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Figure 1 | BMC Medical Informatics and Decision Making

Figure 1

From: Ancillary study management systems: a review of needs

Figure 1

ASMS data flows. This figure shows a conceptual model for data flows for ancillary studies whose primary focus is analysis of stored specimens. In this scenario, data flows into the ASMS from the primary study’s CTMS (which contains information on participants, visits, consent and other pre-existing data) and specimen repository LIMSs (Laboratory Information Management Systems, which contain information on stored specimens available for further investigation). Before an ancillary study is initiated, the ASMS is used for hypothesis generation and feasibility investigations based on specimen availability. Once a particular ancillary study has been identified, a container for its data is established within the ASMS. After the ancillary study has been approved, any additional participant consents required for the study are collected by clinical sites and noted in the ASMS. Requests for needed specimens (including material transfer agreements) are sent to the appropriate specimen repositories, which in turn send stored specimens to appropriate labs. The labs perform assays on the specimens and import the results to the ancillary study container in the ASMS. Once the ancillary study is complete, results may be repatriated to the primary study. Results may also be shared in publications or other venues.

This model presumes that all data for the ancillary study is managed within the ASMS, not the CTMS or an external system. It also presumes that external investigators can be given access to the study within the ASMS. Under different assumptions, usage patterns and data flows would change, but an ASMS could still prove helpful.

For example, if gathering new clinical data from study participants is a significant piece of an ancillary study, using an organization’s existing CTMS for collecting and managing clinical data might make the most sense. An ASMS could still be desirable for other aspects of the study. For our collaborators, CTMSs have not proven amenable to the kinds of queries necessary for hypothesis generation and participant identification. Also, they are not ordinarily well-integrated with relevant LIMSs, so they do not facilitate identification of specimen availability. An ASMS could be used for these steps and others that are not typically supported by CTMSs or LIMSs, such as specimen requests and assay data management

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