the researchers tried to create a maximum comprehensive dataset for a multidisciplinary registry for breast cancer Iranian Breast Cancer Patients.
Methods First, the scientific literature and published studies were systematically reviewed and their related data elements were extracted. The results were presented to specialists in different disciplines to give their comments. The next phase was the survey of experts on a three-step Delphi, which had an expert panel in the last step. Finally, a combination of scientific experts and scientific literature review was used. result In the first step, 194 component (criteria) in 11 specialized (specific) breast cancer-related fields were extracted and sent to experts. In the first Delphi step the experts added 37 components, 11 elements were duplicates and not particularly helpful. Finally, the maximum dataset was confirmed by members of the Expert Panel with 220 independent data elements in 11 specialized multidisciplinary fields in breast cancer.
Introduction In collaboration with Public Health England's (PHE) initiative to improve the management of Atrial Fibrillation (AF) and reduce the number of avoidable AF-related strokes, the Greater Manchester (GM) Academic Health Science Network (AHSN) is harnessing insights from publicly available data across the AF care pathway. The initiative is aligned with NICE clinical guidelines (CG180, 2014), which outline best practice to improve standards of care in AF, including anticoagulation for patients at high risk of stroke. Publicly available datasets capture different segments of the AF care pathway through a range of reporting routes and at different time points -from primary care identification and management of AF, to secondary care and emergency admissions for AF-related stroke. Methods GM AHSN has developed a first-of-its-kind online data capture and visualisation platform in AF that effectively integrates these datasets to gain the most complete evidential picture to-date across the care pathway.
This includes up-to-date Quality Outcomes Framework (QOF) and GRASP-AF reporting, and Hospital Episode Statistics (HES) and Sentinel Stroke National Audit Programme (SSNAP) data. The AF-and AF-related Stroke Data Landscape Tool incorporates data from all 15 AHSNs in the UK and their memberships, enabling each CCG to pinpoint local opportunities for improved AF diagnosis and/or anticoagulation. As reporting cycles of these key datasets are not synchronised with one another, the AF Landscape Tool has been designed to easily incorporate new data releases on either a rolling or an ad hoc basis. results Based on insights collected from these datasets, each AHSN is able to support the creation of specific business cases for the improvement of AF care in their regions. For example, the GM AHSN has created a business case for better anticoagulation in high-risk patients with AF, which predicts a return on investment within 2 years and a potential reduction in avoidable AF-related strokes in GM by 365 in 2017.
GM AHSN has also supported its member organisations to build successful business cases for AF screening programmes or treatment reviews, targeted to CCGs or individual practices that have a demonstrable need. Once piloted, successful initiatives can be scaled up to regional, and potentially national outreach. Introduction Due to data privacy issues, routinely collected data of different sources is pseudonymized (e. g. MBDS minimum basic dataset from the Federal Ministry of Health, which up to 2015 does not have a personal identifier). This makes statistical analysis for decision support and health care planning very difficult. Data from insurance carriers (FoKo) is event based: whenever a hospital reports, a new data entry is generated. To enable efficient, significant and quality assured data analysis for patient centred assertions record linkage of these episodes is required with the aim of finding a personal ID for each MBDS episode.
Discussion The main innovations of this procedure include significant improvement of previously developed methods, mainly concerning reproducibility, stability and adaptability to new data and documentation on every single step of the linkage procedure, allowing researchers to comprehend the origin of a link and adapt their data analysis strategies. Checks if the registered age differs with +/-one year is included and quality checks on the base match showed that age and district differs a lot. The procedure achieves the best possible outcome for the new datasets and is highly suitable to be used within new data. Further evaluations will be included by the end of the year. conclusion The applied determinist record linkage provided a rate of 99,94% matches. It is a further developed, structured and improved implementation of the historic matching.
Introduction The emergence of electronic medical records (EMRs) in primary care in Canada provides a unique opportunity for chronic disease surveillance. Historically, chronic disease surveillance has been difficult in the primary care setting because of the limited administrative data available within primary care. EMR databases include an abundance of rich information that, if properly harnessed, can provide an opportunity for improved chronic disease surveillance. However, the utility of the chronic disease surveillance information is dependent on the quality of the EMR data, as well as the quality of the case identification algorithm that is used. We will present an evaluation of the relative accuracy of a set of case identification algorithms built using machine learning methods as compared to a definition based on clinician expert consultation.
results This is research in progress. Preliminary results show that machine learning methods are capable of creating case definitions that are "as good or better" than committee-created case definitions in terms of their classification accuracy and that the machine learning based definitions involve fewer criteria. Methods 50 foot ulcer cases were included. Data was obtained from the Danish wound database, Pleje. net, and was collected from January 2006 to October 2016 by Danish community nurses and wound specialists. 19 different wound features were tested. The features covered characteristics related to the three wound stages: the inflammatory phase, the proliferation phase and the maturation phase. We developed a pattern prediction model to forecast individualized development of diabetic foot ulcers into one of two classes: (i) healing, and (ii) no healing, of the diabetic foot ulcer.
Abstract no. 514 factors affecting patients' use of electronic personal health records Alaa Abd-alrazaq and Hamish Fraser, Yorkshire Centre for Health Informatics, University of Leeds, Leeds   Peter Gardner, School of Psychology, University of Leeds, Leeds Introduction Electronic personal health records (ePHRs) are web-based tools that enable patients to access parts of their health records and perform other services such as booking appointments and requesting repeat medications. Despite many potential benefits of ePHRs, the adoption rate of these tools is often very low. We describe here a systematic review of the literature regarding factors that affect patients' use of ePHRs. Methods This systematic review employed five search sources: 44 bibliographic databases, hand searching, checking reference lists, contacting experts and professionals, and web searching.
Further, three groups of search terms, related to population, intervention and outcome, were used for searching databases. Detailed study eligibility criteria and forms were developed in this review. The Mixed Methods Appraisal Tool (MMAT) was used to appraise the quality of the included studies. The results of studies were synthesised narratively according to the outcome: intention to use, subjective measures of use, and objective measures of use. results 4843 titles and abstracts were screened, 245 full texts were read, and 85 publications were included in the review. According to the MMAT, the quality of qualitative studies was clearly higher than quantitative and mixed-methods studies. Among 20 studies that assessed the factors affecting patients' intention to use ePHRs, 57 factors were grouped into 4 main categories: personal factors (35 factors), human-technology interaction factors (11), organisational factors (10), and social factors  (1).
Without tooling, grasping a program's contents and keeping it consistent, considering its evolution, is challenging. This abstract discusses ongoing work on the development of an ICT tool to visualize and analyze curricula to facilitate their consistent management. Methods A literature study was performed to identify similar tools and develop an information model that provides the foundation for the tool. A prototype was developed and filled with information of the two Amsterdam programs. The prototype was used to gain insight in user experiences and formulate future improvements. results All information and visualization presented in the tool is generated from structured data in the underlying curricula database. The tool provides an overview of the courses, credit size, learning tracks, learning goals and references to IMIA topics.
32) and patients (average Likert score 4. 62) find the outpatient report to be more useful than the conventional dictated clinic letter. Introduction Administrative Health Databases (AHD) have been widely used in Italy, some dating back two decades or more. Epidemiological observations from AHD data can be useful to stakeholders to support health policies and services. AHD scope and availability for epidemiological studies in Italy are not well known or documented. A Research project from the SISMEC Working Group on Observational Studies was funded by the Italian Ministry of Health and the Puglia Region, to perform a census of Electronic ADH in Italy ('Electronic health databases as a source of reliable information for effective health policy", Project RF-2010-2315604). The project aimed at evaluating methodological issues related to the use of AHD for epidemiology, and focused on the public regional health administrations.
Albert King, Scottish Government Education Analytical Services, Edinburgh Introduction National Records of Scotland (NRS) provide the Trusted Third Party (TTP) Indexing Service on behalf of the Scottish Informatics and Linkage Collaboration, encompassing data linkage projects supported by Farr Institute Scotland, Administrative Data Research Centre -Scotland, Urban Big Data Centre and the Scottish Government. The role of NRS is to match the personal identifiers submitted by data controllers to the national research spine and generate study and datasetspecific index numbers. These indexes are used to link pseudo-anonymised records accessed by approved researchers in a safe haven. To avoid both retaining linked research datasets, and repeatedly sharing personal identifying information from datasets required in multiple projects, NRS are developing a series of "read-through" index keys, which can be re-used and facilitate safer and more efficient data linkage.
Methods NRS are in the process of agreeing a series of memorandums of understanding (MoU) with various Data Controllers of administrative datasets in order to establish safer and more efficient linkages of datasets. Under each MoU, the data controller asks NRS to process their dataset by linking it to the national research spine and creating anonymised "read-through" index keys which the Data Controller will hold at the person-level on their own dataset. NRS will maintain a look-up of the "readthrough" against the spine. This means for approved research studies involving their already indexed data, the Data Controller just needs to send the read-through keys (without any other personal identifying information) for the people comprising the study cohort, to the indexing team to generate study-specific keys in the usual manner.
It can also allow the data provider to receive the "read-through" keys and study-specific index numbers from the TTP, when the research cohort originates from a dataset held by a different data controller. results So far MoU's have been agreed with NHS Scotland for national health data , for primary care data from consenting General Practices, Scottish Government Education Analytical Services for school pupil census and Communities Analysis for housing data, University of Edinburgh for Scottish Mental Surveys, and NRS for Census and Vital Events data.
Methods We identified a set of common elementary approaches to query clinical variables from the primary care database of the Secure Anonymised Information Linkage databank. We then designed an easy-to-use web-based user interface to allow using combinations of these approaches as 'building blocks' for querying more complex variables. We created an R programme to automatically generate and execute the corresponding Structured Query Language (SQL) queries. results The developed prototype allows researchers to query clinical information from primary care databases based on the following elementary variable types: (1) count of events of interest (e. g. asthma prescriptions) or their distinct dates (2) the code or date of the earliest or latest event of interest (e. g. type of the earliest smoking cessation prescription) (3) the code or date of the event of maximum or minimum value (e.
Discussion The developed prototype provides a scalable, versatile solution for the implementation of complex cohort selection and characterisation algorithms using primary care databases. The automatic generation of SQL queries reduces human errors and should enable rapid and scalable implementation of these algorithms, which has the potential to improve research efficiency and reproducibility. In addition, the graphical user interface allows researchers with no programming skills to interrogate the data. The tool is under active development to improve the functionality and usability, and we look forward to testing it in other databases and assessing its suitability in different research contexts. We plan to make this tool available under an open source licence.
The latter can be done by linking the original participant information with data from large administrative databases. Canadian provinces provide universal health care that generates extensive records, which can be linked to the information collected in existing RCTs and cohort studies. The objective of this study is to: (a) describe the process to develop a repository in the province of Manitoba, Canada containing Introduction In order to enhance the safety and efficiency of surgery, systematic information service to support various stakeholders is necessary. A smart system supposed to comprehend whole procedure of the operation and be able to provide with intelligent service such as proactive data mining and generation of warning at appropriate timing is being developed for the support.