Robotic Process Automation (RPA) represents an opportunity to quickly automate repetitive tasks and free employees to work on more value-adding tasks. There are a number of repetitive, time-consuming tasks that if automated would improve Clinical Data Manager daily operations. RPA can be scheduled or triggered to execute and run in an unattended fashion.
Metrics are critical to efforts for reining in clinical trials that are either poorly initiated or have incurred unforeseen events which place the original timelines and/or budgets at risk of overages. They also drive competitive performance among those organizations performing trials.
The focus on technology as a driver of performance improvement in clinical trials in intense, but despite years of valiant efforts, study execution remains far from optimal. For study startup, the data are dismal.
The delivery of patient care is evolving to include options like flexible provider access, urgent care centers, IoT remote monitoring, etc. These options benefit the patient by providing flexible access to treatment and increased communication with providers but also complicate the process of maintaining a complete longitudinal patient medical record.
Blockchain addresses one of the most difficult issues in patient data - how to gain all the advantages that data sharing offers, while still maintaining patient trust and compliance within an increasing array of global privacy laws and regulations.
Where are the bottlenecks in starting clinical trials? It’s an obvious question to ask, but unfortunately, the tools traditionally used to conduct clinical trials lack robust reporting capabilities. Today, BI initiatives continue to top sponsor and CRO priorities, as executives demand greater visibility into trial data at a much faster pace.
The clinical trials sector is heavily invested in technologies that track how studies unfold. But, putting that information to good use requires turning real-time visibility into actionable data. Until recently, use of BI in clinical trials has been far from commonplace. But that is beginning to change, driven largely by a need to revamp how studies are conducted in today’s ultra-competitive global marketplace.