Evidence-based practice in healthcare is changing:in the past, clinicians’ knowledge was primarily learned through formal education, and recollected in the clinical-encounter. Now, knowledge is being externally sourced; the clinician accesses it with their smartphone and appropriates it at the ‘point-of-care’. The advantage of accessing information in this manner is that:
A greater quantity of information is available;
This information does not suffer from recall-inaccuracy;
This information reflects the latest research evidence;
This information can be leveraged to answer the ‘unknowns’ of daily-practice.
The essentiality of information in healthcare and the transformation in how it is being synthesised by clinicians and patients alike is evidenced by the fact that 280 million health-related searches are made on Google per day, with Wikipedia frequently
listed as one of the top results.
Presently, clinicians utilise a variety of information databases (such as Wikipedia and the Cochrane library) and search engines (such as Google Scholar and PubMed) to identify scientific information that is relevant to their information-needs. However, therate at which healthcare information is being produced has meant that clinicians have significant difficulty keeping up with new developments in their field of practice.
It is now possible to expedite information retrieval using machine learning and text-mining techniques, which aggregate multiple information resources and provide a synthesized summary for the clinician, thus fulfilling their ‘information-needs’.
‘SciScanner’ (www.sciscanner.com) is a free, web-based meta-search engine developed by a team of researchers from the Insight Centre for Data Analytics in University College Dublin. SciScanner centralises the most commonly accessed online health-information resources, including Wikipedia, Pubmed and the Cochrane Database of Systematic Reviews on one platform, synthesizing healthcare evidence and presenting it in accordance with the expectations of the ‘digital native’.
The proposed session will involve a ‘lightning talk’ demonstration of the SciScanner system, with a view to garnering feedback about how it be improved, and whether there is scope for collaboration with the Wikipedia community.
The UN’s sustainable development goal for good health and well-being explicitly challenges researchers and clinicians to strive towards achieving universal health coverage, including access to quality and essential health services.
SciScanner is an open-access system designed to address the access to “quality medical information”, derived from contemporary, curated and appraised scientific literature. This is vital in lieu of the
fact that it takes approximately 17 years for 14% of research findings to be adopted in clinical practice (Grol 2001, Westfall, Mold et al. 2007). That fewer than 50% of patients receive the recommended healthcare, 30% receive healthcare that is potentially harmful (Schuster, McGlynn et al. 1998, Grol 2001) and 96% receive healthcare with the absence of evidence of effectiveness (Mikhail, Korner-Bitensky etal. 2005) points to the importance of the proposed system.
These shortcomings in evidence implementation are underpinned by several challenges (Scott, Docking et al. 2013):
1) Time limitations to search for and appraise evidence;
2) Too much evidence being available;
3) Difficulty identifying relevant evidence;
4) The evidence being scattered through a number of databases/journals;
5) Inadequate access to the evidence (limited availability of free text);
6) Limited training in how to access the evidence;
7) Poor presentation of the evidence;
8) Limited competence and confidence in appraising the quality of the evidence.
The proposed research tackles these issues, and thus will facilitate access to quality health-care services (Goal 3.8 of the SDG for health).
1. Demonstration of SciScanner to conference delegates. The SciScanner system supplements the secondary research evidence provided on Wikipedia with primary evidence (Observational and interventional) from PubMed, and secondary evidence from the Cochrane Library (systematic reviews and meta-analyses). Therefore, it may be of value to Wikipedia editors seeking to update or improve Wikipedia articles.
2. Garner feedback about how best to iterate and develop the system, and how best to integrate Wikipedia. Initiate collaboration with interested parties, either in development or testing of the SciScanner system
Each Space at Wikimania 2019 will have specific format requests. The program design prioritises submissions which are future-oriented and directly engage the audience. The format of this submission is a: