Abstract:

This research paper analyzes the adoption of data mining tools as a part of data analytics process via diffusion of innovations theory in the Biomedical Context. Four research questions are explored in this study; What is the current landscape of data mining tools used in biomedical field, and how do they diffuse and gain adoption according to innovation diffusion theory?  How does Technical Compatibility impact IT Implementation in the biomedical field, if any? How does Technical Complexity (features, ease of use, etc.) impact data analytics utilization in the biomedical field, if any? And how does the Relative Advantage of the IT/Systems (for example Electronic Health Records (EHRs)) impact data analytics in the biomedical field, if any? Authors conclude that for data mining tools to be adopted via diffusion throughout the healthcare or biomedical field, they must have technical compatibility, low technical complexity, and provide a relative advantage over the traditional way. Barriers to understanding and developing technical compatibility diminish the value of the insights available from the data analytics using data mining tools. Excessive technical complexity, or the need for more technological tools to ingest data, prevent adoption for organizations beyond the innovation phase. A minimal relative advantage above the traditional way inhibits healthcare organizations' adoption of data mining tools as a key part of data analytics considering the time, cost, and effort it takes to transform to the new technology. If Information Technology and Information Systems focused on data analytics do not address these three areas, adoption does not diffuse throughout the healthcare or biomedical field.