Abstract
This paper presents findings from an ethnographic study of two genomics and bioinformatics labs. The focus of this research is on the day-to-day practices of using multiple technologies to integrate data across different platforms. We argue that sociotechnical challenges (including technical, contextual, and political challenges) emerge when data integration practices are carried out, due to the embedded nature of the important, yet unrecorded and implicit historical information that each dataset carries. We observed that sociotechnical sensemaking was common place in lab work, and was the only method for working out the complexity of the challenges which arose during data integration activities. We suggest that due attention be given to this matter, as challenges related to assessing data are likely to arise once more when such data travels back to the bedside, where it is poised to directly impact human health.
Recommended Citation
Ficociello, Maryam and Balka, Ellen, "Operationalizing Personalized Medicine: Data Translation Practices in Bioinformatics Laboratories" (2012). AMCIS 2012 Proceedings. 17.
https://aisel.aisnet.org/amcis2012/proceedings/PerspectivesIS/17
Operationalizing Personalized Medicine: Data Translation Practices in Bioinformatics Laboratories
This paper presents findings from an ethnographic study of two genomics and bioinformatics labs. The focus of this research is on the day-to-day practices of using multiple technologies to integrate data across different platforms. We argue that sociotechnical challenges (including technical, contextual, and political challenges) emerge when data integration practices are carried out, due to the embedded nature of the important, yet unrecorded and implicit historical information that each dataset carries. We observed that sociotechnical sensemaking was common place in lab work, and was the only method for working out the complexity of the challenges which arose during data integration activities. We suggest that due attention be given to this matter, as challenges related to assessing data are likely to arise once more when such data travels back to the bedside, where it is poised to directly impact human health.