As many of us can agree, Clinical Research Coordinators (CRC) strongly help shape the success
of a clinical trial. A coordinator’s role may consist of many different responsibilities such as regulatory and protocol management, training, subject eligibility and recruitment, study subject visits, data entry and more. As most CRCs are responsible for more than one study, the management of these tasks are also affected by the number of studies one has to juggle at a time, as well as their complexity. This workload can be taxing and may cause dissatisfaction of the CRC job, which in turn may reduce productivity in the workplace. All of these factors can affect the efficiency and progress of clinical trial practices.
A recent article published in the ACRP April 2020 Clinical Researcher highlighted how two studies tested productivity models to improve the efficiency of their clinical trial practice. Productivity models such as the Ontario Protocol Assessment Level (OPAL) scale and the Clinical Research Workload Tool (CRWT) were used as metrics to depict the growth of efficiency in clinical practice. The OPAL model is a pyramid scale to rate the complexity of each clinical trial protocol from 1 to 8. Nontreatment trials, being less complex, would be at the top of the pyramid with a rating of a 1 or 2. At the bottom of the pyramid scale would be the complex phase I interventional trials with a rating of an 8. Each level of the pyramid scale in between were classified by the number of procedures required by the protocol. When you take the OPAL score of a protocol and multiply it by the number of active subjects enrolled in the study, a case workload is able to be quantified. The OPAL score plus case workload determines the total workload for a specific protocol. By adding up the total scores of each protocol managed by a CRC, a manager is able to measure the CRC’s workload. Additionally, the CRWT, also takes into consideration other work factors and study roles. Some studies have more of visits, data collected that then needs to be entered into electronic data capture (EDC) systems, regulatory management, etc. The CRWT adjusts the score of the protocol workload depending on these factors.
Children’s Health System of Texas utilized the OPAL metrics with developmental department initiatives to determine the workload of their CRCs. By doing this, they were able to reorganize the responsibilities across study roles to optimize and specialize staff. Additionally, staff satisfaction surveys and retention increased by 46% correlating to the alleged fairness of study assignments and workload dispersal. At Stamford Health, they were able to utilize the OPAL in combination with the CRWT to identify the difference of ideal workloads for non-oncology coordinators vs oncology coordinators to achieve an increase in revenue generation. Interestingly enough, the study noted an increase in revenue when non-oncology coordinators had a higher workload, whereas a higher workload for oncology coordinators negatively impacted the revenue generation. The results from these studies are examples of how sites are able to adapt productivity models in order to obtain a positive outcome for their practice. While they may help a practice identify that their workload needs to be redistributed or that additional staff is needed, some companies may not be able to accept the value of these metrics and invest in the staff and resources needed.
The results of these models could also bring to light that it isn’t the workload of a CRC that needs to be adjusted, but perhaps that they just need additional support to ensure they are trained to increase productivity and efficiency. By using these models, what benefits would be identified for your practice? If all sites used these models, what kind of impact could this have on the clinical research industry as a whole?2016 STARS Symposium via photopin (license)