Jasmijn Van Camp
Kelly Services Outsourcing and Consulting Group NV
2004-2009: Master in Biochemistry and Biotechnology: Molecular and cellular gene biotechnology
During the final master year, I got the opportunity to work in the institute of Biomedical Sciences (Valencia, Spain), writing my thesis and presenting and discussing data with other scientist in the field of neurodegenerative diseases.
2009-2014: PhD in Sciences: Biochemistry and Biotechnology
During my PhD, I designed and implemented protocols for the molecular genetic investigation of candidate genes for human obesity. I successfully completed my thesis and was able to publish 16 peer-reviewed research articles in several international journals, of which 8 as a first author. In addition, I got the opportunity to give an oral presentation at the Belgian Association for the study of Obesity (BASO) conference and present several posters at (inter)national obesity and endocrinology conferences, including the ENDO conference (Endocrine Society’s annual meeting).
Since 2015: I have been working as a (lead) global clinical data manager at a DM CRO (10 months) and with Kelly Services Outsourcing and Consulting Group NV. I have worked on a number of large multicentre Phase II & III oncology trials and on these projects, I am responsible for providing the final clinical data package to statistical and programming groups.
Title of Session: The Good, the Bad and the Ugly: defining KPIs to measure the quality of Clinical Data Management in ongoing clinical trials.
Description of Session:
One of the main purposes of CDM is to warrant the quality of clinical trial data in order to achieve reliable results that can be presented to regulatory authorities. Modern technology has made it easier to measure data quality, however, it also increased the complexity, volume and speed of the CDM process. Using this technology to our advantage is crucial.
One frequently applied method to measure the quality of CDM is the use of Key Result Indicators (KRIs). These are based on endpoints such as number of issues with or after the database lock and timely delivery of study data. Analysing these KRIs can be helpful to improve data quality for future milestones but is not relevant for endpoints that have already occurred.
Therefore, we propose the use of Key Performance (or Process) Indicators (KPIs) that are not directly related to protocol-defined milestones. These KPIs should be non-subjective, measurable at any point during a trial and not restricted to the clinical trial or therapeutic area.
The KPIs we propose are formulated as questions on DM processes, to be answered by the CDM at one or several protocol-independent timepoints during a clinical trial. Results of the individual KPIs identify indicators which require corrective measures in the CDM process to achieve the expected quality of study data.
Plotting consecutive (individual or total) KPI scores over time gives an indication of the improvement of individual’s or entire team’s CDM performance and can be linked with the KRIs.
The process suggested here will not only improve overall clinical data quality but will also align CDM activities within a company. On an industry level, we suggest that it should be good business practice to use standardized KPIs to measure the quality of CDM tasks and processes and this to guarantee the integrity of clinical data.
Key Performance indicators for CDM
Maintaining study data quality during clinical trials
Setting industry-level, standardized KPIs