Barriers to task‐based information access in molecular medicine

Abstract

We analyze barriers to task-based information access in molecular medicine, focusing on research tasks, which provide task performance sessions of varying complexity. Molecular medicine is a relevant domain because it offers thousands of digital resources as the information environment. Data were collected through shadowing of real work tasks. Thirty work task sessions were analyzed and barriers in these identified. The barriers were classified by their character (conceptual, syntactic, and technological) and by their context of appearance (work task, system integration, or system). Also, work task sessions were grouped into three complexity classes and the frequency of barriers of varying types across task complexity levels were analyzed. Our findings indicate that although most of the barriers are on system level, there is a quantum of barriers in integration and work task contexts. These barriers might be overcome through attention to the integrated use of multiple systems at least for the most frequent uses. This can be done by means of standardization and harmonization of the data and by taking the requirements of the work tasks into account in system design and development, because information access is seldom an end itself, but rather serves to reach the goals of work tasks.

LifeLogging: Personal Big Data

“…There is also potential for lifelogging technologies to be used by organisations as a means of recording/logging the activities of employees, for various reasons, such as logging employee activities for legal/historical reasons, replacing manual record taking, logging information access activities as in Kumpulainen et al (2009), or potentially as a new technology to support aspects of what Stein (1995) refer to as organisational memory. The idea here is to automatically capture procedures and processes for everyday activities in the workplace.…”

“…The idea here is to automatically capture procedures and processes for everyday activities in the workplace. While this tends to have more success for office environments where we log digital activities (web usage, emails, document accesses) rather than physical ones, there are examples of recent work with healthcare workers in clinical practice who have to log their work and record their clinical notes at the end of a shift, Kumpulainen et al (2009), as well as lifelogging for other job-specific tasks, Fleck and Fitzpatrick (2006). Lifelogging has also been used in market research, targeting novel qualitative analysis based on analysis of subjects' lifelogs and the amount of exposure they have to advertisements, Hughes et al (2012).…”

“…Past work has been reported in Byrne et al (2008a); Kumpulainen et al (2009), where healthcare workers in a clinical practice would typically log their work at the end of their shift but in this case they used visual lifelogs to trigger their own recall of their day. In particular this was used in an analyses to better understand the information needs of clinicians in hospitals in Finland.…”

Approaches to socio-cultural barriers to information seeking

“…For the sake of simplicity, however, information searching is not discussed as a separate category; issues related to barriers to information searching are reviewed under the broader concept of information seeking. However, to sharpen the focus, barriers specifically related to IR were excluded (e.g., Borgman, 1996;Kumpulainen & Järvelin, 2012).…”

Knowledge Discovery from Complex High Dimensional Data

“…It is inevitable for the future biomedical domain expert to switch from the classical consumer-like role [44] to an active part in the knowledge discovery process [27,30]. However, this is not so easy, because it is well known that many biomedical research projects fail due to the technical barriers that arise to the domain experts in data integration, data handling, data processing, data visualization and analysis [1,34,43]. A survey from 2012 among hospitals from Germany, Switzerland, South Africa, Lithuania, and Albania [60] showed that only 29 % of the medical professionals were familiar with any practical application of data mining methods and tools.…”