Barriers and enablers to the acceptance of bioinformatics tools: a qualitative study

INTRODUCTION

One of the most important developments in the biomedical sciences today is the emergence of bioinformatics, the “science of managing and analyzing biological data using advanced computing techniques” [1]. The past decade has witnessed an explosion of biological data stored in large central databases as well as software tools to organize, visualize, and analyze the data [2–5], yet the acceptance and use of these applications by biologists lags behind this proliferation [6]. While some practices, such as the analysis of DNA and protein sequences, have fully diffused in the biomedical community, other bioinformatics practices still face adoption difficulties [7,8].

Medical libraries are increasingly required to provide services such as resources, training, occasional reference assistance, and individualized consultations to biomedical researchers [1,9–12] and have the potential to play a significant role in facilitating the acceptance and use of bioinformatics software by researchers. To provide effective services, medical libraries can benefit from gaining an understanding of the barriers and enablers to the acceptance of bioinformatics applications by researchers; however, at present there is a small body of literature on this topic [7–9].

A useful theoretical framework to study bioinformatics acceptance is Rogers' diffusion of innovations theory [13], adapted to the context of information systems by Moore and Benbasat [14]. Like other models of information systems diffusion [15–18], the framework suggests that perceptions of an information system play an important role in explaining end users' intentions to use a system and that intentions are predictors of actual use. Moore and Benbasat [14] propose eight perceptions, which are summarized in Table 1.

Table 1 Perceptions of information systems innovations as defined by Moore and Benbasat [14]

The authors previously studied the effect of hands-on training workshops, using either a structured step-by-step method or guided trial-and-error exploration methods, on end-user perceptions and intended use of bioinformatics tools for primer design and microarray data analysis [19]. Hands-on training positively affected perceived ease of use (PEOU) of the primer design tool; however, it decreased PEOU of the microarray data analysis tools. Surprisingly, intention to use both types of software decreased following hands-on training [19]. The present qualitative study was conducted to further increase understanding of the barriers and enablers to biomedical researchers' acceptance of bioinformatics applications, with a focus on the decision process underlying the selection of tools for primer design and microarray analysis and the long-term effect of training on these behaviors.

METHODS

Semi-structured interviews

In 2003–2004, semi-structured 60-minute interviews with a convenience sample of 12 of the 115 previously studied participants of the microarray analysis and primer design workshops were conducted 3–6 months after the workshops noted above. Although a small convenience sample was employed, the study participants represented the diversity of the biomedical community. Interviewees included 6 doctoral students, a post-doctoral fellow, a laboratory worker, a faculty member, a scientific manager for a biotechnological company, a manager of a genetics laboratory in a public hospital, and a member of a unit that provides DNA sequencing services for researchers. With the exception of 2 plant scientists and the DNA sequencing unit member, all interviewees were involved in biomedical research. Five interviewees (M1–M5) participated in microarray analysis workshops, 2 by structured hands-on activity and 3 by guided exploration. The other 7 participated in primer design workshops (P1–P7), 3 by structured hands-on activity and 4 by guided exploration. Participation in the study was voluntary, and no compensation was offered to participants.

All interviews were conducted by one researcher (Shachak) in interviewees' offices or laboratories. A predetermined set of questions (Appendix online) was employed to collect data regarding the effect of training, attitudes toward bioinformatics, perceptions, and intended and actual use of specific tools. Additional themes that emerged during the interviews were analyzed as well (e.g., knowledge barriers). Interviews were tape-recorded, transcribed verbatim, and analyzed using qualitative data analysis software (ATLAS.ti) [20].

Analysis

Two researchers experienced in qualitative data analysis (Shachak, Shuval) scrutinized the data independently. Initial agreement between researchers was high (80%), and open discussions were held until reaching consensus. The framework analysis approach [21] was used. First, researchers read interview transcripts several times to familiarize themselves with the data. Second, a thematic (coding) framework was identified based on both predefined issues (i.e., perceptions, attitudes, intended and actual use, training effect) and emergent themes from the familiarization stage. Next, codes were assigned to the data, and thematic charts [21] were created. The final stage of analysis was data mapping and interpretation [22] in relation to the above predefined categories and emerging themes. To establish validity, triangulation with quantitative data collected during the workshops was performed to assess agreement with this related data set [23].

FINDINGS

The following findings detail the major themes that emerged during data analysis. Selected quotations are depicted in Table 2.

Table 2 Sample quotations organized by the themes revealed during data analysis

Perceptions and adoption of bioinformatics tools

All twelve participants considered bioinformatics a valuable discipline for biomedical research, though they questioned the validity and standardization between different methods and databases (e.g., in gene annotations). Moreover, participants realized that “a good biological question must precede the use of bioinformatics” and that bioinformatics could not entirely replace experimental research.

Research needs emerged as the driving force behind the use of specific bioinformatics tools. These needs determined which applications to use and were the major reason for participating in training. Results showed that two perceptions of bioinformatics tools were most commonly associated with their intended or actual use: PEOU and perceived usefulness (PU). PEOU often affected the choice of particular software over other equivalent tools. This effect was especially true for participants from the primer design workshops. Participants from the microarray analysis workshop described its complexity as inhibiting their use of microarray data analysis software. All seven users of primer design software commented on the usefulness of primer design tools and reported that using them improved the quality of their work. In contrast, most of the five participants of the microarray analysis workshops did not refer to PU of microarray analysis tools.

Though all the interviewees reported using bioinformatics software, the applications and the extent to which they were used varied greatly between participants. Most interviewees utilized public databases such as PubMed and Online Mendelian Inheritance in Man (OMIM). Many also used sequence analysis tools. However, four interviewees commented on adoption difficulties or said they would have liked to utilize more bioinformatics resources than they actually did. In particular, analysis revealed differences in usage of primer design and microarray analysis tools. After the workshop, six of the seven interviewees from the primer design workshops actually used primer design software. In contrast, none of the interviewees from the microarray analysis workshops made use of the software, although they expressed the intention to use the tools. Except for one researcher who decided not to use microarrays at all, participants from the microarray analysis workshops employed research collaborations or services to analyze their data.

Training needs

Knowledge gaps and extensive learning time emerged as key factors that in combination inhibited the use of bioinformatics tools. In particular, interviewees felt that learning to analyze the results of microarray experiments would require a substantial learning effort and time investment. Therefore, alternative ways to analyze microarray experimental results were sought. Some interviewees reported looking for support and consulting in attempting to overcome knowledge barriers. These individuals looked to colleagues and local experts as well as institutional bioinformatics units. However, they felt that support, especially from local experts, was insufficient.

Interview data revealed three reasons for taking the workshops. Most participants attended the workshops because they fulfilled a specific job need. Participants of the primer design workshops described a positive effect on their decision to use software tools for this purpose. On the other hand, participants of the microarray analysis workshops described a negative or no effect on their usage decision.

DISCUSSION

The present study attempted to illuminate factors affecting the acceptance of bioinformatics software by biomedical researchers. Although data (theoretical) saturation [24] was reached as no new themes emerged when the final participants' responses were analyzed, the small number of participants and the fact that they volunteered for the study might affect external validity. None of the interviewees participated in both microarray analysis and primer design workshops, which might weaken the validity of comparison between the two workshops. Although participants referred to other bioinformatics applications as well, the primary focus of the interviews was on tools for primer design and microarray analysis. Therefore, results might not be generalizable to all types of bioinformatics tools yet despite these limitations, the study provided insight into their acceptance by researchers.

Participants expressed two major perceptions that affected their usage decisions (of the primer design and microarray analysis tools): PEOU and PU. This was consistent with previous quantitative findings, which suggested these two perceptions significantly explained respondents' intention of use. [25]. The quantitative data, however, suggested that PU explained a greater portion of the variance in intended use than PEOU, while in this study the majority of comments referred to PEOU. Gefen and Straub proposed that PEOU has a greater impact on acceptance “when the task itself is an integral part of an IT interface” [26]. The task, for which bioinformatics applications are frequently used (i.e., managing organizing, visualizing, and analyzing biological data), could be considered to be such an integral task. As many bioinformatics applications suffered from poor user interfaces [27], the current findings suggested that their implementation might benefit from research and development of the human–computer interaction aspects of such tools.

Key factors that emerged as highly important for bioinformatics software acceptance were high knowledge barriers and learning time required for use, especially for complex tools such as applications for microarray data analysis. Instead of analyzing the data themselves, participants in the microarray analysis workshops employed alternatives such as research collaborations and paid services. Attewell suggested that services could “enable user organizations to adopt a complex technology without (initially) having to acquire a full range of technical knowledge” [28]. This highlights an opportunity for medical librarians, who are often familiar with the research needs and qualifications of researchers, to take an active role in establishing research collaborations and providing services and resources to facilitate researchers' use of bioinformatics tools. Services may range from bioinformatics training and support to paid services for complex tasks, such as microarray data analysis. Institutions that have employed highly qualified bioinformatics specialists, such as the medical libraries at the University of Washington and Purdue University [9,29], provide valuable service models for these approaches.

The present study finds that training positively influenced usage decisions regarding simple tasks and applications (primer design) but had no effect or a negative influence on adoption of complex tasks and tools (microarray analysis). This finding contrasts somewhat with a study by Yarfitz and Ketchell, in which the establishment of a bioinformatics training program resulted in increased use of bioinformatics resources overall [9]. However, the present study examined the effect of individual, short-term workshops rather than a long-term program, a difference in scope that might account for the different findings.

Previous quantitative data suggest that training moderated the effect of task and system complexity on perceived ease of use and that it might have a negative effect on intended usage of bioinformatics tools [19]. The findings of the present study further support this idea. The authors propose that training, or at least short training as is the case here, should be regarded as a complex intervention that allows potential adopters to better assess the objective characteristics of bioinformatics applications. This proposition is consistent with studies of other information systems that suggested that hands-on experience assists users to better assess systems' usability, thereby allowing them to form more realistic perceptions and expectations [30,31].

CONCLUSION

Effective utilization of bioinformatics in biomedical research has significant implications for discovering the underlying mechanisms of numerous diseases as well as potential treatments. The present study illuminates some of the barriers and enabling factors to the implementation of bioinformatics in biomedical research. Researchers employ the bioinformatics training they receive in their work only when the tools are easy to use and require short learning time. Research collaborations and data analysis services enable researchers to use cutting edge technology (microarray), thus overcoming knowledge barriers and enabling researchers to analyze the data themselves. These findings suggest a number of potential roles for medical libraries in supporting bioinformatics implementation, including infrastructure support, consultation, and training. In addition, libraries could provide services and initiate research collaborations for complex tasks. Future research may use the findings of this study to further examine ways of integrating bioinformatics into biomedical research and developing training modules to improve bioinformatics acceptance.