Evaluating physicians’ serendipitous knowledge discovery in online discovery systems. A new approach
| DOI | https://doi.org/10.1108/AJIM-02-2019-0045 |
| Pages | 755-772 |
| Date | 18 November 2019 |
| Published date | 18 November 2019 |
| Author | Mark E. Hopkins,Oksana L. Zavalina |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
Evaluating physicians’
serendipitous knowledge
discovery in online
discovery systems
A new approach
Mark E. Hopkins
INTEGRIS Health, Oklahoma City, Oklahoma, USA, and
Oksana L. Zavalina
Department of Information Science,
University of North Texas, Denton, Texas, USA
Abstract
Purpose –A new approach to investigate serendipitous knowledge discovery (SKD) of health information is
developed and tested to evaluate the information flow-serendipitous knowledge discovery (IF-SKD) model.
The purpose of this paper is to determine the degree to which IF-SKD reflects physicians’information
behaviour in a clinical setting and explore how the information system, Spark, designed to support
physicians’SKD, meets its goals.
Design/methodology/approach –The proposed pre-experimental study design employs an adapted
version of the McCay-Peet’s (2013) and McCay-Peet et al.’s (2015) serendipitous digital environment (SDE)
questionnaire research tool to address the complexity associated with defining the way in which SKD is
understood and applied in system design. To test the IF-SKD model, the new data analysis approach
combining confirmatory factor analysis, data imputation and Monte Carlo simulations was developed.
Findings –The pilotingof the proposed novel analysis approachdemonstrated that small sampleinformation
behaviour surveydata can be meaningfully examined using a confirmatory factor analysis technique.
Research limitations/implications –This method allows to improve the reliability in measuring SKD and
the generalisability of findings.
Originality/value –This papermakes an original contributionto developing and refiningmethods and tools
of research into information-system-supported serendipitous discoveryof information by health providers.
Keywords Health professionals, Information behaviour, Serendipity, Confirmatory factor analysis,
Monte Carlo simulations, Questionnaire design
Paper type Research paper
1. Introduction
Due to the growth and complexity of the biomedical literature, as well as the increasingly
specialised nature of medicine, there is a need for advanced systems that can quickly
present information and assist physicians to discover new knowledge through serendipity.
Over the years, the idea of serendipitous knowledge d iscovery (SKD) –chance, or accidental
discovery of new knowledge –has been studied using a variety of methods. This paper
presents the data collection and data analysis approaches developed and tested by
Dr Mark E. Hopkins at the University of North Texas in 2017–2018 in the dissertation research
which sought to address the gaps in the literature and some of the important limitations of
research tools and methods used for assessing information seeking behaviour of physicians,
and more specifically serendipitous information discovery with the help of specialised Aslib Journal of Information
Management
Vol. 71 No. 6, 2019
pp. 755-772
© Emerald PublishingLimited
2050-3806
DOI 10.1108/AJIM-02-2019-0045
Received 15 February 2019
Revised 31 May 2019
4 August 2019
13 August 2019
15 August 2019
19 August 2019
Accepted 28 August 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
The authors would like to thank the experts who greatly contributed to developing and
refining this methodological approach and to implementation of the study in which it was tested:
Drs Richard Herrington, T. Elizabeth Workman and LeRoy Southmayd.
755
Online
discovery
systems: a new
approach
information systems in a clinical care setting. In this research, SKD has been defined as “the
chance, or accidental discovery of new knowledge, where its encountering happens without the
expressed or known information of interest at the time of initial searching/browsing”(Hopkins,
2018, p. 7). While an anticipated outcome of this research is a better understanding of the
complexity associated with defining and measuring how SKD is operationalised and applied in
research, the primary aim is to evaluate whether the Spark information system (discussed in
detail next) contributes to physicians’SKD and if the information flow-serendipitous
knowledge discovery (IF-SKD) model recently proposed by Workman and colleagues (2014) is
a good representation of this type of information behaviour.
2. Literature review
While the concept of serendipity has been present in the literature since the 1960s
(e.g. Bernier, 1960), its targeted study has shown enormous growth in the literature in the
past 20 years (Erdelez et al., 2016). According to Agarwal (2015, “literature review”), SKD is
a logical extension of Wilson’s (1999) nested model of information behaviour. Information
behaviour refers to the information seeking, information needs, and gaps encountered in
information acquisition. Information behaviour models seek to explain how humans interact
with information, whether that be in their daily life or through online systems (Case and
Given, 2016). There are numerous types of information behaviour models, and several of
them focus on the idea of SKD, such as information encountering and accidental information
discovery (e.g. Erdelez, 1997; McCay-Peet and Toms, 2010).
Because of serendipity’s elusive and unpredictable nature, SKD is challenging to
understand within existing information behaviour models (Foster and Ford, 2003). Multiple
factors such as age, education, task, personality, information need and prior knowledge
influence SKD (Burkell et al., 2012; Heinström, 2006; Spink, 2004). Yet, despite these
fundamental complexities, the study of SKD is vital in today’s information world. It is
particularly important for the health information domain: for example, in a recent review
that looked at the opportunities to utilise existing scientific knowledge to assist with the
identification of new drugs to treat diseases, and the costs often associated with these
endeavours, Prasad et al. (2016) noted that serendipity was, and remains, an integral factor
in many major drug discoveries.
Physicians’information behaviour research is quite rich. Gorman (1995) identified five
pieces of “information used”–data that play a role in physician information behaviour:
patient data, population statistics, medical knowledge, logistical information and social
influences. He also categorised physicians’information needs as recognised, pursued,
satisfied or unrecognised (this unrecognised information need relates to SKD). Studies
demonstrate that physicians look for information such as treatment modalities, procedures,
equipment and medication (Case and Given, 2016). Capturing how physicians find this type
of information and use it is challenging (Chen et al., 2006). For example, in addition to busy
and complex routines, physicians were found to face the information overload which occurs
when “information received becomes more of a hindrance rather than a help when the
information is potentially useful”(Bawden et al., 1999).
The format, presentation, access and modes of using information have changed greatly
over the past 30 years, with a strong move towards utilising electronic resources to answer
clinical questions. However, studies consistently demonstrate that physicians prefer
colleagues and textbooks as sources of information over electronic information resources,
and note challenges related to usability and ease of access to the latter (e.g. studies reviewed
by Younger, 2010). The narrative nature of physician questions makes it difficult to express
their information need as a query that is understandable by information systems. This
narrative nature and complexity may be part of the reason why physicians rely on human
sources of information (Gorman, 1995, 1999; Clarke et al., 2013).
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