Computing and Information Systems - Research Publications
Permanent URI for this collection
Search Results
1 - 2 of 2
-
ItemThe brain monitoring with information technology (BrainIT) collaborative network: EC feasibility study results(Springer, 2009)BACKGROUND: The BrainIT group works collaboratively on developing standards for collection and analyses of data from brain injured patients towards providing a more efficient infrastructure for assessing new health care technology. EC funding supported meetings over a year to discuss and define a core dataset to be collected with IT based methods from patients with traumatic brain injury. We now report on the results of a follow-up period of funding to test the feasibility for collection of the core dataset with IT based methods. METHODS: Over a three year period, data collection client and web-server based tools were developed and core data (grouped into 9 categories) were collected from 200 head-injured patients by local nursing staff. Data were uploaded by the BrainIT web and random samples of received data were selected automatically by computer for validation by data validation (DV) research nurse staff against gold standard sources held in the local centre. Validated data were compared with original data sent and percentage error rates calculated by data category. Feasibility was assessed in terms of the amount of missing data, accuracy of data collected and limitations reported by users of the IT methods. FINDINGS: Thirteen percent of data files required cleaning. Thirty “one-off” demographic and clinical data elements had significant amounts of missing data (> 15%). Validation nurses conducted 19,461 comparisons between uploaded database data with local data sources and error rates were generally less than or equal to 6%, the exception being the surgery data class where an unacceptably high error rate was found. Nearly 10,000 therapies were successfully recorded with start-times but approximately a third had inaccurate or missing end times which limits analyses assessing duration of therapy. Over 40,000 events and procedures were recorded but events with long durations (such as transfers) were more likely to have “end-times” missed. CONCLUSIONS: The BrainIT core dataset is a rich dataset for hypothesis generation and post-hoc analyses provided studies avoid known limitations in the dataset. Limitations in the current IT based data collection tools have been identified and have been addressed. Future academic led multi-centre data collection projects must decrease validation costs and likely will require more direct electronic access to hospital based clinical data sources for both validation purposes and for reducing the research nurse time needed for double data entry. This type of infrastructure will foster remote monitoring of patient management and protocol adherence in future trials of patient management and monitoring.
-
ItemSupporting grid-based clinical trials in Scotland(Sage, 2008)A computational infrastructure to underpin complex clinical trials and medical population studies is highly desirable. This should allow access to a range of distributed clinical data sets; support the effi cient processing and analysis of the data obtained; have security at its heart; and ensure that authorized individuals are able to see privileged data and no more. Each clinical trial has its own requirements on data sets and how they are used; hence a reusable and fl exible framework offers many advantages. The MRC funded Virtual Organisations for Trials and Epidemiological Studies (VOTES) is a collaborative project involving several UK universities specifi cally to explore this space. This article presents the experiences of developing the Scottish component of this nationwide infrastructure, by the National e-Science Centre (NeSC) based at the University of Glasgow, and the issues inherent in accessing and using the clinical data sets in a fl exible, dynamic and secure manner.