SALSA (Statistical Assessment Layer for Site Audits) is a validated statistical tool which provides a robust basis for decisions related to a modern risk-based auditing approach. Originally developed to respond to the increasing demand for remote site auditing, it can also be used as an additional layer to decide whether an audit is required, or if the risk level is below the defined threshold, taking – beyond other factors – data quality and data integrity into consideration.
SALSA makes your audit planning “smarter” as it utilises an evidence-based rationale for data quality and data integrity risk identification that allows you to make the right decision.
SALSA also provides an evidence-base for data selection for review during the audit and for the questions asked to site staff and the CRA. If the opportunity for interaction with site staff is limited SALSA helps to come to the point immediately; ask the right questions, which carry the highest risks.
As SALSA analyses can be used by the client in addition to other parameters in their risk-based quality management approach to provide objective, data-based evidence to support decisions to postpone or cancel individual audits, or to convert to a Remote Investigator Site Audit (RISA) strategy.
In the selection of investigator site audits as well as for the preparation of individual site audits, contracted to GXP Engaged Auditing Services
When a full RISA is not possible, necessary or wanted, it allows a stepwise RISA approach, with or without site/CRO staff involvement or even just a remote data review
As your SALSA report can be independent of site audits and you can use GXP Engaged Auditing Services consulting services to obtain recommendations on possible and appropriate next steps or you can conduct further QA activities yourselves
As SALSA can also be used for ongoing site compliance and quality status monitoring, either as part of QA routine processes or to remotely review effectiveness of corrective and preventive actions implemented after an inspection or audit, which are expected to become visible in the study data.