How Technology Can Help ClinOps Leaders Build a Quality Culture

Clinical trials are time sensitive, costly endeavors with high-risk and high-expectations at every juncture. For the vice president of clinical operations (ClinOps), expecting and preparing for the unexpected is crucial to ensuring the company hits all its trial milestones. In fact, under 10% of clinical trials are completed on time and 80% of trials regularly miss milestones. This can be improved with improved data quality, a study team committed to quality, and technology solutions that help the continuous data monitoring process.

FDA’s Guidance for Quality

The FDA’s Good Clinical Practice (GCP) has established objective standards to help guide clinical operations leaders with two quality imperatives: (1) protect the safety and well-being of trial subjects and, (2) ensure clinical trial data is credible and supports effective evaluation of research objectives.

To effectively assess and manage quality along these two imperatives, two core questions should be evaluated prior to and during the execution of every study:

• Is there any evidence of increased risk to – or actual poor management of – subject safety and well-being?

• Is there any evidence of increased risk – or actual damage – to the credibility and completeness of clinical trial data (i.e., “data quality”) that might prevent effective evaluation of the research objectives?

This mindset should be encouraged at every stage of the trial and part of every team member’s “mental checklist.” Having the ability to constantly monitor Study Quality Metrics (SQMs) can help keep these imperatives top of mind amidst competing priorities.

Study Quality Metrics to Manage Risk

SQMs aid clinical leaders by giving them the ability to effectively manage risk around data quality. With a constant view of study quality metrics, ClinOps leaders empower not just themselves but their entire team with the knowledge to move from reactive to proactive trial management. Additionally, intelligent forecasting and real-time alerts can play an important role in alleviating quality uncertainty throughout the trial, helping everyone make smarter decisions faster and improve the speed towards milestones.

The best way to see how a solution automating SQMs can improve clinical trials is by examining how it was implemented at a last-minute enrollment scramble. A VP of ClinOps at a 20-year-old biotech faced the challenge of preserving a major trial. The company had 15 studies in the clinical pipeline and an anti-inflammatory biologic in two late stage global trials. Stakes were high as the recent stock prices for the drug anticipated that the lead product would receive approval, the first product fully developed in-house and sold by the company.

As the last planned milestone was fast approaching, the VP of ClinOps received a warning on the study manager’s weekly report that enrollment dropped by 50% to five patients/month from all 50 sites. This was likely to delay the study by months. In the past when studies stalled, a study manager would spend days collecting information on root causes, confirming the information with the study team, discussing a plan and implementing in about one month. Three pivotal decisions helped save this trial:

Decision 1: Employ Aggressive Recruitment

The patients needed for the anti-inflammatory biologic trial were difficult to recruit. Only 1% to 2% of patients diagnosed with ulcerative colitis – a chronic, inflammatory bowel disease that causes inflammation in the digestive tract – had a severe form of the disease. Consequently, many colitis patients needed to be screened in order to find applicable patients. The team had to ensure failure rates remained sufficiently low to yield enough qualified patients.

Intelligent forecasting was invaluable in monitoring and acting on trends that could have jeopardized the trial. The intelligent forecasting insights revealed that study sites exhausted their reach of accessible patients. The VP of ClinOps had the study management team employ an aggressive recruitment strategy to expand the patient reach, investing in more print advertising and social media awareness, which helped secure additional patients.

Decision 2:More Coordinator Time to Avoid Delay in Data Entry

Prompt data-entry is especially important during the early enrollment stage. For example, without insights and visibility into these delays, patients were previously kept in the trial and later identified as patients that did not fit the trial criteria, wasting valuable time and effort. This retroactive delay can be entirely avoided with timely data-entry. Patients who do not meet the entry criteria should be excluded from the trial altogether and replacements should be identified early on.

In the coordination process, real-time alerts were set to trigger a 10-day delay threshold across sites, which revealed a coordinator was falling behind on data-entry. The coordinator explained that this was a workload issue and there were too many competing priorities, such as seeing patients from multiple studies. These alerts helped the ClinOp leader quickly identify data entry delays and assign another coordinator to the study before the data backlog exploded.

Decision 3:A Patient-Centric Approach to Increase Retention

Patient retention is important to clinical studies and there are early warning signs to look out for, such as delays in patient visit compliance. To overcome this challenge, real-time alerts were leveraged again to help identify risks related to patient visit compliance. Each site ultimately identified three issues that were associated with patient visit compliance: a patient’s worsening condition, limited available appointments or problems with transportation to study visits. These all increased risk for “lost-to-follow up” or withdrawal.

After a risk-mitigation conversation with the patient and care provider, they employed the following to increase patient visit compliance: calls to the care provider to remind them of upcoming visits, additional office hours for study visits, reimbursement for transportation and meal stipends when study visits were several hours long. With proactive collaboration and smart decisions, the trial team prevented several patients from dropping out and put the trial back on track.

Mitigating Last-Minute Risks through Data Quality

While the aforementioned trial successfully mitigated additional delays and related costs, fostering a culture around continuously monitoring data quality can minimize the impact of last minute challenges. Using a continuous quality solution and a study team committed to data quality, ClinOps leaders can focus on what matters. Through intelligent forecasting and real-time data alerts, the entire study team can make smarter decisions faster and improve the speed to milestones.

Jud Gardner, Founder and Chief Technical Officer at Comprehend Systems.